AI-based energy edge platform, system and method

Abstract

Provided herein is an AI-based energy edge platform with a wide range of features, components, and capabilities for managing and improving traditional infrastructure and coordinating with a distributed system to support important use cases for a series of enterprises. The platform may employ emerging technologies to improve the efficiency, flexibility, participation and profitability of an ecosystem and a single energy edge node. Embodiments may predict, plan, and manage energy demand and utilization in a larger distributed environment. Embodiments may use AI, IoT, and techniques to more efficiently filter, process, and move data across communication networks. Embodiments of the platform may utilize an energy market connection, communication, and transaction support platform. Embodiments may employ intelligent provisioning, data summarization, and analysis.

Description

Background Art

[0001] Energy remains a key factor in the world economy and is undergoing an evolution and transformation involving changes in energy production, storage, planning, demand management, consumption, and delivery systems and processes. These changes are enabled by the development and convergence of a large number of different technologies, including more distributed, modular, mobile, and / or portable energy generation and storage technologies that will support a more decentralized and localized energy market, and also by the development and convergence of a range of technologies that will facilitate the management of energy in more decentralized systems, including edge and Internet of Things (IoT) technologies, advanced computing and artificial intelligence technologies, transaction support technologies (e.g., blockchain, distributed ledgers, and smart contracts), etc. The convergence of these more decentralized energy technologies with these network, computing, and intelligence technologies is referred to herein as the "energy edge."

[0002] The energy market is expected to evolve and transform in the coming decades from a highly centralized model that relies on fossil fuels and managed power grids to a more distributed and decentralized model that involves more localized generation, storage, and consumption systems. During this transition, hybrid systems may persist for many years, where traditional power grids become smarter and distributed systems will play an increasingly large role. There is a need for a platform that helps manage and improve traditional infrastructure in coordination with distributed systems. Summary of the Invention

[0003] This document provides an AI-based energy edge platform that has a wide range of features, components, and capabilities for managing and improving traditional infrastructure and coordinating with distributed systems to support a range of important use cases for enterprises. The platform may employ emerging technologies to improve the efficiency, flexibility, engagement, and profitability of the ecosystem and individual energy edge nodes. Embodiments may be guided by, and in some cases integrated with, methods and systems for predicting, planning, and managing energy demand and utilization in a larger distributed environment. Embodiments may use AI and AI enablers (e.g., IoT) that can be deployed in a much denser data environment (reflecting the proliferation of smart energy systems and sensors in IoT) and technologies for more effectively filtering, processing, and moving data across communication networks. Embodiments of the platform may utilize energy market connectivity, communication, and transaction support platforms. Embodiments may employ smart supply, data aggregation, and analysis. In many use cases, the platform can improve energy generation, storage, delivery, and / or consumption in enterprise operations (e.g., buildings, data centers, and factories, etc.), integrate and utilize new power generation and energy storage technologies and assets (distributed energy resources or "DER"), optimize the energy utilization of existing networks, and digitize existing infrastructure and support systems.

[0004] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of power and energy, including: an adaptive energy data pipeline configured to transmit data across a set of nodes in a network, where each node in the set of nodes is adapted to operate on an energy data set associated with at least one of energy generation, energy storage, energy delivery, or energy consumption, and where at least one node in the set of nodes is configured by one or both of an algorithm or a rule set to filter, compress, transform, correct errors, and / or route at least a portion of the energy data set based on at least one of a set of network conditions, data size, data granularity, or data content.

[0005] In some aspects, the techniques described herein relate to an AI-based platform, where the adaptive energy data pipeline is further configured to adapt to data transmission through a network and / or communication system, where the adaptation is based on one or more of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; and user configuration conditions.

[0006] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin representing one or more of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0007] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin configured to perform one or more of the following: provide visual and / or analytical metrics of energy consumption of one or more energy consumers; filter energy data; highlight energy data; or adjust energy data.

[0008] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by one or more of the following: one or more machines; one or more factories; or one or more vehicles in a fleet.

[0009] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is also configured to perform one or more of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0010] In some aspects, the techniques described herein relate to an AI-based platform, wherein the energy data set is based on one or more public data resources, and the public data resources include one or more of the following: weather data resources; satellite data resources; census, population, demographic, and / or psychographic data resources; market data resources; or e-commerce data resources.

[0011] In some aspects, the techniques described herein relate to an AI-based platform, wherein the energy data set is based on one or more enterprise data resources, and the enterprise data resources include one or more of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0012] In some aspects, the techniques described herein relate to an AI-based platform, further comprising at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of the following: one or more human labels and / or annotations; one or more human interactions with a hardware and / or software system; one or more outcomes; one or more AI-generated training data samples; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0013] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one node in a set of nodes is configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy transmission instances; one or more fuel deliveries; or one or more stored energy deliveries.

[0014] In some aspects, the techniques described herein relate to an AI-based platform, where at least one node in a set of nodes is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: energy procurement and / or sales events; service fees associated with energy procurement and / or sales events; energy consumption events; energy generation events; energy distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0015] In some aspects, the techniques described herein relate to an AI-based platform, where at least one node in a set of nodes is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0016] In some aspects, the techniques described herein relate to an AI-based platform, where the adaptive energy data pipeline is further configured to monitor one or both of the following: the total energy consumption of at least a portion of a set of nodes; or the role of at least one node in a set of nodes in the total energy consumption of at least a portion of the set of nodes; and based on the monitoring, perform one or more of the following: manage the energy consumption of a set of nodes; predict the energy consumption of a set of nodes; or supply resources associated with the energy consumption of a set of nodes.

[0017] In some aspects, the techniques described herein relate to an AI-based platform, where a set of nodes in a network including an adaptive energy data pipeline includes a set of edge networking devices, and the edge networking devices manage at least one of energy consumption, energy storage, energy delivery, or energy consumption through a set of operating devices controlled via the edge networking devices.

[0018] In some aspects, the techniques described herein relate to an AI-based platform, where the adaptive energy data pipeline is further configured to automatically select the lowest-cost route for data transmitted across a set of nodes, and the selection is based on low-priority energy usage associated with the data.

[0019] In some aspects, the techniques described herein relate to an AI-based platform, where the adaptive energy data pipeline is further configured to automatically select a high-quality service route for data transmitted across a set of nodes, and the selection is based on high-priority energy usage associated with the data.

[0020] In some aspects, the techniques described herein relate to an AI-based platform, where the adaptive energy data pipeline includes a set of artificial intelligence capabilities configured to adjust the pipeline to enable components that optimize data transmission according to energy coordination requirements.

[0021] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline includes a self-organizing data store, and the data store is configured to store data on a device based on one or more of a data pattern, data content, or data context.

[0022] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is configured to perform automated adaptive networking, and the automated adaptive networking includes one or more of adaptive protocol selection, adaptive data routing based on RF conditions, adaptive data filtering, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity, or adaptive use of peer-to-peer network capacity.

[0023] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is configured to perform enterprise environment adaptation by automatically processing data based on one or more of an enterprise's operating environment, an enterprise's transaction environment, or an enterprise's financial environment.

[0024] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one node in a set of nodes is further configured to adjust communication with at least one other node in the set of nodes to adapt reporting of data associated with at least one of energy generation, energy storage, energy delivery, or energy consumption to the at least one other node.

[0025] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one node in a set of nodes is further configured to adapt reported data to at least one other node in the set of nodes, and adapting the reported data is based on the priority of consumption of the reported data.

[0026] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of nodes includes a heterogeneous group that includes at least one energy producer and at least one energy consumer, and the adaptive energy data pipeline is further configured to direct one or both of the at least one energy producer and the at least one energy consumer to communicate with at least one other node in the set of nodes via at least one communication route.

[0027] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to request reported data from at least one node in a set of nodes, the reported data is based on a granularity level, and the granularity level is based on the priority of a machine associated with the reported data.

[0028] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to prioritize the transmission of reported data through the adaptive energy data pipeline, and the prioritization is based on the monitoring responsibilities associated with the reported data.

[0029] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of electricity and energy, including: a set of adaptive autonomous data processing systems, wherein each adaptive autonomous data processing system is configured to collect data related to energy generation, storage, or delivery from a set of edge devices under the operation control of a set of distributed energy sources, and is configured to autonomously adjust a set of operating parameters for such operation control based on the collected data.

[0030] In some aspects, the techniques described herein relate to an AI-based platform, wherein each adaptive autonomous data processing system is further configured to adapt to data transmission through a network and / or communication system, and the adaptation is based on one or more of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0031] In some aspects, the techniques described herein relate to an AI-based platform, wherein each adaptive autonomous data processing system includes an adaptive energy digital twin that represents one or more of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0032] In some aspects, the techniques described herein relate to an AI-based platform, wherein each adaptive autonomous data processing system includes an adaptive energy digital twin that is configured to perform one or more of the following: provide visual and / or analytical metrics of the energy consumption of one or more energy consumers; filter energy data; highlight energy data; or adjust energy data.

[0033] In some aspects, the techniques described herein relate to an AI-based platform, wherein each adaptive autonomous data processing system includes an adaptive energy digital twin that is configured to generate visual and / or analytical metrics of energy consumption through one or more of the following: one or more machines; one or more factories; or one or more vehicles in a fleet.

[0034] In some aspects, the techniques described herein relate to an AI-based platform, wherein each adaptive autonomous data processing system is further configured to perform one or more of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0035] In some aspects, the techniques described herein relate to an AI-based platform, wherein the energy edge data is based on one or more common data resources, and the common data resources include one or more of the following: weather data resources; satellite data resources; census, population, demographic, and / or psychographic data resources; market data resources; or e-commerce data resources.

[0036] In some aspects, the techniques described herein relate to an AI-based platform, wherein the energy edge data is based on one or more enterprise data resources, and the enterprise data resources include one or more of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0037] In some aspects, the techniques described herein relate to an AI-based platform, further comprising at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of the following: one or more human labels and / or annotations; one or more human interactions with hardware and / or software systems; one or more outcomes; one or more AI-generated training data samples; supervised learning training processes; semi-supervised learning training processes; or deep learning training processes.

[0038] In some aspects, the techniques described herein relate to an AI-based platform, wherein each adaptive autonomous data processing system is further configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy transmission instances; one or more fuel deliveries; or one or more stored energy deliveries.

[0039] In some aspects, the techniques described herein relate to an AI-based platform, wherein each adaptive autonomous data processing system is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: energy procurement and / or sales events; service fees associated with energy procurement and / or sales events; energy consumption events; energy distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0040] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one adaptive autonomous data processing system is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy dispatch system.

[0041] In some aspects, the techniques described herein relate to an AI-based platform, wherein the platform further includes an adaptive energy data pipeline configured to transmit data across a set of nodes in a network.

[0042] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of nodes in the network including the adaptive energy data pipeline includes a set of edge networking devices that manage at least one of energy consumption, energy storage, energy delivery, or energy consumption through a set of operating devices controlled by the edge networking devices.

[0043] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to automatically select the lowest-cost route for data transmitted across a set of nodes, and the selection is based on low-priority energy usage associated with the data.

[0044] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to automatically select a high-quality service route for data transmitted across a set of nodes, and the selection is based on high-priority energy usage associated with the data.

[0045] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities configured to adjust the pipeline to enable components that optimize data transmission according to energy coordination requirements.

[0046] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline includes a self-organizing data store configured to store data on a device based on one or more of data patterns, data content, or data context.

[0047] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is configured to perform automated adaptive networking, which includes one or more of adaptive protocol selection, adaptive data routing based on RF conditions, adaptive data filtering, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity, or adaptive use of peer-to-peer network capacity.

[0048] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is configured to perform enterprise environment adaptation by automatically processing data based on one or more of an enterprise's operating environment, an enterprise's trading environment, or an enterprise's financial environment.

[0049] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one adaptive autonomous data processing system is further configured to determine a schedule for a set of processes based on at least one priority and / or demand associated with a set of distributed energy sources.

[0050] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one adaptive autonomous data processing system is further configured to adjust communication with at least one edge device in a set of edge devices based on at least one priority and / or demand associated with a set of distributed energy sources, and the communication is associated with an investigation of energy generation, storage, or delivery of the distributed energy sources.

[0051] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one adaptive autonomous data processing system is further configured to issue an instruction to at least one edge device in a set of edge devices, the instruction being based on an investigation of energy generation, storage, or delivery of the distributed energy sources, and the instruction causes the at least one edge device to adjust energy generation, storage, or delivery of the at least one edge device.

[0052] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of electricity and energy, including: a system configured to perform automatic and coordinated governance within a set of energy entities operationally coupled within an energy grid and a set of distributed edge energy resources, wherein at least one of the distributed edge energy resources is operationally independent of the energy grid.

[0053] In some aspects, the techniques described herein relate to an AI-based platform, wherein the system is further configured to accommodate data transmission over a network and / or communication system, where the accommodation is based on one or more of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0054] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin representing one or more of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0055] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin configured to perform one or more of the following: provide visual and / or analytical metrics of energy consumption of one or more energy consumers; filter energy data; highlight energy data; or adjust energy data.

[0056] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by one or more of the following: one or more machines; one or more factories; or one or more vehicles in a fleet.

[0057] In some aspects, the techniques described herein relate to an AI-based platform, wherein the system is further configured to perform one or more of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0058] In some aspects, the techniques described herein relate to an AI-based platform that also includes at least one AI-based model and / or algorithm, where the at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: one or more human labels and / or tags; one or more human interactions with a hardware and / or software system; one or more results; one or more AI-generated training data samples; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0059] In some aspects, the techniques described herein relate to an AI-based platform, where the system is also configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy transmission instances; one or more fuel deliveries; or one or more stored energy deliveries.

[0060] In some aspects, the techniques described herein relate to an AI-based platform, where the system is also configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: energy procurement and / or sales events; service fees associated with energy procurement and / or sales events; energy consumption events; energy generation events; energy distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0061] In some aspects, the techniques described herein relate to an AI-based platform, where at least one distributed energy edge resource is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0062] In some aspects, the techniques described herein relate to an AI-based platform, where the system is configured to facilitate the governance of a mining environment.

[0063] In some aspects, the techniques described herein relate to an AI-based platform, where the system includes mine-level Internet of Things (IoT) sensing of a mining environment, ground-penetrating sensing of unmined portions of a mining environment, mass spectrometry- and computer vision-based sensing of mined materials, asset tagging of smart containers, wearable devices for detecting the physiological state of miners, secure recording and resolution of transactions and transaction-related events, smart contracts for automatically allocating proceeds obtained from a mining environment, and an automated system for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements.

[0064] In some aspects, the techniques described herein relate to an AI-based platform, wherein the system includes a set of carbon-aware energy edge solutions that include exploring, configuring, and implementing a set of strategies regarding carbon generation.

[0065] In some aspects, the techniques described herein relate to an AI-based platform, wherein the solution requires monitoring the production of energy in the mining environment to track the carbon emissions generated in the mining environment.

[0066] In some aspects, the techniques described herein relate to an AI-based platform, wherein the solution requires the mining environment to produce energy to offset the carbon generated in the mining environment.

[0067] In some aspects, the techniques described herein relate to an AI-based platform, wherein the platform includes a user interface, and the system includes a set of automated energy policy deployment solutions that can be configured via the user's interaction with the user interface.

[0068] In some aspects, the techniques described herein relate to an AI-based platform, wherein the system includes an intelligent agent that is trained to generate strategies related to the governance of the mining environment, and the intelligent agent is trained on a training set of historical data, feedback from results, and human policy settings interactions.

[0069] In some aspects, the techniques described herein relate to an AI-based platform, wherein the system promotes the governance of the mining environment by implementing one or more of the following strategies: setting a maximum energy usage for an entity for a period of time; setting a maximum energy cost for an entity for a period of time; setting a maximum carbon production for an entity within a period of time; setting a maximum pollution emission for an entity within a period of time; setting carbon offset requirements; setting renewable energy quota requirements; setting energy mix requirements; setting a minimum profit margin based on the energy and other marginal costs of the production entity; or setting a minimum storage baseline for an energy storage entity.

[0070] In some aspects, the techniques described herein relate to an AI-based platform, wherein the system includes a set of energy management smart contract solutions that are configured to allow users of the platform to design, generate, and deploy smart contracts that automatically provide a level of management for a set of energy transactions.

[0071] In some aspects, the techniques described herein relate to an AI-based platform, wherein the system includes a set of automated energy financial control solutions that are configured to allow users of the platform to design, generate, configure, or deploy strategies related to controlling financial factors associated with one or more of energy generation, storage, delivery, or utilization.

[0072] In some aspects, the techniques described herein relate to an AI-based platform, wherein the system is further configured to determine a priority associated with at least one of a set of energy entities or a set of distributed edge energy resources, and the priority is based on a policy associated with at least one of a set of energy entities or a set of distributed energy resources.

[0073] In some aspects, the techniques described herein relate to an AI-based platform, wherein the system is further configured to perform monitoring of energy productivity through a set of energy entities and adjust the automatic and coordinated governance of a set of energy entities based on the monitoring of productivity.

[0074] In some aspects, the techniques described herein relate to an AI-based platform, wherein the system is further configured to allocate the processing of a set of distributed edge energy based on at least one energy measurement and / or prediction associated with a set of energy entities.

[0075] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of power and energy, including: an adaptive energy data pipeline configured to transmit data across a set of nodes in a network, wherein at least one subset of the set of nodes is configured to set at least one parameter of data communication associated with the adaptive energy data pipeline by at least one of a rule or an algorithm, and the at least one parameter is based on a set of metrics of the current network condition so as to optimize the energy used in data communication.

[0076] In some aspects, the techniques described herein relate to an AI-based platform, wherein the at least one parameter is one or more of the following: a routing instruction; a routing parameter; an error correction parameter; a compression parameter; a storage parameter; or a timing parameter.

[0077] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to adapt to data transmission through a network and / or a communication system, and the adaptation is based on one or more of the following: a congestion condition; a delay and / or latency condition; a packet loss condition; an error rate condition; a transport cost condition; a quality of service (QoS) condition; a usage condition; a market factor condition; or a user configuration condition.

[0078] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin representing one or more of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; a market condition; or an energy usage priority condition.

[0079] In some aspects, the techniques described herein relate to an AI-based platform that further includes an adaptive energy digital twin configured to perform one or more of the following: provide visual and / or analytical metrics of energy consumption of one or more energy consumers; filter energy data; highlight energy data; or adjust energy data.

[0080] In some aspects, the techniques described herein relate to an AI-based platform that further includes an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by one or more of the following: one or more machines; one or more factories; or one or more vehicles in a fleet.

[0081] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to perform one or more of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0082] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data is based on one or more public data resources, and the public data resources include one or more of the following: weather data resources; satellite data resources; census, population, demographic, and / or psychographic data resources; market data resources; or e-commerce data resources.

[0083] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data is based on one or more enterprise data resources, and the enterprise data resources include one or more of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0084] In some aspects, the techniques described herein relate to an AI-based platform that further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: one or more human labels and / or annotations; one or more human interactions with hardware and / or software systems; one or more outcomes; one or more AI-generated training data samples; supervised learning training processes; semi-supervised learning training processes; or deep learning training processes.

[0085] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy transmission instances; one or more fuel deliveries; or one or more stored energy deliveries.

[0086] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: energy procurement and / or sales events; service fees associated with energy procurement and / or sales events; energy consumption events; energy generation events; energy distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0087] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least a portion of the adaptive energy data pipeline is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0088] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to monitor one or both of the following: the total energy consumption of at least a portion of a set of nodes; or the role of at least one node of a set of nodes in the total energy consumption of at least a portion of a set of nodes; and based on the monitoring, perform one or more of the following: manage the energy consumption of a set of nodes; predict the energy consumption of a set of nodes; or provide resources associated with the energy consumption of a set of nodes.

[0089] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of nodes in the network that includes the adaptive energy data pipeline includes a set of edge networking devices, and the edge networking devices manage at least one of energy consumption, energy storage, energy delivery, or energy consumption through a set of operating devices controlled via the edge networking devices.

[0090] In some aspects, the techniques described herein relate to an AI-based platform, wherein the adaptive energy data pipeline is further configured to automatically select the lowest-cost route for data transmitted across a set of nodes, the selection being based on low-priority energy usage associated with the data.

[0091] In some aspects, the techniques described herein relate to an AI-based platform, where an adaptive energy data pipeline is also configured to automatically select a high-quality service route for data transmitted across a set of nodes, the selection being based on high-priority energy usage associated with the data.

[0092] In some aspects, the techniques described herein relate to an AI-based platform, where an adaptive energy data pipeline includes a set of artificial intelligence capabilities configured to adjust the pipeline to enable components that optimize data transmission according to energy coordination requirements.

[0093] In some aspects, the techniques described herein relate to an AI-based platform, where an adaptive energy data pipeline includes a self-organizing data memory configured to store data on a device based on one or more of a data pattern, data content, or data context.

[0094] In some aspects, the techniques described herein relate to an AI-based platform, where an adaptive energy data pipeline is configured to perform automated adaptive networking, which includes one or more of adaptive protocol selection, adaptive data routing based on RF conditions, adaptive data filtering, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity, or adaptive use of peer-to-peer network capacity.

[0095] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of power and energy, including: a digital twin system having a digital twin of a mining environment, where the digital twin includes at least one parameter detected by sensors in the mining environment.

[0096] In some aspects, the techniques described herein relate to an AI-based platform, where at least one parameter is associated with one or more of the following: an unmined portion of the mining environment; mining materials from the mining environment; intelligent container events involving intelligent containers associated with the mining environment; the physiological state of miners associated with the mining environment; transaction-related events associated with the mining environment; or the mining environment's compliance with one or more contractual, regulatory, and / or legal policies.

[0097] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin system also represents one or more of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or an energy usage priority status.

[0098] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin system is further configured to perform one or more of the following: provide visual and / or analytical metrics of the energy consumption of one or more energy consumers; filter energy data; highlight energy data; or adjust energy data.

[0099] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin system is further configured to generate visual and / or analytical metrics of energy consumption by one or more of the following: one or more machines; one or more factories; or one or more vehicles in a fleet.

[0100] In some aspects, the techniques described herein relate to an AI-based platform, wherein the parameters are based on one or more common data resources, and the common data resources include one or more of the following: weather data resources; satellite data resources; census, population, demographic, and / or psychographic data resources; market data resources; or e-commerce data resources.

[0101] In some aspects, the techniques described herein relate to an AI-based platform, wherein the parameters are based on one or more enterprise data resources, and the enterprise data resources include one or more of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0102] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin system includes at least one AI-based model and / or algorithm, and the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of the following: one or more human labels and / or annotations; one or more human interactions with a hardware and / or software system; one or more outcomes; one or more AI-generated training data samples; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0103] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin system is further configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy transmission instances; one or more fuel deliveries; or one or more stored energy deliveries.

[0104] In some aspects, the technologies described herein relate to an AI-based platform, wherein the digital twin system is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: energy procurement and / or sales events; service fees associated with energy procurement and / or sales events; energy consumption events; energy generation events; energy distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0105] In some aspects, the technologies described herein relate to an AI-based platform, wherein the digital twin system is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0106] In some aspects, the technologies described herein relate to an AI-based platform, wherein the mining environment is a data mining environment.

[0107] In some aspects, the technologies described herein relate to an AI-based platform, wherein the mining environment is a set of resources for performing computational operations.

[0108] In some aspects, the technologies described herein relate to an AI-based platform, wherein the platform includes mine-level Internet of Things (IoT) sensing of the mining environment, ground-penetrating sensing of unmined portions of the mining environment, mass spectrometry- and computer vision-based sensing of mining materials, asset tagging of smart containers, wearable devices for detecting the physiological state of miners, secure recording and resolution of transactions and transaction-related events, smart contracts for automatically allocating proceeds obtained from the mining environment, and an automated system for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements.

[0109] In some aspects, the technologies described herein relate to an AI-based platform, wherein the platform includes a set of carbon-aware energy edge solutions, and the solutions include exploring, configuring, and implementing a set of strategies regarding carbon generation.

[0110] In some aspects, the technologies described herein relate to an AI-based platform, wherein the solutions require monitoring the mining environment for energy production to track carbon emissions generated by the mining environment.

[0111] In some aspects, the technologies described herein relate to an AI-based platform, wherein the solutions require the mining environment to produce energy to offset the carbon generated by the mining environment.

[0112] In some aspects, the techniques described herein relate to an AI-based platform, where the platform includes a user interface, and the platform includes a set of automated energy policy deployment solutions that can be configured via user interaction with the user interface.

[0113] In some aspects, the techniques described herein relate to an AI-based platform, where the platform includes an intelligent agent that is trained to generate policies related to the governance of a mining environment, and the intelligent agent is trained on a training set of historical data, feedback from results, and human policy settings interactions.

[0114] In some aspects, the techniques described herein relate to an AI-based platform, where the platform promotes the governance of a mining environment by implementing one or more of the following policies: setting a maximum energy usage for an entity over a period of time; setting a maximum energy cost for an entity over a period of time; setting a maximum carbon production for an entity over a period of time; setting a maximum pollution emission for an entity over a period of time; setting carbon offset requirements; setting renewable energy quota requirements; setting energy mix requirements; setting a minimum profit margin based on the energy and other marginal costs of a production entity; or setting a minimum storage baseline for an energy storage entity.

[0115] In some aspects, the techniques described herein relate to an AI-based platform, where at least one parameter includes measurements made by sensors, and the measurements are associated with at least one piece of equipment included in the industrial operations of a mining environment.

[0116] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin system includes a scheduler configured to determine a schedule for generating, storing, and / or delivering energy to at least one piece of equipment associated with the industrial operations of a mining environment, and the schedule is based on at least one parameter detected by sensors.

[0117] In some aspects, the techniques described herein relate to an AI-based platform, where at least one parameter included in the digital twin includes at least one attribute of at least one dataset associated with a mining environment.

[0118] In some aspects, the techniques described herein relate to an AI-based platform for achieving intelligent coordination and management of electricity and energy, including: a governance system for mining operations; and a reporting system for transmitting at least one parameter sensed by sensors of a mine of the mining operations, where the at least one parameter is associated with the mining operations' compliance with a set of labor standards.

[0119] In some aspects, the techniques described herein relate to an AI-based platform, wherein the reporting system is further configured to accommodate data transmission over a network and / or communication system, wherein the accommodation is based on one or more of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0120] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin representing one or more of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0121] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical metrics of energy consumption of one or more energy consumers; filtering energy data; highlighting energy data; adjusting energy data, or generating visual and / or analytical metrics of energy consumption by one or more of the following: one or more machines; one or more factories; or one or more vehicles in a fleet.

[0122] In some aspects, the techniques described herein relate to an AI-based platform, wherein the reporting system is further configured to perform one or more of the following: extracting energy-related data; detecting and / or correcting errors in energy-related data; transforming, converting, normalizing, and / or cleaning energy-related data; parsing energy-related data; detecting patterns, content, and / or objects in energy-related data; compressing energy-related data; streaming energy-related data; filtering energy-related data; loading and / or storing energy-related data; routing and / or transmitting energy-related data; or maintaining the security of energy-related data.

[0123] In some aspects, the techniques described herein relate to an AI-based platform, wherein the reporting system is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, the one or more energy-related events including one or more of the following: energy procurement and / or sales events; service fees associated with energy procurement and / or sales events; energy consumption events; energy generation events; energy distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0124] In some aspects, the techniques described herein relate to an AI-based platform, where at least one of at least one parameter is based on one or more of the following: one or more public data resources, and one or more public data resources include one or more of the following: weather data resources; satellite data resources; census, population, demographic, and / or psychographic data resources; market data resources; or e-commerce data resources, or one or more enterprise data resources, and one or more enterprise data resources include one or more of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operational data.

[0125] In some aspects, the techniques described herein relate to an AI-based platform, further including at least one AI-based model and / or algorithm, where at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: one or more human labels and / or annotations; one or more human interactions with a hardware and / or software system; one or more outcomes; one or more AI-generated training data samples; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0126] In some aspects, the techniques described herein relate to an AI-based platform, where the governance system is further configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy transmission instances; one or more fuel deliveries; or one or more stored energy deliveries.

[0127] In some aspects, the techniques described herein relate to an AI-based platform, where a set of labor standards is associated with at least one activity performed by a mine worker, and transmitting at least one parameter sensed by a sensor includes transmitting an indication of the worker's performance of the at least one activity sensed by the sensor.

[0128] In some aspects, the techniques described herein relate to an AI-based platform, where a set of labor standards is associated with at least one object, at least one object is associated with a mine worker, and transmitting at least one parameter sensed by a sensor includes transmitting an indication of the detection of the at least one object by the sensor.

[0129] In some aspects, the techniques described herein relate to an AI-based platform, where a set of labor standards includes thresholds for attributes of a mine, and the reporting system is further configured to transmit a determination based on a comparison of at least one parameter sensed by a sensor with the thresholds.

[0130] In some aspects, the techniques described herein relate to an AI-based platform further including a compliance recovery system configured to perform at least one compliance recovery action based on a determination that at least one parameter sensed by a sensor indicates a condition of non-compliance with a set of labor standards.

[0131] In some aspects, the techniques described herein relate to an AI-based platform further including an emergency response system configured to perform at least one emergency response action based on a determination that at least one parameter sensed by a sensor indicates the occurrence of an emergency event associated with a mine.

[0132] In some aspects, the techniques described herein relate to an AI-based platform further including a sensor configuration system configured to determine a configuration of a sensor to perform sensing of at least one parameter, wherein the configuration is based on a mining operation being compliant with a set of labor standards.

[0133] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of labor standards is accessible to and specified in natural language by a sensor configuration system, and the sensor configuration system is configured to determine the configuration of the sensor based on natural language parsing of the set of labor standards.

[0134] In some aspects, the techniques described herein relate to an AI-based platform further including a sensor repair system configured to perform at least one sensor repair measure based on a determination that a sensor has not sensed at least one parameter, wherein the at least one sensor repair measure includes one or more of the following: initiate replacement of the sensor; initiate a diagnostic operation involving the sensor; initiate reconfiguration of the sensor to detect the at least one parameter in a different manner; initiate a request to a mine worker to perform manual sensing of the at least one parameter; or initiate replacement of the sensor of the mine with at least one other sensor of the mine to sense the at least one parameter.

[0135] In some aspects, the techniques described herein relate to an AI-based platform further including a compliance verification system configured to verify that at least one parameter sensed by a sensor indicates that a mining operation is compliant with a set of labor standards, wherein the verification includes one or more of the following: verify calibration of a sensor of the mine; verify the at least one parameter sensed by a sensor of the mine based on comparison of the at least one parameter with at least one parameter sensed by at least one other sensor of the mine; request manual verification of the at least one parameter by a mine worker; or request verification by a compliance officer that the at least one parameter indicates that a mining operation is compliant with a set of labor standards.

[0136] In some aspects, the techniques described herein relate to an AI-based platform that further includes a worker communication interface configured to participate in communication with mine workers based on at least one parameter sensed by sensors, where the communication is associated with a mining operation's compliance with a set of labor standards.

[0137] In some aspects, the techniques described herein relate to an AI-based platform that further includes a user interface configured to display a map of a mining operation, where the map includes an indication that the mining operation complies with a set of labor standards based on at least one parameter sensed by sensors.

[0138] In some aspects, the techniques described herein relate to an AI-based platform, where a set of labor standards includes a set of work requirements for a worker to perform tasks associated with a mining operation, and the reporting system is further configured to adapt a worker's assignment to a task based on the set of work requirements.

[0139] In some aspects, the techniques described herein relate to an AI-based platform, where at least one parameter includes a schedule for a worker to perform tasks associated with a mining operation, and the reporting system is further configured to adapt the schedule based on the mining operation's compliance with a set of labor standards.

[0140] In some aspects, the techniques described herein relate to an AI-based platform, where the reporting system is further configured to initiate at least one protocol in response to at least one parameter sensed by sensors, and the at least one protocol is based on adjusting at least one parameter sensed by sensors to maintain or restore the mining operation's compliance with a set of labor standards.

[0141] In some aspects, the techniques described herein relate to an AI-based platform, where the reporting system is further configured to maintain a digital record of the training status and / or certification status of at least one worker associated with at least one task of a mining operation.

[0142] In some aspects, the techniques described herein relate to an AI-based platform for implementing intelligent coordination and management of power and energy, including: a set of edge devices, where each edge device in the set of edge devices is configured to maintain awareness of the carbon generation and / or emissions of at least one entity linked to the set of edge devices and / or managed by the set of edge devices in a set of energy-using entities.

[0143] In some aspects, the techniques described herein relate to an AI-based platform, where at least one edge device in the set is configured to simulate the carbon generation and / or emissions of at least one entity in a set of energy-using entities.

[0144] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is configured to execute a set of machine learning algorithms trained on a training dataset of carbon generation data to compute carbon generation and / or emission metrics for a set of operational entities.

[0145] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is configured to execute a set of machine learning algorithms trained on a training dataset of carbon generation data to compute carbon generation and / or emission metrics for a set of operational entities.

[0146] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is further configured to adapt data transmission over a network and / or communication system, wherein the adaptation is based on one or more of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0147] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin representing one or more of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0148] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin configured to perform one or more of the following: provide visual and / or analytical metrics of energy consumption for one or more energy consumers; filter energy data; highlight energy data; adjust energy data, or generate visual and / or analytical metrics of energy consumption through one or more of the following: one or more machines; one or more factories; or one or more vehicles in a fleet.

[0149] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is further configured to perform one or more of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0150] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group includes at least one AI-based model and / or algorithm, and wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: one or more human labels and / or markings; one or more human interactions with a hardware and / or software system; one or more results; one or more AI-generated training data samples; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0151] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy transmission instances; one or more fuel deliveries; or one or more stored energy deliveries.

[0152] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: energy purchase and / or sale events; service fees associated with energy purchase and / or sale events; energy consumption events; energy generation events; energy distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0153] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0154] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is further configured to determine a change in carbon generation and / or emission over a period of time based on a comparison of a current metric of carbon generation and / or emission with a historical metric of carbon generation and / or emission.

[0155] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is further configured to determine carbon generation and / or emission targets based on a carbon generation and / or emission strategy.

[0156] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is further configured to: perform a comparison of carbon generation and / or emissions metrics with carbon generation and / or emissions targets; and determine compliance of carbon generation and / or emissions with carbon generation and / or emissions strategies based on the comparison.

[0157] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is further configured to determine the environmental impact of carbon generation and / or emissions based on carbon generation and / or emissions metrics and carbon generation and / or emissions targets.

[0158] In some aspects, the techniques described herein relate to an AI-based platform, wherein carbon generation and / or emissions are associated with a set of activities, and at least one edge device in the group is further configured to allocate at least a portion of the carbon generation and / or emissions to at least one activity in the set of activities.

[0159] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is further configured to associate at least one metric with carbon generation and / or emissions metrics and carbon generation and / or emissions targets, where the metrics include one or more of the following: the date, time, and / or time period of carbon generation and / or emissions; the source location of carbon generation and / or emissions; the transport direction and / or speed of carbon generation and / or emissions; the affected location of carbon generation and / or emissions; the physical metrics of carbon generation and / or emissions; the chemical composition of carbon generation and / or emissions; weather patterns present in the area associated with carbon generation and / or emissions; wildlife populations in the area associated with carbon generation and / or emissions; or human activities affected by carbon generation and / or emissions.

[0160] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is further configured to transmit an alert associated with carbon generation and / or emissions based on a comparison of carbon generation and / or emissions metrics with an alert threshold associated with carbon generation and / or emissions.

[0161] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in the group is further configured to adjust activities associated with carbon generation and / or emissions based on carbon generation and / or emissions metrics, and the adjustment modifies the future state of carbon generation and / or emissions.

[0162] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in a group of edge devices is further configured to maintain awareness by detecting measurements of carbon generation and / or emissions associated with at least one entity in a group of energy-using entities based on a detection interval.

[0163] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device among a set of edge devices is also configured to maintain awareness by generating at least one local report and / or alert, and at least one local report and / or alert is associated with a carbon generation and / or emission pattern, and the carbon generation and / or emission pattern is associated with at least one entity among a set of energy-using entities.

[0164] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device among a set of edge devices is also configured to change the operation of one or more devices and / or processes associated with at least one entity among a set of energy-using entities, and the change in operation is based on at least one measurement of carbon generation and / or emission associated with at least one entity among a set of energy-using entities.

[0165] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of electricity and energy, including: a digital twin updated by a data collection system that dynamically maintains a set of historical, current, and / or predicted energy demand parameters for a set of fixed entities and a set of mobile entities within a domain, wherein the update of the digital twin is based on a set of energy demand parameters.

[0166] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of operating entities is controlled via a set of edge networking devices linked to the set of operating entities, and the energy demand parameters are based on one or more of the following: a set of current aggregated data derived from demands from a set of operating entities, wherein the set of operating entities is controlled via a set of edge networking devices linked to the set of operating entities; a set of historical aggregated data derived from demands from a set of operating entities, wherein the set of operating entities is controlled via a set of edge networking devices linked to the set of operating entities; or a set of simulated aggregated data derived from demands from a set of operating entities.

[0167] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data collection system is also configured to adapt data transmission through a network and / or communication system, and the adaptation is based on one or more of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0168] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin represents one or more of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a cyber infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; a market condition; or an energy usage priority condition.

[0169] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin is further configured to perform one or more of the following: provide visual and / or analytical metrics of the energy consumption of one or more energy consumers; filter energy data; highlight energy data; adjust energy data, or generate visual and / or analytical metrics of energy consumption by one or more of the following: one or more machines; one or more factories; or one or more vehicles in a fleet.

[0170] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one energy demand parameter is based on one or more of the following: on one or more public data resources, and the one or more public data resources include one or more of the following: a weather data resource; a satellite data resource; a census, population, demographics, and / or psychographics data resource; a market data resource; or an e-commerce data resource; or one or more enterprise data resources, and the one or more enterprise data resources include one or more of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0171] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin includes at least one AI-based model and / or algorithm, and the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of the following: one or more human labels and / or markings; one or more human interactions with a hardware and / or software system; one or more results; one or more AI-generated training data samples; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0172] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin is further configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy transmission instances; one or more fuel deliveries; or one or more stored energy deliveries.

[0173] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin is also configured to adjust the delivery of energy to one or more consumption points based on energy delivery and / or consumption strategies.

[0174] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin is also configured to determine the carbon generation and / or emission effects of delivering energy to one or more consumption points.

[0175] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin is also configured to adjust the delivery of energy to one or more consumption points based on the probability of insufficient available energy at one or more consumption points and the consequences of insufficient available energy at one or more consumption points.

[0176] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin is also configured to determine the delivery of energy to one or more consumption points based on a comparison of the energy availability of each of two or more energy sources, where the comparison includes one or more of the following: the current and / or future quantity of energy stored by at least one of two or more energy sources, the current and / or future resource consumption associated with obtaining, storing, and / or delivering energy by at least one of two or more energy sources, or the current and / or future demand for at least one of two or more energy sources by other energy consumers.

[0177] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin is also configured to record one or more energy-related events in a distributed ledger and / or blockchain, where the one or more energy-related events include one or more of the following: energy procurement and / or sales events; service fees associated with energy procurement and / or sales events; energy consumption events; energy generation events; energy distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0178] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0179] In some aspects, the techniques described herein relate to an AI-based platform, where the AI-based platform is configured to measure the performance of the digital twin based on a prediction increment, and the prediction increment is based on a comparison of a prediction generated by the digital twin based on a set of energy demand parameters with a measurement corresponding to the prediction within a data collection system.

[0180] In some aspects, the techniques described herein relate to an AI-based platform, where the AI-based platform is configured to update a digital twin based on a predictive increment, and the update includes one or more of the following: retraining the digital twin based on the predictive increment, adjusting a prediction correction applied to the prediction of the digital twin based on the predictive increment, supplementing the digital twin with at least one other trained machine learning model, or replacing the digital twin with an alternative digital twin.

[0181] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin is further configured to generate: a prediction based on at least one energy demand parameter, and an indication of the impact of at least one energy demand parameter on the prediction.

[0182] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin is further configured to determine one or more modifications to a set of energy demand parameters to improve future predictions of the digital twin, and the one or more modifications include one or more of the following: one or more additional historical, current, and / or predicted energy demand parameters associated with a set of stationary entities and a set of mobile entities within a domain; or one or more modifications to one or more of the historical, current, and / or predicted energy demand parameters associated with a set of stationary entities and a set of mobile entities within a domain.

[0183] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin is further configured to coordinate the delivery of energy to one or more consumption points based on one or more entity parameters received from at least one entity in a set of stationary entities and / or a set of mobile entities within a domain, and the one or more entity parameters include one or more of the following: the current and / or future energy state of at least one entity, the current and / or future energy consumption of at least one entity, or the current and / or future activities associated with energy consumption performed by at least one entity.

[0184] In some aspects, the techniques described herein relate to an AI-based platform, where the digital twin is further configured to transmit a request to adjust one or more entity parameters associated with at least one entity to at least one entity in a set of stationary entities and / or a set of mobile entities within a domain, and the one or more entity parameters include one or more of the following: the current and / or future energy state of at least one entity, the current and / or future energy consumption of at least one entity, or the current and / or future activities associated with energy consumption performed by at least one entity.

[0185] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin is further configured to: perform a simulation of at least one process of at least one physical machine associated with one or both of a set of stationary entities or a set of mobile entities, and based on the simulation, output at least one energy demand parameter generated by the at least one process.

[0186] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin is associated with at least one physical machine, the at least one physical machine is associated with one or both of a set of stationary entities or a set of mobile entities, and the digital twin is updated by a data collection system to generate an output of a process corresponding to an updated detection of an output of a process performed by the at least one physical machine.

[0187] In some aspects, the techniques described herein relate to an AI-based platform, wherein the digital twin is updated by a data collection system based on a strategy for saving electricity and energy consumption associated with a set of energy demand parameters.

[0188] In some aspects, the techniques described herein relate to an AI-based platform for implementing intelligent coordination and management of electricity and energy, including: a set of modular distributed energy systems that can be configured based on local demand requirements.

[0189] In some aspects, the techniques described herein relate to an AI-based platform, wherein local demand requirements are predicted by a demand prediction algorithm operating on a set of edge networking devices linked to a set of energy-consuming systems.

[0190] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one of the set of modular distributed energy systems is configured by the AI-based platform to be located near the location and time of demand.

[0191] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one of the set of modular distributed energy systems is configured by the AI-based platform to be located based on the location and type of local demand requirements.

[0192] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one of the set of modular distributed energy systems is configured by the AI-based platform to generate energy at a local demand point.

[0193] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one of the set of modular distributed energy systems is configured by the AI-based platform to deliver a modular power generation system to a demand location.

[0194] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one modular distributed energy system in the group is configured by the AI-based platform to route the energy delivery of a group of energy delivery facilities to a demand location.

[0195] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one modular distributed energy system in the group is coordinated by the AI-based platform to store energy near the location and time of demand.

[0196] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one modular distributed energy system in the group is further configured to accommodate data transmission through a network and / or communication system, wherein the accommodation is based on one or more of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0197] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin that represents one or more of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0198] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin that is configured to perform one or more of the following: provide visual and / or analytical metrics of the energy consumption of one or more energy consumers; filter energy data; highlight energy data; adjust energy data, or generate visual and / or analytical metrics of energy consumption through one or more of the following: one or more machines; one or more factories; or one or more vehicles in a fleet.

[0199] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one modular distributed energy system is further configured to perform one or more of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0200] In some aspects, the techniques described herein relate to an AI-based platform, where local requirements are based on one or more of the following: on one or more public data resources, and one or more public data resources include one or more of the following: weather data resources; satellite data resources; census, population, demographic, and / or psychographic data resources; market data resources; or e-commerce data resources; or one or more enterprise data resources, and one or more enterprise data resources include one or more of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0201] In some aspects, the techniques described herein relate to an AI-based platform, further including at least one AI-based model and / or algorithm, where the at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: one or more human labels and / or markings; one or more human interactions with a hardware and / or software system; one or more results; one or more AI-generated training data samples; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0202] In some aspects, the techniques described herein relate to an AI-based platform, where at least one modular distributed energy system is configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy transmission instances; one or more fuel deliveries; or one or more stored energy deliveries.

[0203] In some aspects, the techniques described herein relate to an AI-based platform, where a first system of the modular distributed energy system is configured to communicate with a second system of the modular distributed energy system to coordinate the delivery of energy to one or more consumption points by adjusting the energy generation, storage, delivery, and / or consumption in one or both of the first system or the second system.

[0204] In some aspects, the techniques described herein relate to an AI-based platform, where at least one modular distributed energy system is configured to adjust the delivery of energy to one or more consumption points based on a carbon generation and / or emission strategy.

[0205] In some aspects, the technologies described herein relate to an AI-based platform, wherein at least one modular distributed energy system is also configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: energy procurement and / or sales events; service fees associated with energy procurement and / or sales events; energy consumption events; energy generation events; energy distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0206] In some aspects, the technologies described herein relate to an AI-based platform, wherein at least one modular distributed energy system is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0207] In some aspects, the technologies described herein relate to an AI-based platform, wherein at least one modular distributed energy system is associated with a digital twin, and the digital twin is configured to model and / or predict one or more attributes and / or operations of the at least one modular distributed energy system.

[0208] In some aspects, the technologies described herein relate to an AI-based platform, wherein a set of modular distributed energy systems can be configured to change the amount of reserved capacity to adapt to an energy demand pattern associated with local demand requirements.

[0209] In some aspects, the technologies described herein relate to an AI-based platform, wherein a set of modular distributed energy systems can be configured to change the location of energy supply and / or access resources based on measurements and / or predictions of local demand requirements.

[0210] In some aspects, the technologies described herein relate to an AI-based platform, wherein a set of modular distributed energy systems can be configured to change an energy production schedule based on measurements and / or predictions of local demand requirements.

[0211] In some aspects, the technologies described herein relate to an AI-based platform, wherein a set of modular distributed energy systems can be configured to change the resource allocation associated with the set of modular distributed energy systems, and the allocation is based on a subset of local demand requirements.

[0212] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of power and energy, including: an artificial intelligence system configured to: perform an analysis of an energy pattern associated with an operation process involving a set of resources, the set of resources being at least partially independent of the power grid; and output a set of operation parameters to provide for energy generation, storage, and / or consumption to enable the operation process, wherein the set of operation parameters is based on the analysis.

[0213] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one of the set of operation parameters is a generation output level of a distributed energy generation resource.

[0214] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one of the set of operation parameters is a target storage level of a distributed energy storage resource.

[0215] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one of the set of operation parameters is a delivery time of a distributed energy delivery resource.

[0216] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is further configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on one or more of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0217] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin representing one or more of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0218] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin configured to perform one or more of the following: provide visual and / or analytical metrics of energy consumption of one or more energy consumers; filter energy data; highlight energy data; adjust energy data, or generate visual and / or analytical metrics of energy consumption through one or more of the following: one or more machines; one or more factories; or one or more vehicles in a fleet.

[0219] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is further configured to perform one or more of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0220] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one operating parameter is based on one or more of the following: one or more public data resources, and the one or more public data resources include one or more of the following: weather data resources; satellite data resources; census, population, demographic, and / or psychographic data resources; market data resources; or e-commerce data resources; or one or more enterprise data resources, and the one or more enterprise data resources include one or more of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0221] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is trained based on a training dataset, and the training dataset is based on one or more of the following: one or more human labels and / or markings; one or more human interactions with a hardware and / or software system; one or more results; one or more AI-generated training data samples; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0222] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy transmission instances; one or more fuel deliveries; or one or more stored energy deliveries.

[0223] In some aspects, the techniques described herein relate to an AI - based platform, wherein the artificial intelligence system is further configured to record one or more energy - related events in a distributed ledger and / or blockchain, and the one or more energy - related events include one or more of the following: energy procurement and / or sales events; service fees associated with energy procurement and / or sales events; energy consumption events; energy generation events; energy distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0224] In some aspects, the techniques described herein relate to an AI - based platform, wherein the artificial intelligence system is deployed in an off - grid environment, and the off - grid environment includes one or more of the following: an off - grid energy generation system; an off - grid energy storage system; or an off - grid energy mobilization system.

[0225] In some aspects, the techniques described herein relate to an AI - based platform, wherein the artificial intelligence system is further configured to determine the environmental impact of carbon generation and / or emissions associated with an operation process on the area associated with the operation process.

[0226] In some aspects, the techniques described herein relate to an AI - based platform, wherein the artificial intelligence system is further configured to evaluate the compliance of an operation process with one or both of the following: a carbon generation and / or emissions strategy, or a set of labor standards related to the operation process.

[0227] In some aspects, the techniques described herein relate to an AI - based platform, wherein the artificial intelligence system is further configured to adjust a set of operation parameters based on one or both of the following to provide energy generation, storage, and / or consumption associated with an operation process: a carbon generation and / or emissions strategy, or a set of labor standards related to the operation process.

[0228] In some aspects, the techniques described herein relate to an AI - based platform, wherein the artificial intelligence system is further configured to transmit a message to at least one edge device in a set of edge devices associated with an operation process, and the message includes a request to adjust at least one operation of the at least one edge device based on a set of operation parameters.

[0229] In some aspects, the techniques described herein relate to an AI - based platform, wherein the artificial intelligence system is further configured to receive metrics of the current and / or predicted energy state of at least one edge device from at least one edge device in a set of edge devices associated with an operation process, and the set of operation parameters is based on the metrics of the current and / or predicted energy state of the at least one edge device.

[0230] In some aspects, the techniques described herein relate to an AI-based platform, wherein an artificial intelligence system is further configured to determine a set of operating parameters based on the output of a digital twin, the digital twin representing at least one edge device of a set of edge devices associated with an operating process, and the output of the digital twin indicating the current and / or predicted energy state of the at least one edge device.

[0231] In some aspects, the techniques described herein relate to an AI-based platform, wherein an artificial intelligence system is further configured to coordinate a set of modular distributed energy systems to generate, store, and / or deliver energy, wherein the coordination is based on a set of operating parameters and local demand requirements.

[0232] In some aspects, the techniques described herein relate to an AI-based platform, wherein the analysis of an energy pattern associated with an operating process includes analyzing the availability of a backup power source based on a fault in at least a portion of an electrical grid.

[0233] In some aspects, the techniques described herein relate to an AI-based platform, wherein the analysis of an energy pattern associated with an operating process includes the analysis of at least one auxiliary function associated with a set of resources, and the set of operating parameters includes at least one operating parameter associated with the at least one auxiliary function.

[0234] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of power and energy, including: a policy and governance engine configured to deploy a set of rules and / or policies that govern a set of energy generation, storage, and / or consumption workloads, wherein the rules and / or policies are associated with the configuration of a set of edge devices that operate in local data communication with a set of energy generation facilities, energy storage facilities, energy delivery facilities, or energy consumption systems.

[0235] In some aspects, the techniques described herein relate to an AI-based platform, wherein when configured in the policy and governance engine, a policy associated with an energy generation instruction is automatically applied by at least one edge device to control the energy generation of at least one energy generation system controlled by the edge device.

[0236] In some aspects, the techniques described herein relate to an AI-based platform, wherein when configured in the policy and governance engine, a policy associated with an energy consumption instruction is automatically applied by at least one edge device to control the energy consumption of at least one energy consumption system controlled by the edge device.

[0237] In some aspects, the techniques described herein relate to an AI-based platform, wherein when configured in a policy and governance engine, policies associated with energy delivery instructions are automatically applied by at least one edge device to control the energy delivery of at least one energy delivery system controlled by the edge device.

[0238] In some aspects, the techniques described herein relate to an AI-based platform, wherein when configured in a policy and governance engine, policies associated with energy storage instructions are automatically applied by at least one edge device to control the energy storage of at least one energy storage system controlled by the edge device.

[0239] In some aspects, the techniques described herein relate to an AI-based platform, wherein the policy and governance engine is configured to operate on a stored set of policy templates to configure policies.

[0240] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of recommended policies is automatically generated based on a dataset of historical policies, a dataset representing the operating states and / or configurations of a set of distributed energy, and a set of historical results for presentation in the policy and governance engine.

[0241] In some aspects, the techniques described herein relate to an AI-based platform, wherein the policy and governance engine is further configured to adjust rules and / or policies based on at least one context factor, and the at least one context factor includes at least one of the following: historical data of energy transactions; at least one operating factor; at least one market factor; at least one expected market behavior; or at least one expected customer behavior.

[0242] In some aspects, the techniques described herein relate to an AI-based platform, wherein the policy and governance engine is further configured to accommodate data transmission through a network and / or communication system, and the accommodation is based on at least one of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0243] In some aspects, the techniques described herein relate to an AI-based platform further including an adaptive energy digital twin that represents at least one of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or an energy usage priority condition.

[0244] In some aspects, the techniques described herein relate to an AI-based platform that further includes an adaptive energy digital twin configured to perform at least one of the following: provide visual and / or analytical metrics of energy consumption of at least one energy consumer, filter energy data; highlight energy data; or adjust energy data.

[0245] In some aspects, the techniques described herein relate to an AI-based platform that further includes an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by at least one of the following: at least one machine; at least one factory; or at least one vehicle in a fleet.

[0246] In some aspects, the techniques described herein relate to an AI-based platform, wherein the policy and governance engine is further configured to perform at least one of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0247] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one rule and / or policy is based on at least one common data resource, and the at least one common data resource includes at least one of the following: weather data resource; satellite data resource; census, population, demographic, and / or psychographic data resource; market data resource; or e-commerce data resource.

[0248] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one rule and / or policy is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0249] In some aspects, the techniques described herein relate to an AI-based platform, further including at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or annotation; at least one human interaction with a hardware and / or software system; at least one result; at least one AI-generated training data sample; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0250] In some aspects, the techniques described herein relate to an AI-based platform, wherein a policy and governance engine is configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: at least one fixed transmission line; at least one wireless energy transmission instance; at least one fuel delivery; or at least one stored energy delivery.

[0251] In some aspects, the techniques described herein relate to an AI-based platform, wherein the policy and governance engine is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: an energy purchase and / or sale event; a service fee associated with the energy purchase and / or sale event; an energy consumption event; an energy generation event; an energy distribution event; an energy storage event; a carbon emission generation event; a carbon emission reduction event; a renewable energy credit event; a pollution generation event; or a pollution reduction event.

[0252] In some aspects, the techniques described herein relate to an AI-based platform, wherein the policy and governance engine is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0253] In some aspects, the techniques described herein relate to an AI-based platform, wherein the policy and governance engine is further configured to generate and / or execute at least one smart contract, and each of the at least one smart contracts applies rules and / or policies to at least one energy-related transaction.

[0254] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of rules and / or policies is based on at least one objective associated with a set of energy generation, storage, and / or consumption workloads, and the policy and governance engine is further configured to deploy updates to the set of rules and / or policies to a set of edge devices based on the objective.

[0255] In some aspects, the techniques described herein relate to an AI-based platform, wherein the policy and governance engine is further configured to deploy at least one instruction to a set of edge devices to adapt at least one operating parameter associated with at least one industrial machine and / or industrial process controlled by the set of edge devices.

[0256] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of power and energy, comprising: a set of edge devices configured to: communicate with at least one energy generation facility, energy storage facility, and / or energy consumption system, and automatically execute a set of pre-configured policies that manage energy generation, energy storage, or energy consumption of the corresponding energy generation facility, energy storage facility, or energy consumption system.

[0257] In some aspects, the techniques described herein relate to an AI-based platform, wherein the automatically executed policies are a set of context policies that are adjusted based on the current state of a set of energy generation entities in an energy grid.

[0258] In some aspects, the techniques described herein relate to an AI-based platform, wherein the automatically executed policies are a set of context policies that are adjusted based on the current state of a set of energy generation entities in an energy generation environment, the energy generation environment including an energy grid and a set of distributed energy sources operating independently of the energy grid.

[0259] In some aspects, the techniques described herein relate to an AI-based platform, wherein the automatically executed policies are a set of context policies that are adjusted based on the current state of a set of energy storage entities in an energy grid.

[0260] In some aspects, the techniques described herein relate to an AI-based platform, wherein the automatically executed policies are a set of context policies that are adjusted based on the current state of a set of energy storage entities in an energy storage environment, the energy storage environment including an energy grid and a set of distributed energy resources operating independently of the energy grid, wherein the automatically executed policies are a set of context policies that are adjusted based on the current state of a set of energy delivery entities in an energy grid.

[0261] In some aspects, the techniques described herein relate to an AI-based platform, wherein the automatically executed policies are a set of context policies that are adjusted based on the current state of a set of energy transmission entities in an energy transmission environment, the energy transmission environment including an energy grid and a set of distributed energy resources operating independently of the energy grid.

[0262] In some aspects, the techniques described herein relate to an AI-based platform in which the automatically executed policies are a set of context policies that are adjusted based on the current state of a set of energy-consuming entities that consume energy from an energy grid.

[0263] In some aspects, the techniques described herein relate to an AI-based platform in which the automatically executed policies are a set of context policies that are adjusted based on the current state of a set of energy-consuming entities that consume energy from an energy grid and from a set of distributed energy resources operating independent of the energy grid.

[0264] In some aspects, the techniques described herein relate to an AI-based platform in which a set of edge devices are also configured to adjust a set of pre-configured policies based on at least one context factor, and the at least one context factor includes at least one of the following: historical data of energy transactions; at least one operational factor; at least one market factor; at least one expected market behavior; or at least one expected customer behavior.

[0265] In some aspects, the techniques described herein relate to an AI-based platform in which at least one edge device is also configured to adapt to data transmission over a network and / or communication system, where the adaptation is based on at least one of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transport cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0266] In some aspects, the techniques described herein relate to an AI-based platform further including an adaptive energy digital twin that represents at least one of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0267] In some aspects, the techniques described herein relate to an AI-based platform further including an adaptive energy digital twin that is configured to perform at least one of the following: provide visual and / or analytical metrics of energy consumption of at least one energy consumer, filter energy data; highlight energy data; or adjust energy data.

[0268] In some aspects, the techniques described herein relate to an AI-based platform further including an adaptive energy digital twin that is configured to generate visual and / or analytical metrics of energy consumption through at least one of the following: at least one machine; at least one factory; or at least one vehicle in a fleet.

[0269] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device is further configured to perform at least one of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0270] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one preconfigured policy is based on at least one common data resource, and the at least one common data resource includes at least one of the following: weather data resource; satellite data resource; census, population, demographic, and / or psychographic data resource; market data resource; or e-commerce data resource.

[0271] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one preconfigured policy is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0272] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device includes at least one AI-based model and / or algorithm, and the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on at least one of the following: at least one human label and / or annotation; at least one human interaction with a hardware and / or software system; at least one result; at least one AI-generated training data sample; supervised learning training process; semi-supervised learning training process; or deep learning training process.

[0273] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: at least one fixed transmission line; at least one wireless energy transmission instance; at least one fuel delivery; or at least one stored energy delivery.

[0274] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device is also configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: an energy procurement and / or sales event; a service fee associated with the energy procurement and / or sales event; an energy consumption event; an energy generation event; an energy distribution event; an energy storage event; a carbon emission generation event; a carbon emission reduction event; a renewable energy credit event; a pollution generation event; or a pollution reduction event.

[0275] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0276] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of edge devices is also configured to: determine at least one energy availability pattern based on communication with at least one energy generation facility, energy storage facility, and / or energy consumption system, and update the execution of a set of preconfigured policies based on the at least one pattern.

[0277] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device in a set of edge devices is configured to manage the operation of an industrial facility, and the set of preconfigured policies is based on at least one energy goal associated with the industrial facility.

[0278] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one energy generation facility, energy storage facility, and / or energy consumption system is located in a geographical region, and the set of preconfigured policies is based on at least one energy goal associated with the geographical region.

[0279] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of edge devices is configured to automatically execute a set of preconfigured policies by adjusting at least one of the allocation of energy resources associated with at least one energy generation facility, energy storage facility, and / or energy consumption system or the schedule of processes performed by at least one energy generation facility, energy storage facility, and / or energy consumption system.

[0280] In some aspects, the techniques described herein relate to an AI-based platform for implementing intelligent coordination and management of electricity and energy, including: a machine learning system that is trained on a set of energy intelligence data and deployed on an edge device, wherein the machine learning system is configured to receive additional training performed by the edge device to improve energy management.

[0281] In some aspects, the techniques described herein relate to an AI-based platform, where energy management includes managing the energy generation of a set of distributed energy generation resources.

[0282] In some aspects, the techniques described herein relate to an AI-based platform, where energy management includes managing the energy storage of a set of distributed energy storage resources.

[0283] In some aspects, the techniques described herein relate to an AI-based platform, where energy management includes managing the energy delivery of a set of distributed energy delivery resources.

[0284] In some aspects, the techniques described herein relate to an AI-based platform, where energy management includes managing the energy consumption of a set of distributed energy consumption resources.

[0285] In some aspects, the techniques described herein relate to an AI-based platform, where energy management is based on a set of rules and / or policies associated with edge devices and a set of energy generation facilities, energy storage facilities, energy delivery facilities, or energy consumption systems.

[0286] In some aspects, the techniques described herein relate to an AI-based platform, where the machine learning system is further configured to adapt data transmission through a network and / or communication system, where the adaptation is based on at least one of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0287] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin, where the adaptive energy digital twin represents at least one of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0288] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin, where the adaptive energy digital twin is configured to perform at least one of the following: provide visual and / or analytical metrics of the energy consumption of at least one energy consumer, filter energy data; highlight energy data; or adjust energy data.

[0289] In some aspects, the techniques described herein relate to an AI-based platform that also includes an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption through at least one of: at least one machine; at least one factory; or at least one vehicle in a fleet.

[0290] In some aspects, the techniques described herein relate to an AI-based platform, wherein the machine learning system is also configured to perform at least one of: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0291] In some aspects, the techniques described herein relate to an AI-based platform, wherein the energy intelligence data is based on at least one common data resource, and the at least one common data resource includes at least one of: a weather data resource; a satellite data resource; a census, population, demographics, and / or psychographics data resource; a market data resource; or an e-commerce data resource.

[0292] In some aspects, the techniques described herein relate to an AI-based platform, wherein the energy intelligence data is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0293] In some aspects, the techniques described herein relate to an AI-based platform, wherein the machine learning system is also trained based on a training data set, and the training data set is based on at least one of: at least one human label and / or annotation; at least one human interaction with a hardware and / or software system; at least one result; at least one AI-generated training data sample; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0294] In some aspects, the techniques described herein relate to an AI-based platform, wherein the machine learning system is also configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of: at least one fixed transmission line; at least one wireless energy transmission instance; at least one fuel delivery; or at least one stored energy delivery.

[0295] In some aspects, the techniques described herein relate to an AI-based platform, wherein the machine learning system is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: an energy procurement and / or sales event; a service fee associated with the energy procurement and / or sales event; an energy consumption event; an energy generation event; an energy distribution event; an energy storage event; a carbon emission generation event; a carbon emission reduction event; a renewable energy credit event; a pollution generation event; or a pollution reduction event.

[0296] In some aspects, the techniques described herein relate to an AI-based platform, wherein edge devices are deployed in an off-grid environment, and the off-grid environment includes at least one of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0297] In some aspects, the techniques described herein relate to an AI-based platform, wherein the edge devices are located near at least one entity that generates, stores, transports, and / or uses energy.

[0298] In some aspects, the techniques described herein relate to an AI-based platform, wherein the edge devices provide information about the energy status and / or energy flow of at least one entity that generates, stores, transports, and / or uses energy.

[0299] In some aspects, the techniques described herein relate to an AI-based platform, wherein the edge devices contain and / or govern at least one sensor in a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transport, and / or use energy.

[0300] In some aspects, the techniques described herein relate to an AI-based platform, wherein the edge devices are associated with a situation and / or environment, and the edge devices are further configured to perform additional training of the machine learning system in response to changes in the situation and / or environment.

[0301] In some aspects, the techniques described herein relate to an AI-based platform, wherein the edge devices are further configured to perform additional training of the machine learning system based on the determination of model drift by the machine learning system.

[0302] In some aspects, the techniques described herein relate to an AI-based platform, wherein the additional training is based on a set of energy intelligence data on which the machine learning system was initially trained and additional energy intelligence data on which the machine learning system has not been trained.

[0303] In some aspects, the techniques described herein relate to an AI-based platform, where additional training includes adding a machine learning system to a collection that includes at least one other artificial intelligence system.

[0304] In some aspects, the techniques described herein relate to an AI-based platform, where a set of energy intelligence data is based on at least one energy-related policy and / or rule, and additional training is based on changes to at least one energy-related policy and / or rule.

[0305] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of electricity and energy, including: a set of edge devices, the set of edge devices including a set of artificial intelligence systems, the set of artificial intelligence systems configured to: process data processed by the edge devices; and based on the data, determine a mix of energy generation, storage, delivery, and / or consumption characteristics of a set of systems that communicate locally with the edge devices, and output a data set representing the composition ratios of the mix.

[0306] In some aspects, the techniques described herein relate to an AI-based platform, where the output data set indicates the fraction of energy generated by an energy grid and the fraction of energy generated by a set of distributed energy sources operating independently of the energy grid.

[0307] In some aspects, the techniques described herein relate to an AI-based platform, where the output data set indicates the fraction of energy generated by renewable energy sources and the fraction of energy generated by non-renewable resources.

[0308] In some aspects, the techniques described herein relate to an AI-based platform, where the output data set indicates the fraction of energy generation by type for each of a series of time intervals.

[0309] In some aspects, the techniques described herein relate to an AI-based platform, where the output data set indicates the carbon generation associated with the energy generation of each energy type in an energy mix during each of a series of time intervals.

[0310] In some aspects, the techniques described herein relate to an AI-based platform, where the output data set indicates the carbon emissions associated with the energy generation of each energy type in an energy mix during each of a series of time intervals.

[0311] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device is further configured to adapt data transmission over a network and / or communication system, wherein the adaptation is based on at least one of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0312] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin representing at least one of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0313] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption of at least one energy consumer, filtering energy data; highlighting energy data; or adjusting energy data.

[0314] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by at least one of the following: at least one machine; at least one factory; or at least one vehicle in a fleet.

[0315] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device is further configured to perform at least one of the following: extracting energy-related data; detecting and / or correcting errors in energy-related data; converting, transforming, normalizing, and / or cleaning energy-related data; parsing energy-related data; detecting patterns, content, and / or objects in energy-related data; compressing energy-related data; streaming energy-related data; filtering energy-related data; loading and / or storing energy-related data; routing and / or transmitting energy-related data; or maintaining the security of energy-related data.

[0316] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data is based on at least one common data resource, the common data resource including at least one of the following: a weather data resource; a satellite data resource; a census, population, demographic, and / or psychographic data resource; a market data resource; or an e-commerce data resource.

[0317] In some aspects, the techniques described herein relate to an AI-based platform, wherein data is based on at least one enterprise data resource, and the enterprise data resource includes at least one of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0318] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device includes at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on at least one of the following: at least one human label and / or annotation; at least one human interaction with a hardware and / or software system; at least one result; at least one AI-generated training data sample; supervised learning training process; semi-supervised learning training process; or deep learning training process.

[0319] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device is further configured to coordinate the delivery of energy to at least one point of consumption, and the energy delivery includes at least one of the following: at least one fixed transmission line; at least one wireless energy transfer instance; at least one fuel delivery; or at least one stored energy delivery.

[0320] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: energy procurement and / or sales event; service fee associated with the energy procurement and / or sales event; energy consumption event; energy generation event; energy distribution event; energy storage event; carbon emission generation event; carbon emission reduction event; renewable energy credit event; pollution generation event; or pollution reduction event.

[0321] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one edge device is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: off-grid energy generation system; off-grid energy storage system; or off-grid energy mobilization system.

[0322] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least a portion of a set of edge devices is located near at least one entity that generates, stores, delivers, and / or uses energy.

[0323] In some aspects, the techniques described herein relate to an AI-based platform in which a set of edge devices provides information about the energy state and / or energy flow of at least one entity involved in generating, storing, delivering, and / or using energy.

[0324] In some aspects, the techniques described herein relate to an AI-based platform in which a set of edge devices includes and / or governs at least one sensor from a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, deliver, and / or use energy.

[0325] In some aspects, the techniques described herein relate to an AI-based platform in which the mix of energy generation, storage, delivery, and / or consumption characteristics is based on at least one energy demand requirement associated with a set of edge devices.

[0326] In some aspects, the techniques described herein relate to an AI-based platform in which the mix of energy generation, storage, delivery, and / or consumption characteristics is based on the priorities of energy collection, storage, transportation, and / or use associated with each energy source associated with a set of edge devices.

[0327] In some aspects, the techniques described herein relate to an AI-based platform in which the mix of energy generation, storage, delivery, and / or consumption characteristics is based on a schedule for storage, transportation, and / or use associated with each energy source associated with a set of edge devices.

[0328] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of electricity and energy, including: a data processing system configured to fuse at least one entity that generates, stores, delivers, or consumes a grid data set of an energy grid entity with at least one entity that generates, stores, delivers, and / or consumes a data set of an off-grid energy entity.

[0329] In some aspects, the techniques described herein relate to an AI-based platform in which the data processing system is configured to automatically time-align energy grid entity data with off-grid energy entity data.

[0330] In some aspects, the techniques described herein relate to an AI-based platform in which the data processing system is configured to automatically collect off-grid energy entity sensor data from a set of edge devices and control a set of off-grid energy entities via the set of edge devices.

[0331] In some aspects, the techniques described herein relate to an AI-based platform in which the data processing system is configured to automatically normalize energy grid entity data and off-grid energy entity data to present the data according to a set of common units.

[0332] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data processing system is further configured to accommodate data transmission over a network and / or communication system, where the accommodation is based on at least one of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0333] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin that represents at least one of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0334] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin that is configured to perform at least one of the following: provide visual and / or analytical metrics of energy consumption of at least one energy consumer, filter energy data; highlight energy data; or adjust energy data.

[0335] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin that is configured to generate visual and / or analytical metrics of energy consumption through at least one of the following: at least one machine; at least one factory; or at least one vehicle in a fleet.

[0336] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data processing system is further configured to perform at least one of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0337] In some aspects, the technologies described herein relate to an AI-based platform that also includes at least one AI-based model and / or algorithm, where the at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or annotation; at least one human interaction with a hardware and / or software system; at least one result; at least one AI-generated training data sample; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0338] In some aspects, the technologies described herein relate to an AI-based platform, where the data processing system is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: at least one fixed transmission line; at least one wireless energy transfer instance; at least one fuel delivery; or at least one stored energy delivery.

[0339] In some aspects, the technologies described herein relate to an AI-based platform, where the data processing system is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: an energy purchase and / or sale event; a service fee associated with an energy purchase and / or sale event; an energy consumption event; an energy generation event; an energy distribution event; an energy storage event; a carbon emission generation event; a carbon emission reduction event; a renewable energy credit event; a pollution generation event; or a pollution reduction event.

[0340] In some aspects, the technologies described herein relate to an AI-based platform, where at least one entity of an off-grid energy generation, storage, and / or consumption dataset is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy dispatch system.

[0341] In some aspects, the technologies described herein relate to an AI-based platform, where the data processing system is further configured to intelligently coordinate and manage electricity and / or energy based on a dataset of energy generation, storage, and / or consumption data of a set of infrastructure assets, and the dataset is at least partially generated by a set of sensors included in and / or managed by a set of edge devices.

[0342] In some aspects, the technologies described herein relate to an AI-based platform, where the data processing system is further configured to manage at least one of the following: generating energy by a set of distributed energy generation resources; storing energy by a set of distributed energy storage resources; delivering energy by a set of distributed energy delivery resources; or consuming energy by a set of distributed energy consumption resources.

[0343] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data processing system is further configured to intelligently coordinate and manage the power and / or energy of a set of entities, where the set of entities includes at least one of the following: weather data resources; satellite data resources; census, population, demographic, and / or psychographic data resources; market data resources; or e-commerce data resources.

[0344] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data processing system is further configured to execute at least one algorithm that simulates the energy consumption of at least one entity, where the simulation is based on a data set that includes alternative state or event parameters of the at least one entity, the alternative state or event parameters reflecting alternative consumption scenarios, and the algorithm accesses a demand response model that describes how energy demand responds to changes in energy prices or the prices of operations or activities that consume energy.

[0345] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data processing system includes a policy and governance engine that is configured to deploy a set of rules and / or policies to at least one edge device that communicates locally with at least one entity, and the edge device is configured to govern the at least one entity based on the rules and / or policies.

[0346] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data processing system includes an analysis system that represents a set of operating parameters and a current state of at least one entity based on a set of sensed parameters, the set of sensed parameters being generated by a set of edge devices located near the at least one entity, and the analysis system is configured to provide recommendations associated with at least one of the at least one entity or at least one additional available entity.

[0347] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data processing system includes an artificial intelligence system that is trained on a historical data set related to the generation, storage, and / or utilization of energy in an operating process associated with at least one entity, and the data processing system is further configured to: analyze the energy pattern of the operating process and, based on the current state and / or information associated with the at least one entity, output a prediction of the energy demand of the operating process.

[0348] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data processing system is further configured to fuse grid data sets generated, stored, transported, or consumed by grid energy entities and off-grid energy entities that generate, store, transport, and / or consume data sets with at least one entity that generates, stores, transports, or consumes grid data sets for backup and / or auxiliary energy generation.

[0349] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data processing system is further configured to coordinate the development of energy grid resources and / or off-grid energy resources based on the grid data sets generated, stored, transported, or consumed by the integrated energy grid entities and the data sets generated, stored, transported, and / or consumed by the off-grid energy entities.

[0350] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of electricity and energy, including: a set of autonomous coordination systems for improving the delivery of a set of heterogeneous energy types to a consumption point based on: the location of the consumption point, and a set of consumption attributes including at least one of the following: peak power demand at the consumption point; continuity of power demand at the consumption point; and type of energy that can be used at the consumption point.

[0351] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of autonomous coordination systems coordinates the delivery of a defined type of energy generation capacity to a consumption point.

[0352] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of autonomous coordination systems coordinates the delivery of a defined type of energy storage capacity to a consumption point.

[0353] In some aspects, the techniques described herein relate to an AI-based platform, wherein the type of energy that can be used is determined at least in part based on a set of operational compatibility parameters.

[0354] In some aspects, the techniques described herein relate to an AI-based platform, wherein the type of energy that can be used is determined at least in part based on a set of governance parameters.

[0355] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of governance parameters relates to the use of renewable energy.

[0356] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of governance parameters relates to carbon generation or emissions.

[0357] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one of a set of autonomous coordination systems is further configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on at least one of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transport cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0358] In some aspects, the techniques described herein relate to an AI-based platform that further includes an Adaptive Energy Digital Twin that represents at least one of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; a market condition; or an energy usage priority condition.

[0359] In some aspects, the techniques described herein relate to an AI-based platform that further includes an Adaptive Energy Digital Twin that is configured to perform at least one of the following: provide visual and / or analytical metrics of the energy consumption of at least one energy consumer, filter energy data; highlight energy data; or adjust energy data.

[0360] In some aspects, the techniques described herein relate to an AI-based platform that further includes an Adaptive Energy Digital Twin that is configured to generate visual and / or analytical metrics of energy consumption by at least one of the following: at least one machine; at least one factory; or at least one vehicle in a fleet.

[0361] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one of a group of autonomous coordination systems is further configured to perform at least one of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0362] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one consumption attribute is based on at least one common data resource, and the common data resource includes at least one of the following: a weather data resource; a satellite data resource; a census, population, demographic, and / or psychographic data resource; a market data resource; or an e-commerce data resource.

[0363] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one consumption attribute is based on at least one enterprise data resource, and the enterprise data resource includes at least one of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0364] In some aspects, the techniques described herein relate to an AI-based platform that also includes at least one AI-based model and / or algorithm, where the at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or annotation; at least one human interaction with a hardware and / or software system; at least one outcome; at least one AI-generated training data sample; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0365] In some aspects, the techniques described herein relate to an AI-based platform, where at least one of a set of autonomous coordination systems is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: at least one fixed transmission line; at least one wireless energy transfer instance; at least one fuel delivery; or at least one stored energy delivery.

[0366] In some aspects, the techniques described herein relate to an AI-based platform, where at least one of a set of autonomous coordination systems is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: an energy purchase and / or sale event; a service fee associated with an energy purchase and / or sale event; an energy consumption event; an energy generation event; an energy distribution event; an energy storage event; a carbon emission generation event; a carbon emission reduction event; a renewable energy credit event; a pollution generation event; or a pollution reduction event.

[0367] In some aspects, the techniques described herein relate to an AI-based platform, where at least one of a set of autonomous coordination systems is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0368] In some aspects, the techniques described herein relate to an AI-based platform, where a set of autonomous coordination systems is further configured to determine the delivery of a set of heterogeneous energy types based on a set of rules and / or policies that govern a set of energy generation, storage, and / or consumption workloads, and the rules and / or policies are associated with the configuration of a set of edge devices that perform local data communication operations with a set of energy generation facilities, energy storage facilities, energy delivery facilities, or energy consumption systems.

[0369] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of autonomous coordination systems is also configured to determine the delivery of a set of heterogeneous energy types based on an energy consumption simulation of at least one energy consumer. The simulation is based on a data set including alternative state or event parameters of at least one of the at least one energy consumer, where the alternative state or event parameters reflect alternative consumption scenarios, and the simulation is based on a demand response model that describes how energy demand responds to changes in energy prices or the prices of operations or activities that consume energy.

[0370] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of autonomous coordination systems improves the delivery of a set of heterogeneous energy types to a consumption point by matching each of the set of heterogeneous energy types with at least one consumer associated with the consumption point.

[0371] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of autonomous coordination systems improves the delivery of a set of heterogeneous energy types to a consumption point by determining the development of additional energy for one or more energy types, and the development is based on a prediction of the energy demand associated with the consumption point.

[0372] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of autonomous coordination systems improves the delivery of a set of heterogeneous energy types to a consumption point by comparing the characteristics of the energy demand associated with the consumption point and the characteristics of each energy type in the set of heterogeneous energy types.

[0373] In some aspects, the techniques described herein relate to an AI-based platform for implementing intelligent coordination and management of electricity and energy, including: an intelligent agent trained on a data set interacting with experts in an energy supply system, wherein the intelligent agent is trained to generate at least one recommendation and / or instruction for optimization with respect to at least one energy goal and at least one other goal.

[0374] In some aspects, the techniques described herein relate to an AI-based platform, wherein another goal is an operational goal of an enterprise.

[0375] In some aspects, the techniques described herein relate to an AI-based platform, wherein the intelligent agent operates on status data from a set of edge devices and controls a set of energy generation resources via the set of edge devices.

[0376] In some aspects, the techniques described herein relate to an AI-based platform, wherein the intelligent agent operates on status data from a set of edge devices and controls a set of energy consumption resources via the set of edge devices.

[0377] In some aspects, the techniques described herein relate to an AI-based platform where intelligent agents operate on status data from a set of edge devices and control a set of energy storage resources via the edge devices.

[0378] In some aspects, the techniques described herein relate to an AI-based platform where intelligent agents operate on status data from a set of edge devices and control a set of energy delivery resources via a set of edge devices.

[0379] In some aspects, the techniques described herein relate to an AI-based platform where the intelligent agents are also configured to adapt data transmission over a network and / or communication system, where the adaptation is based on at least one of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0380] In some aspects, the techniques described herein relate to an AI-based platform further including an adaptive energy digital twin that represents at least one of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0381] In some aspects, the techniques described herein relate to an AI-based platform further including an adaptive energy digital twin that is configured to perform at least one of the following: provide visual and / or analytical metrics of energy consumption of at least one energy consumer, filter energy data; highlight energy data; or adjust energy data.

[0382] In some aspects, the techniques described herein relate to an AI-based platform further including an adaptive energy digital twin that is configured to generate visual and / or analytical metrics of energy consumption through at least one of the following: at least one machine; at least one factory; or at least one vehicle in a fleet.

[0383] In some aspects, the techniques described herein relate to an AI-based platform where the intelligent agents are also configured to perform at least one of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0384] In some aspects, the techniques described herein relate to an AI-based platform, wherein the dataset is based on at least one common data resource, and the common data resource includes at least one of the following: weather data resource; satellite data resource; census, population, demographic, and / or psychographic data resource; market data resource; or e-commerce data resource.

[0385] In some aspects, the techniques described herein relate to an AI-based platform, wherein the dataset is based on at least one enterprise data resource, and the enterprise data resource includes at least one of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0386] In some aspects, the techniques described herein relate to an AI-based platform, wherein the intelligent agent is trained based on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or annotation; at least one human interaction with a hardware and / or software system; at least one result; at least one AI-generated training data sample; supervised learning training process; semi-supervised learning training process; or deep learning training process.

[0387] In some aspects, the techniques described herein relate to an AI-based platform, wherein the intelligent agent is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: at least one fixed transmission line; at least one wireless energy transmission instance; at least one fuel delivery; or at least one stored energy delivery.

[0388] In some aspects, the techniques described herein relate to an AI-based platform, wherein the intelligent agent is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: energy procurement and / or sales event; service fee associated with the energy procurement and / or sales event; energy consumption event; energy generation event; energy distribution event; energy storage event; carbon emission generation event; carbon emission reduction event; renewable energy credit event; pollution generation event; or pollution reduction event.

[0389] In some aspects, the techniques described herein relate to an AI-based platform, wherein the intelligent agent is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: off-grid energy generation system; off-grid energy storage system; or off-grid energy mobilization system.

[0390] In some aspects, the techniques described herein relate to an AI-based platform in which intelligent agents are located near at least one entity that generates, stores, transports, and / or uses energy.

[0391] In some aspects, the techniques described herein relate to an AI-based platform in which intelligent agents provide information about the energy state and / or energy flow of at least one entity that generates, stores, transports, and / or uses energy.

[0392] In some aspects, the techniques described herein relate to an AI-based platform in which intelligent agents manage at least one sensor in a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transport, and / or use energy.

[0393] In some aspects, the techniques described herein relate to an AI-based platform in which intelligent agents are further configured to manage at least one processing task associated with at least one device, and at least one recommendation and / or instruction includes adjusting at least one processing task based on at least one energy goal and / or at least one other goal.

[0394] In some aspects, the techniques described herein relate to an AI-based platform in which intelligent agents are further configured to migrate between at least two devices and, when residing on each of the at least two devices, apply at least one recommendation and / or instruction to the device on which the intelligent agent resides.

[0395] In some aspects, the techniques described herein relate to an AI-based platform in which intelligent agents are further configured to exchange information with at least one other intelligent agent, and the information is based on one or both of at least one recommendation and / or instruction, or at least one energy goal and / or at least one other goal.

[0396] In some aspects, the techniques described herein relate to an AI-based platform in which recommendations and / or instructions are associated with at least one device, and intelligent agents are further configured to exchange collected and / or determined data associated with at least one device with at least one other intelligent agent.

[0397] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of electricity and energy, including: an artificial intelligence system that operates on a set of energy generation, energy storage, energy transport, and / or energy consumption results, wherein the artificial intelligence system is configured to: analyze a dataset of current energy generation, current energy storage, current energy transport, and / or current energy consumption information, and provide recommendations including at least one operating parameter that meets the energy demands of moving entities or fixed-location energy demands within the domain.

[0398] In some aspects, the techniques described herein relate to an AI-based platform, where the domain includes a defined geographical location and a defined time period.

[0399] In some aspects, the techniques described herein relate to an AI-based platform, where at least one operating parameter indicates a generation instruction for a set of energy generation resources.

[0400] In some aspects, the techniques described herein relate to an AI-based platform, where at least one operating parameter indicates a storage instruction for a set of energy storage resources.

[0401] In some aspects, the techniques described herein relate to an AI-based platform, where at least one operating parameter indicates a delivery instruction for a set of energy delivery resources.

[0402] In some aspects, the techniques described herein relate to an AI-based platform, where at least one operating parameter indicates a consumption instruction for a set of entities that consume energy.

[0403] In some aspects, the techniques described herein relate to an AI-based platform, where the artificial intelligence system is further configured to adapt data transmission over a network and / or communication system, where the adaptation is based on at least one of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0404] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin, the adaptive energy digital twin representing at least one of the following: energy stakeholder entities; energy distribution resources; stakeholder information technology; network infrastructure entities; energy-related stakeholder production facilities; stakeholder transportation systems; market conditions; or energy usage priority conditions.

[0405] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin, the adaptive energy digital twin being configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption of at least one energy consumer, filtering energy data; highlighting energy data; or adjusting energy data.

[0406] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin, the adaptive energy digital twin being configured to generate visual and / or analytical metrics of energy consumption through at least one of the following: at least one machine; at least one factory; or at least one vehicle in a fleet.

[0407] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is further configured to perform at least one of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0408] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data set is based on at least one common data resource, and the at least one common data resource includes at least one of the following: weather data resource; satellite data resource; census, population, demographic, and / or psychographic data resource; market data resource; or e-commerce data resource.

[0409] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data set is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0410] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is trained based on a training data set, and the training data set is based on at least one of the following: at least one human label and / or annotation; at least one human interaction with a hardware and / or software system; at least one result; at least one AI-generated training data sample; supervised learning training process; semi-supervised learning training process; or deep learning training process.

[0411] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: at least one fixed transmission line; at least one wireless energy transmission instance; at least one fuel delivery; or at least one stored energy delivery.

[0412] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: an energy procurement and / or sales event; a service fee associated with the energy procurement and / or sales event; an energy consumption event; an energy generation event; an energy distribution event; an energy storage event; a carbon emission generation event; a carbon emission reduction event; a renewable energy credit event; a pollution generation event; or a pollution reduction event.

[0413] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0414] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is located near at least one entity that generates, stores, transports, and / or uses energy.

[0415] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system provides information about the energy status and / or energy flow of at least one entity that generates, stores, transports, and / or uses energy.

[0416] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system manages at least one sensor in a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transport, and / or use energy.

[0417] In some aspects, the techniques described herein relate to an AI-based platform, wherein the domain includes at least one boundary, and the data set is restricted based on the at least one boundary associated with the domain.

[0418] In some aspects, the techniques described herein relate to an AI-based platform, wherein the recommendation is based on at least one constraint associated with at least one operating parameter, and the artificial intelligence system is trained to analyze the data set based on the at least one constraint.

[0419] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of power and energy, including: an artificial intelligence system configured to: analyze a dataset of monitored local conditions, and generate a recommended configuration for at least one of a group of distributed systems, where each of the group of distributed systems can be configured to produce energy and consume energy, and where the configuration causes at least one of the distributed systems to generate and / or consume energy based on the monitored local conditions.

[0420] In some aspects, the techniques described herein relate to an AI-based platform, where the artificial intelligence system configures a plurality of distributed systems in a group such that a set of aggregate performance requirements are met across the plurality of distributed systems.

[0421] In some aspects, the techniques described herein relate to an AI-based platform, where the aggregate performance requirements are a set of economic performance requirements.

[0422] In some aspects, the techniques described herein relate to an AI-based platform, where the aggregate performance requirements are a set of regulatory performance requirements.

[0423] In some aspects, the techniques described herein relate to an AI-based platform, where the aggregate performance requirements are related to carbon generation or emissions.

[0424] In some aspects, the techniques described herein relate to an AI-based platform, where the aggregate performance requirements are a set of consumption requirements.

[0425] In some aspects, the techniques described herein relate to an AI-based platform, where the artificial intelligence system is further configured to adapt data transmission over a network and / or communication system, where the adaptation is based on at least one of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transport cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0426] In some aspects, the techniques described herein relate to an AI-based platform, further including an adaptive energy digital twin representing at least one of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0427] In some aspects, the techniques described herein relate to an AI-based platform that also includes an adaptive energy digital twin configured to perform at least one of the following: provide visual and / or analytical metrics of the energy consumption of at least one energy consumer, filter energy data; highlight energy data; or adjust energy data.

[0428] In some aspects, the techniques described herein relate to an AI-based platform that also includes an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by at least one of the following: at least one machine; at least one factory; or at least one vehicle in a fleet.

[0429] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is also configured to perform at least one of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0430] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data set is based on at least one common data resource, and the common data resource includes at least one of the following: weather data resource; satellite data resource; census, population, demographic, and / or psychographic data resource; market data resource; or e-commerce data resource.

[0431] In some aspects, the techniques described herein relate to an AI-based platform, wherein the data set is based on at least one enterprise data resource, and the enterprise data resource includes at least one of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0432] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is also configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: at least one fixed transmission line; at least one wireless energy transmission instance; at least one fuel delivery; or at least one stored energy delivery.

[0433] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is also configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: an energy procurement and / or sales event; a service fee associated with an energy procurement and / or sales event; an energy consumption event; an energy generation event; an energy distribution event; an energy storage event; a carbon emission generation event; a carbon emission reduction event; a renewable energy credit event; a pollution generation event; or a pollution reduction event.

[0434] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0435] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is trained based on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or annotation; at least one human interaction with a hardware and / or software system; at least one result; at least one AI-generated training data sample; a supervised learning training process; a semi-supervised learning training process; or a deep learning training process.

[0436] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system is located near at least one entity that generates, stores, transports, and / or uses energy.

[0437] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system provides information about the energy status and / or energy flow of at least one entity that generates, stores, transports, and / or uses energy.

[0438] In some aspects, the techniques described herein relate to an AI-based platform, wherein the artificial intelligence system manages at least one sensor in a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transport, and / or use energy.

[0439] In some aspects, the techniques described herein relate to an AI-based platform, wherein the recommended configuration is based on at least one auxiliary power resource associated with a set of distributed systems.

[0440] In some aspects, the techniques described herein relate to an AI-based platform, where the recommended configuration is based on at least one of the following: the current and / or predicted location of at least one distributed system in a set of distributed systems, or the current and / or predicted location of at least one energy resource associated with a set of distributed systems.

[0441] In some aspects, the techniques described herein relate to an AI-based platform, where the recommended configuration is further based on at least one of the following: the local demand situation associated with the current and / or predicted location of at least one distributed system in a set of distributed systems, or the local demand situation associated with the current and / or predicted location of at least one energy resource associated with a set of distributed systems.

[0442] In some aspects, the techniques described herein relate to an AI-based platform for implementing intelligent coordination and management of electricity and energy, including: a set of adaptive autonomous data processing systems that are configured to collect and transmit energy data from a set of edge-connected devices, and control a set of distributed energy entities via the set of edge-connected devices, where the data processing systems are trained based on a training data set to identify a set of events and / or signals indicating at least one energy pattern of the set of distributed energy entities.

[0443] In some aspects, the techniques described herein relate to an AI-based platform, where the set of distributed energy entities includes at least one energy generation resource.

[0444] In some aspects, the techniques described herein relate to an AI-based platform, where the set of distributed energy entities includes at least one energy consumption entity.

[0445] In some aspects, the techniques described herein relate to an AI-based platform, where the set of distributed energy entities includes at least one energy storage resource.

[0446] In some aspects, the techniques described herein relate to an AI-based platform, where the set of distributed energy entities includes at least one energy delivery resource.

[0447] In some aspects, the techniques described herein relate to an AI-based platform, where the training data set includes historical energy generation data of a set of entities similar to the entities controlled via the edge-connected devices.

[0448] In some aspects, the techniques described herein relate to an AI-based platform, where the training data set includes historical energy consumption data of a set of entities similar to the entities controlled via the edge-connected devices.

[0449] In some aspects, the techniques described herein relate to an AI-based platform, wherein a training data set includes historical energy delivery data for a set of entities similar to entities controlled via edge-connected devices.

[0450] In some aspects, the techniques described herein relate to an AI-based platform, wherein a training data set includes historical energy storage data for a set of entities similar to entities controlled via edge-connected devices.

[0451] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one adaptive autonomous data processing system is further configured to adapt data transmission over a network and / or communication system, wherein the adaptation is based on at least one of the following: congestion conditions; latency and / or delay conditions; packet loss conditions; error rate conditions; transportation cost conditions; quality of service (QoS) conditions; usage conditions; market factor conditions; or user configuration conditions.

[0452] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin that represents at least one of the following: an energy stakeholder entity; an energy distribution resource; stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0453] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin configured to perform at least one of the following: provide visual and / or analytical metrics of energy consumption for at least one energy consumer, filter energy data; highlight energy data; or adjust energy data.

[0454] In some aspects, the techniques described herein relate to an AI-based platform, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption via at least one of the following: at least one machine; at least one factory; or at least one vehicle in a fleet.

[0455] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one adaptive autonomous data processing system is further configured to perform at least one of the following: extract energy-related data; detect and / or correct errors in energy-related data; transform, convert, normalize, and / or clean energy-related data; parse energy-related data; detect patterns, content, and / or objects in energy-related data; compress energy-related data; stream energy-related data; filter energy-related data; load and / or store energy-related data; route and / or transmit energy-related data; or maintain the security of energy-related data.

[0456] In some aspects, the techniques described herein relate to an AI-based platform, wherein an energy edge set is based on at least one common data resource, and the common data resource includes at least one of the following: weather data resource; satellite data resource; census, population, demographic, and / or psychographic data resource; market data resource; or e-commerce data resource.

[0457] In some aspects, the techniques described herein relate to an AI-based platform, wherein an energy edge set is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of the following: resource planning data; sales and / or marketing data; financial planning data; demand planning data; supply chain data; procurement data; pricing data; customer data; product data; or operations data.

[0458] In some aspects, the techniques described herein relate to an AI-based platform, further including at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on at least one of the following: at least one human label and / or annotation; at least one human interaction with a hardware and / or software system; at least one result; at least one AI-generated training data sample; supervised learning training process; semi-supervised learning training process; or deep learning training process.

[0459] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one adaptive autonomous data processing system is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: at least one fixed transmission line; at least one wireless energy transmission instance; at least one fuel delivery; or at least one stored energy delivery.

[0460] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one adaptive autonomous data processing system is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: an energy procurement and / or sales event; a service fee associated with the energy procurement and / or sales event; an energy consumption event; an energy generation event; an energy distribution event; an energy storage event; a carbon emission generation event; a carbon emission reduction event; a renewable energy credit event; a pollution generation event; or a pollution reduction event.

[0461] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one adaptive autonomous data processing system is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0462] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of adaptive autonomous data processing systems is further configured to perform additional training of the data processing systems based on an initial set of energy intelligence data on which the data processing systems were initially trained and additional energy intelligence data on which the data processing systems have not been trained.

[0463] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of adaptive autonomous data processing systems is further configured to instruct at least one edge-connected device in a set of edge-connected devices to adjust operation parameters associated with a set of distributed energy entities based on the identification of events and / or signals in a set of events and / or signals.

[0464] In some aspects, the techniques described herein relate to an AI-based platform, wherein a set of adaptive autonomous data processing systems is further configured to detect events and / or signals based on data collected from a set of edge-connected devices over a period of time, and the data processing systems are trained to identify a set of events and / or signals based on at least one characteristic of the period of time.

[0465] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of electricity and energy, including: a data integration module that integrates energy intelligence data collected from at least one internal edge device located within an environment and at least one external edge device located outside the environment.

[0466] In some aspects, the techniques described herein relate to an AI-based platform, wherein data collected from at least one of at least one internal edge device or at least one external edge device is vectorized.

[0467] In some aspects, the techniques described herein relate to an AI-based platform in which data collected from at least one of at least one internal edge device or at least one external edge device is stored in a distributed database.

[0468] In some aspects, the techniques described herein relate to an AI-based platform in which a data integration module is further configured to determine an energy pattern based on a localized energy pattern associated with data collected from at least one internal edge device and at least one external edge device.

[0469] In some aspects, the techniques described herein relate to an AI-based platform for implementing intelligent coordination and management of electricity and energy, including: a digital dynamic twin configured to model at least one of historical energy demand, current historical energy demand, or predicted energy demand; and an AI-based digital twin updater that updates the dynamic digital twin based on a set of energy parameters.

[0470] In some aspects, the techniques described herein relate to an AI-based platform in which the AI-based digital twin updater performs an update of the dynamic digital twin to determine a predicted energy demand for a future period of time and updates a prediction of the energy demand for the future period of time based on another AI model.

[0471] In some aspects, the techniques described herein relate to an AI-based platform in which the dynamic digital twin is associated with a device type and the AI-based digital twin updater analyzes data associated with the energy consumption of devices of the device type in order to update the dynamic digital twin to model the energy consumption of devices of the device type.

[0472] In some aspects, the techniques described herein relate to an AI-based platform in which the dynamic digital twin is further configured to model the energy demand of at least one entity, where the model is based on data indicating the energy consumption of the at least one entity.

[0473] In some aspects, the techniques described herein relate to an AI-based platform for implementing intelligent coordination and management of electricity and energy, including: an energy access arbiter that arbitrates access to at least one energy source by at least one energy-consuming device among a set of energy-consuming devices.

[0474] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of power and energy, including: a set of edge devices that communicate locally with at least one energy-consuming device to determine at least one energy consumption characteristic of the at least one energy-consuming device, wherein at least one edge device of the set of edge devices determines at least one energy consumption characteristic of the at least one energy-consuming device based on multiple perspectives associated with the energy consumption of the at least one energy-consuming device.

[0475] In some aspects, the techniques described herein relate to an AI-based platform further including an edge device monitoring system that monitors the energy consumption of at least one downstream device among the at least one energy-consuming device and implements an energy policy on the at least one downstream device based on the energy consumption.

[0476] In some aspects, the techniques described herein relate to an AI-based platform, wherein the energy policy is based on a generation mechanism that generates energy associated with the energy consumption.

[0477] In some aspects, the techniques described herein relate to an AI-based platform, wherein the edge device monitoring system is further configured to determine the carbon emissions associated with the energy consumption of the at least one downstream device.

[0478] In some aspects, the techniques described herein relate to an AI-based platform for enabling intelligent coordination and management of power and energy, including: a set of general artificial intelligence (AGI) agents, wherein each AGI agent is assigned to manage a set of energy generation, storage, and / or consumption workloads of a set of entities.

[0479] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one AGI agent of the set of AGI agents is further configured to adjust at least one parameter associated with the AI-based platform based on at least one interaction between the at least one AGI agent and at least one of a human, another AGI agent, or another component of the AI-based platform.

[0480] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one AGI agent of the set of AGI agents monitors the decisions of at least one other AGI agent of the set of AGI agents and adjusts at least one parameter associated with the AI-based platform based on the decisions of the at least one other AGI agent.

[0481] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one AGI agent among a set of AGI agents monitors energy-related data associated with at least one of the following: at least one interaction between at least one person and at least one component of the AI-based platform; at least one wildlife usage pattern; at least one space travel instance; at least one satellite; at least one asteroid mining operation; at least one banking system; at least one marketing operation; at least one radioactive waste disposal instance associated with at least one nuclear power plant; at least one cyberattack associated with at least one type of energy; at least one land clearing operation; at least one AI entity; or at least one robotic entity.

[0482] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one AGI agent among a set of AGI agents performs an adjustment of data associated with at least one of a data collection process, a data storage process, a data reporting process, or a data transmission process, and adapts to at least one of an anonymous request of a person associated with the data or a privacy request of a person associated with the data.

[0483] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one AGI agent among a set of AGI agents monitors the movement of at least one energy resource within a networked element and updates a policy associated with the at least one energy resource based on the movement.

[0484] In some aspects, the techniques described herein relate to an AI-based platform, wherein at least one AGI agent among a set of AGI agents updates energy allocation in response to movement to facilitate the energy availability of at least one energy resource. BRIEF DESCRIPTION OF THE DRAWINGS

[0485] The present disclosure will become more fully understood through the detailed description and the accompanying drawings.

[0486] Figure 1 is a schematic diagram showing an introduction of a platform and main elements according to some embodiments.

[0487] Figure 2A and Figure 2B is a schematic diagram showing an introduction of main subsystems of a main ecosystem according to some embodiments.

[0488] Figure 3 is a schematic diagram showing more detailed information about a distributed energy generation system according to some embodiments.

[0489] Figure 4 is a schematic diagram showing more detailed information about data resources according to some embodiments.

[0490] Figure 5 is a schematic diagram showing more details about the configured energy edge stakeholders according to some embodiments.

[0491] Figure 6 is a schematic diagram showing more details about the intelligent support system according to some embodiments.

[0492] Figure 7 is a schematic diagram showing more details about the AI-based energy coordination according to some embodiments.

[0493] Figure 8 is a schematic diagram showing more details about the configurable data and intelligence according to some embodiments.

[0494] Figure 9 is a schematic diagram showing the dual-process learning function of the dual-process artificial neural network according to some embodiments.

[0495] Figures 10 to 37 is a schematic diagram of an embodiment of a neural network system according to an embodiment of the present disclosure. The neural network system can be connected to, integrated into, and accessed by a platform to implement intelligent transactions, which include transactions involving expert systems, self-organization, machine learning, and artificial intelligence, and include neural network systems trained for pattern recognition, classification of one or more parameters, features, or phenomena, for supporting autonomous control, and for other purposes.

[0496] Figure 38 is a schematic diagram of an exemplary embodiment of a quantum computing service according to some embodiments of the present disclosure.

[0497] Figure 39 shows the quantum computing service request processing according to some embodiments of the present disclosure.

[0498] Figure 40 is a graphical view of the thalamus service according to the present disclosure and how it coordinates within a module.

[0499] Figure 41 is another graphical view of the thalamus service according to the present disclosure and its coordination within a module. Detailed Description

[0500] Figure 1 : Introduction of the Platform and Main Components

[0501] In an embodiment, an AI-based energy edge platform 102 is provided herein. For convenience, in some cases, the AI-based energy edge platform is referred to herein as platform 102 for short. It includes a set of systems, subsystems, applications, processes, methods, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other cooperating elements that work together to achieve intelligent, and in some cases autonomous or semi-autonomous, coordination and management of electricity and energy in various ecosystems and environments. These ecosystems and environments include distributed entities (referred to herein as "distributed energy resources" or "DER" in some cases) and other energy resources and systems that generate, store, consume, and / or transport energy, and include IoT, edge, and other devices and systems that process data related to DER and other energy resources and can be used to inform, analyze, control, optimize, predict, and otherwise assist in the coordination of distributed energy resources and other energy resources.

[0502] For example, distributed energy resources ("DER") can include (but are not limited to): wind turbines (including wind turbine farms), solar photovoltaics (PV), flexible and / or floating solar systems (including solar power plants), fuel cells (including fuel cells that burn natural gas and fuel cells that burn biomass), coal mines, oil wells, gas wells, modular nuclear reactors, nuclear batteries, modular hydroelectric power systems, microturbines and turbine arrays, reciprocating engines, gas turbines, cogeneration plants, biomass generators, municipal solid waste incinerators, battery energy storage (including chemical batteries, etc.), capacitive energy storage, geothermal energy systems, molten salt energy storage, electro-thermal energy storage (ETES), gravity-based energy storage, compressed fluid energy storage, pumped hydroelectric energy storage (PHES), liquid air energy storage (LAES), coal storage facilities, oil storage tanks, gas storage tanks, liquefied natural gas (LNG) storage tanks, physical energy storage systems such as flywheels, gravity batteries (e.g., substances suspended in a gravity well), fuel transport vehicles, fuel transport pipelines, wired power transmission systems, wireless power transmission systems, etc.

[0503] In an embodiment, platform 102 implements a set of configured stakeholder energy edge solutions 108, which have a wide range of functions, applications, capabilities, and uses that can be achieved by using or coordinating a set of advanced energy resources and systems 104 (including, but not limited to, DERs). This set of configured stakeholder energy edge solutions 108 can integrate, for example, domain-specific stakeholder data (e.g., proprietary data sets generated in relation to enterprise operations, analytics, and / or strategies), real-time data from stakeholder assets (e.g., data collected by IoT and edge devices located near stakeholder assets and operations), stakeholder-specific energy resources and systems 104 (e.g., available energy generation, storage, or distribution systems that can be located at stakeholder locations to augment or replace the power grid), etc. into solutions that meet the energy needs and capabilities of the stakeholders, including baseline, cycle, and peak energy demands for operations such as large-scale data processing, transportation, goods and material production, resource extraction and processing, heating, and cooling.

[0504] In an embodiment, platform 102 (and / or its elements) and / or the set of configured stakeholder energy advantage solutions 108 can obtain data from, provide data to, and / or exchange data with a set of data resources 110 for energy edge coordination. Platform 102 obtains information from a set of data resources 110 for energy edge coordination. These data resources may include data sets, from real-time energy consumption metrics to predictive analytics of future energy demands. By using these resources, platform 102 is able to make timely and informed decisions. Platform 102 is also equipped to provide data to a set of data resources 110 for energy edge coordination. Such data can include feedback on energy optimization strategies, insights derived from AI analytics, and / or even raw data collected from various sensors and nodes within the energy infrastructure. This feedback loop ensures that the data resources remain up-to-date, contributing to more accurate and dynamic energy management. Additionally, the set of configured stakeholder energy advantage solutions 108, customized to meet the unique needs of various stakeholders, can contribute data to and obtain insights from platform 102. For example, a stakeholder solution designed for a solar power plant can provide real-time data on the efficiency of solar panels, which platform 102 can then use to optimize energy distribution. This data exchange between platform 102, the set of configured stakeholder energy edge solutions 108, and the set of data resources 110 for energy edge coordination ensures that the optimization is based on the latest available data.

[0505] Platform 102 may include a set of intelligent support systems 112, a set of AI-based energy coordination, optimization, and automation systems 114, and a set of configurable data and intelligent modules and services 118, integrated with, exchanging data with, and / or otherwise linked to them. The set of intelligent support systems 112 serves as the cognitive center of platform 102. The set of intelligent support systems 112 utilizes advanced algorithms and computational tools to endow platform 102 with the necessary intelligence to parse large datasets, identify patterns, and make informed decisions. The set of AI-based energy coordination, optimization, and automation systems 114 ensures that platform 102 achieves efficiency and adaptability. By coordinating energy sources, optimizing energy flows, and automating processes, the set of AI-based energy coordination, optimization, and automation systems 114 transforms platform 102 into a dynamic entity that responds to real-time changes and proactively adopts strategies. The set of configurable data and intelligent modules and services 118 provides platform 102 with modular and customization flexibility. Depending on specific use cases, stakeholders can configure these modules to meet their unique needs.

[0506] The set of intelligent support systems 112 may include a set of intelligent data layers 130 for managing and processing information, a set of distributed ledger and smart contract systems 132 for ensuring secure and transparent transactions and data management, a set of adaptive energy digital twin systems 134 for creating virtual replicas of physical energy assets for better monitoring and optimization, and / or a set of energy simulation systems 136 for modeling potential energy scenarios to assist in decision-making. These integrated systems work together within the set of intelligent support systems 112 to provide a comprehensive solution for advanced energy management.

[0507] The set of AI-based energy coordination, optimization, and automation systems 114 may include a set of energy generation coordination systems 138 for managing and coordinating energy production sources, a set of energy consumption coordination systems 140 for supervising and optimizing how energy is used, a set of energy market coordination systems 146 for facilitating energy trading and transactions, a set of energy delivery coordination systems 147 for ensuring efficient and reliable energy distribution, and a set of energy storage coordination systems 142 for managing energy storage. These systems together provide an overall approach for coordinating the entire energy lifecycle.

[0508] The set of configurable data and intelligent modules and services 118 may include a set of energy trading support systems 144 for facilitating and streamlining energy-related transactions, a set of stakeholder energy digital twins 148 for providing virtual representations of stakeholder-specific energy assets for better monitoring and management, and a set of data integration microservices 150 that can support or contribute to supporting the set of stakeholder energy advantage solutions 108, thus ensuring an integrated approach to energy management.

[0509] Platform 102 may include one or more artificial intelligence (AI) systems, integrated with, linked to, exchanging data with, managed by, obtaining input from, and / or providing output to which. The AI systems may include models, rule-based systems, expert systems, neural networks, deep learning systems, supervised learning systems, robotic process automation systems, natural language processing systems, intelligent agent systems, self-optimizing and self-organizing systems, and other systems described in the present disclosure and in the documents incorporated herein by reference. Unless the context specifically indicates otherwise, references to AI or one or more examples of AI should be understood to cover these various alternative methods and systems; for example but not limited to, an AI system described as being used to support any one of the various functions, capabilities, and solutions described herein (e.g., optimization, autonomous operation, prediction, control, coordination, etc.) should be understood to be capable of being implemented by operation on a model or set of rules; by training on a training dataset of human labels, tags, etc.; by training on a training dataset of human interactions (e.g., human interactions with a software interface or a hardware system); by training on a training dataset of results; by training on an AI-generated training dataset (e.g., where the complete training dataset is generated by the AI from a seed training dataset); by supervised learning; by semi-supervised learning; by deep learning; such as this. For any given function or capability described herein, various types of neural networks may be used, including any type described in this document or in the documents incorporated by reference, and in embodiments, a set of hybrid neural networks may be selected such that within the set, the neural network types that are more conducive to implementing each element of a multi-functional or multi-capability system or method are realized. As an example among many examples, a deep learning or black box system may use a gated recurrent neural network to implement functions such as language translation of an intelligent agent, where as long as the user favorably perceives the result, there is no need to understand the underlying mechanism of the AI operation, while a more transparent model or system and a simpler neural network may be used for a system of automated governance, where it may be necessary to better understand how to transform the input into the output to comply with regulations or policies.

[0510] AI-based energy coordination, optimization, and automation system

[0511] In embodiments, platform 102 may employ demand forecasting, including automated forecasting through artificial intelligence or through a data stream that obtains forecasting information from a third party. In addition to this, forecasting demand helps to guide site selection and intelligent planning of network expansion. In embodiments, machine learning algorithms may generate multiple forecasts - for example, regarding weather, price, solar power generation, energy demand, and other factors - and analyze how energy assets can best capture or generate value at different times and / or locations.

[0512] In an embodiment, the AI-based energy coordination, optimization, and automation system 114 can achieve energy pattern optimization, for example, by analyzing building or other operational energy usage and seeking to reshape patterns for optimization (e.g., by modeling demand response to various stimuli). By analyzing energy consumption trends, the AI-based energy coordination, optimization, and automation system 114 can identify areas of waste or inefficiency. For example, these systems can evaluate how a building's energy consumption varies at different times of the day or in different seasons. Using this knowledge, the automation system 114 can then reshape these patterns to achieve optimal energy use. This can be applied in commercial office buildings, where the AI-based energy coordination, optimization, and automation system 114 can notice peaks in energy consumption in the early afternoon due to the simultaneous use of lighting, heating, and cooling systems. By modeling how the building might respond to certain stimuli, such as optimizing the heating, ventilation, and air conditioning (HVAC) system based on real-time occupancy data, the AI-based energy coordination, optimization, and automation system 114 can recommend measures to distribute energy consumption more evenly throughout the day, thereby reducing peak demand and associated costs.

[0513] The AI-based energy coordination, optimization, and automation system 114 can be implemented by a set of intelligent support systems 112 that provide functions and capabilities to support a range of applications and use cases.

[0514] In an embodiment, the platform 102 can be configured to integrate data from at least one internal edge device located within the environment (e.g., sensors within a building, vehicle, machine, utility) and at least one external edge device located outside the environment (e.g., sensors on a weather monitoring station, vehicle, etc. that broadcast real-time data). The platform 102 can collect real-time energy intelligence data and provide the real-time energy intelligence data to an intelligent circuit that is trained on the data and results and automatically performs actions to optimize energy management. For example, edge devices connected to DERs can be combined with edge devices from a local weather monitoring station. Local weather data (e.g., cloud cover, temperature, wind, rainfall, etc.) can be associated with the energy output from DERs, and a machine learning model can be trained to predict actions associated with the environment of the first edge device using variables from the second edge device. By another example, radar features output by a weather station edge device can be used to ramp up or ramp down the energy from DERs.

[0515] In an embodiment, data output from one or more edge devices can be vectorized and / or stored in a distributed database. By using vector-based data updates, capturing energy data from devices can be further optimized, where only the changes that affect the consumption information model are transmitted. The vector can be developed based on the analysis of data from the consumption devices mentioned above. The vector of the composite energy consumption system can be a multi-dimensional vector representing consumption type, purpose, device, etc., to form an efficient way to transmit complex energy usage environments. For example, consider a smart grid system where thousands of household appliances, HVAC systems, and lighting solutions continuously send energy consumption data. Instead of sending every minute of detail, the system analyzes this data and develops a vector based on consumption patterns. The vector, especially for the composite energy consumption system, can include various parameters such as consumption type, consumption purpose, energy consumption of specific devices, etc.

[0516] In an embodiment, energy usage patterns can include local patterns, such as based on a consumer's daily work schedule. However, energy usage patterns may be based on broader data, including weather forecast data; energy consumption in areas currently affected by a weather system for preparing areas predicted to receive the weather system; and so on. Pattern analysis can include not only raw usage but also information about the consumer (e.g., energy-consuming devices that are running) that may affect learning. For example, a consumer's daily work schedule can be a local pattern that the system can identify and adapt to, and this schedule can involve turning off all household appliances during working hours and increasing energy consumption at night.

[0517] Demographics and other human activities may play a role in energy pattern analysis. In one example, demographics in areas where it is suggested that consumers replace old cars with new cars more frequently than in other areas may indicate that the local energy demand for electric vehicle charging may increase more rapidly in these areas. When demographics and / or consumer behavior indicate that consumers in an area tend to replace vehicles with used cars, then the maintenance of traditional energy may be indicated as the preference for these areas.

[0518] Subsystems and modules of the intelligent support system

[0519] Intelligent data layer

[0520] A set of intelligent support systems 112 may include a set of intelligent data layers 130, e.g., a set of services (including microservices), APIs, interfaces, modules, applications, programs, etc., which may consume any of the data entities and types described in this disclosure and undertake a wide range of processing functions, e.g., extraction, cleaning, normalization, calculation, transformation, loading, batching, streaming, filtering, routing, parsing, conversion, pattern recognition, content recognition, object recognition, etc. Through a set of interfaces, users of the platform 102 may configure a set of intelligent data layers 130 or their outputs to meet internal platform requirements and / or support further configuration, e.g., for a set of configured stakeholder energy edge solutions 108. A set of intelligent data layers 130, more generally a set of intelligent support systems 112 and / or configurable data and intelligent modules and services 118 may access data from various sources across the platform 102 and, in embodiments, may operate from this set of shared data resources, which may be contained in a centralized database and / or a set of distributed databases or may consist of a set of distributed or decentralized data sources, e.g., IoT or edge devices producing energy-related event logs or streams. A set of intelligent data layers 130 may be configured for a wide range of energy-related tasks, e.g., prediction / forecasting of energy consumption, generation, storage, or distribution parameters (e.g., at the level of a single device, subsystem, system, machine, or fleet); optimization of energy generation, storage, distribution, or consumption (also at different optimization levels); automatic discovery, configuration, and / or execution of energy transactions (including spot and futures markets and microtransactions and / or larger transactions in peer-to-peer groups or single counterparty transactions); monitoring and tracking of parameters and attributes of energy consumption, generation, distribution, and / or storage (e.g., baseline levels, volatility, periodic patterns, incidental events, peak levels, etc.); monitoring and tracking of energy-related parameters and attributes (e.g., pollution, carbon production, renewable energy quotas, waste heat production, etc.); automatic generation of energy-related alerts, recommendations, and other content (e.g., messaging to prompt or facilitate favorable user behavior); and the like.

[0521] In an embodiment, platform 102 may be configured to analyze a monitored energy dataset and generate configuration recommendations for energy generation and consumption in a distributed system. Platform 102 may be configured to analyze streams from one or more local power consumption entities and generate recommendations. For example, a manufacturing plant may have a very different set of requirements than a hospital campus. In this way, an AI-based platform can analyze each of multiple energy consumption scenarios, as well as the associated equipment and requirements, and recommend the types of DERs for providing energy and regulating the energy corresponding to the needs and demands of local power consumption entities. The ER of a hospital may have a specific set of requirements, such as the hours during which an operating room is open, or emergency requirements based on emergencies. Examples of monitored energy datasets may include one or more grid-based energy resources and mobile energy resources. Grid-based energy resources may include, for example, fossil fuel-based energy production facilities (coal, oil, natural gas, etc.), renewable energy-based production facilities (solar farms, wind farms, geothermal generators, tidal generators, hydroelectric facilities, etc.). Mobile energy resources may include, for example, mobile battery devices, mobile fossil fuel generators, mobile renewable energy producers, mobile transformers and power conditioning systems, drone-based power delivery / storage systems, vehicle-based power delivery / storage systems, etc.

[0522] Distributed ledger and smart contract system

[0523] A set of intelligent support systems 112 may include a smart contract system 132 for processing a set of smart contracts, each of which may optionally operate on a set of blockchain-based distributed ledgers. Each smart contract in the set of smart contracts may operate on data stored in the set of distributed ledgers or blockchains, for example, to record energy-related transaction events, such as energy purchases and sales (in spot, forward, and peer-to-peer markets, as well as direct counterparty transactions), associated service fees, etc.; transaction-related energy events, such as consumption, generation, distribution, and / or storage events, and other transaction-related events generally related to energy, such as carbon generation or reduction events, renewable energy credit events, pollution generation or reduction events, etc. The set of smart contracts processed by the smart contract system 132 may consume any of the data types and entities described in this disclosure as a set of inputs, perform a set of computations (optionally configured in a flow for obtaining inputs from different systems in a multi-step transaction), and provide a set of outputs that enable the completion of transactions, reporting (optionally recorded on a set of distributed ledgers), etc. A set of energy transaction support systems 144 may be supported or enhanced by artificial intelligence, including autonomously discovering, configuring, and executing transactions according to policies and / or providing automation or semi-automation of transactions based on the training and / or supervision of a set of transaction experts.

[0524] In an embodiment, the smart contract system 132 can be used by a set of energy trading support systems 144 (described elsewhere in this disclosure) to configure trading solutions. Each smart contract within the smart contract system 132 is intricately designed to process data stored within these distributed ledgers or blockchains. The functionality of the smart contracts extends to recording various energy-related transaction events. This includes, but is not limited to, recording peer-to-peer energy transactions and even direct transactions between parties. Additionally, data related to energy events associated with service fees and other transactions are captured, including information on energy consumption, generation, distribution, and storage. For example, a city's energy grid has integrated renewable energy sources, such as solar and wind energy. The smart contract system 132 can autonomously execute contracts to purchase solar energy during peak sunlight hours and wind energy during windy periods. At the same time, every transaction, the associated service fees, and even the carbon offsets achieved through the use of renewable resources are recorded.

[0525] Adaptive Energy Digital Twin System

[0526] In an embodiment, any entity, analysis result, output of artificial intelligence, state, operating condition, or other feature mentioned in this disclosure can be presented in a digital twin, such as a widely applicable set of adaptive energy digital twin systems 134 and / or a set of stakeholder energy digital twins 148, which are configured for the needs of a specific stakeholder or stakeholder solution. A set of adaptive energy digital twin systems 134 can, for example, provide visual or analytical metrics of energy consumption for a set of machines, a set of factories, a fleet, etc.; subsets thereof (e.g., comparing the energy parameters of each machine in a set of similar machines to identify out-of-range behavior); and many other aspects. The digital twin can be adaptive, for example, filtering, highlighting, or otherwise adjusting the presented data based on real-time conditions (such as changes in energy costs, changes in operating behavior, etc.).

[0527] In an embodiment, platform 102 may be configured to create, manage, and / or otherwise provide a dynamic digital twin based on the historical, current, and predicted distributed energy demands of mobile and stationary entities within a domain. For example, relatively large corporate or organizational environments may be modeled via digital twins, such as industrial environments, factory environments, distribution centers, hospital environments, university / college environments, office building environments, mining operations, etc. In a specific example, for a manufacturing facility with many machines, assembly lines, and automation systems, platform 102 may create a digital twin of the environment, capturing every detail of its energy consumption patterns. Such a digital twin may provide real-time information about the factory's energy demands, from the historical energy usage data of each machine to the current consumption rate, and even predict future energy demands based on a predicted production schedule. Larger environments may be modeled, where costs may be significantly shifted based on energy adjustments across the entire environment. For example, in a large environment with high energy consumption, even small adjustments can result in significant financial impacts. By having a dynamic digital twin, stakeholders can simulate various energy adjustments and analyze their impacts. For example, in an office building environment, adjusting the operation of the HVAC system based on real-time occupancy data or optimizing lighting based on natural daylight availability can significantly shift energy costs.

[0528] In an embodiment, platform 102 may be configured to model government entities via one or more digital twins, such as states, counties, cities, towns, development zones, communities, etc. In one example, for a city with thousands or hundreds of thousands of residents, businesses, public transportation systems, and numerous facilities, platform 102 may create a digital twin of such a city, capturing every aspect of its energy consumption. Such a digital representation may include everything from the lighting of public parks, the HVAC systems of government buildings, to the energy demands of public transportation systems. By doing so, platform 102 provides city managers with an overall view of the city's energy footprint, helping to make informed decisions regarding energy management. Platform 102 may even model larger entities, such as states or counties, capturing the different energy demands of various regions, from urban centers to rural areas. On the other hand, platform 102 may also represent smaller entities, such as towns. For example, in a new town being developed for industrial use, platform 102 may model the expected energy demands based on the planned industries, ensuring that the energy infrastructure is fully prepared to meet the demands. In another example, a county planning to transition to renewable energy may utilize its digital twin to simulate the impact of integrating solar farms or wind turbines. Such simulations may provide insights into potential energy savings, grid stability, and even the environmental benefits of such a transition.

[0529] In an embodiment, platform 102 may include an AI-based system for updating a digital twin based on a set of energy parameters, which may include adapting energy consumption data for the digital twin from a physical device based on the set of energy parameters, e.g., by adjusting the cost resulting from energy consumption through a dynamic energy market that supplies energy to the device. For example, consider a device that obtains its energy from a dynamic energy market, where the energy cost fluctuates based on demand, supply, and other market factors. If the device consumes energy when the cost is high, the AI-based system can adjust the digital twin to reflect this, ensuring that the virtual representation accurately reflects the financial impact of real-world energy consumption. When updating the device, the AI-based system can also incorporate the energy source preferences of the user of the device (optionally, as expressed in the device digital twin). For example, if the user expresses a preference for green energy through the digital twin of their device, the AI system ensures that this preference is incorporated into the energy consumption data update. For shared devices (e.g., electric bicycles), the energy consumed during and / or associated with the sharing of the device by the users of the device (when the electric bicycle is checked out in the user's account) can be allocated to / across specific energy sources based on the user profile. For example, when a user checks out an electric bicycle on their user account, the energy consumed during their use can specifically come from their preferred energy source, as detailed in their user profile associated with the user account. Additionally or alternatively, the owner of the device and / or the digital twin can identify the allocation of the consumed energy to be assigned to each of multiple energy sources. For example, there may be a situation where the owner of the device has a specific allocation for the energy consumed across multiple energy sources. In such a case, the AI system ensures that the digital twin accurately reflects this allocation. For example, the owner can specify that 50% of the energy consumed by the device should come from wind energy and the remaining 50% from water energy. When updating the digital twin, the AI system can ensure that this allocation is accurately represented. Thus, platform 102 with an AI-based system provides a digital twin that is not just a static representation, but is dynamic, responsive, and tailored to individual preferences and real-world scenarios.

[0530] In an embodiment, an AI-based system for updating a digital twin based on a set of energy parameters may include adapting energy production and / or distribution control based on the set of energy parameters for an upcoming time period (e.g., during an upcoming high-demand event, etc.). This may include relying on AI-based energy demand forecasting for a future period of time to adjust how the energy supply system operates, e.g., determining energy parameters such as how much energy to store versus how much to generate and deliver. For example, in the presence of an anticipated high-demand event, perhaps due to a holiday, the AI-based system can predict such a demand spike by analyzing the energy parameters and accordingly adapt energy production and / or distribution control. In another example, based on past data and current trends, the AI-based system can predict increased energy demand during summer months. In addition to AI-based energy demand forecasting, the AI-based system can evaluate macro trends / activities based on the energy parameters. In one example, an AI-based system for updating an energy consumption system can detect pricing patterns indicating that energy costs may increase sharply (e.g., due to a major weather event, etc.), and the set of energy parameters can guide the AI-based system to adapt energy consumption and / or storage guidance for at least selected consumers (e.g., public systems (e.g., tax-based systems) to avoid unnecessary burdens on taxpayers). For example, if the AI-based system detects a pattern indicating that energy costs may increase due to an upcoming major weather event, preemptive measures can be taken. By analyzing the set of energy parameters, the AI-based system can guide certain consumers to adapt their energy consumption or storage patterns, or guide public systems to reduce consumption or increase storage. Thus, platform 102 with the AI-based system ensures that energy management is proactive and efficient.

[0531] In an embodiment, platform 102 may be configured to provide and / or facilitate digital twins of common device types (e.g., electric bicycles of the same model). The digital twins may exchange consumption data across a range of usage instances to understand how such common device types consume energy in different environments, at different times of day, different geographical locations, and user demographics (including demographics local to the point of use). For example, an electric bicycle primarily used in hilly terrain may exhibit different energy consumption patterns compared to an electric bicycle used in a flat urban environment. By aggregating data from various digital twins, platform 102 can identify these patterns and make informed predictions. This can enable a digital twin of a specific device (a specific electric bicycle) to better predict energy demand, resulting in, for example, a dynamic charging profile. Some devices may be located in high-demand areas, indicating a need for more frequent charging, while other devices may allow for a lower average energy cost due to, for example, shorter and less frequent use. For example, an electric bicycle located in a busy city center may be identified as requiring frequent charging due to high demand; on the other hand, another electric bicycle located in an area where it is not used frequently may operate well even with infrequent charging. This can also allow for the aggregation of demand profiles across a range of geographical areas to identify needs such as, for example, charging demand, available energy, etc. For instance, in areas where electric bicycles are highly concentrated (e.g.), platform 102 may recommend staggered charging schedules to balance demand and prevent grid overload. This can result in the management of electric bicycle charging activities, including demand balancing with other charging devices within an area.

[0532] In an embodiment, the platform 102 can be configured such that not every physical instance of a device (e.g., a particular model of electric bicycle) needs to have its own permanent digital twin. The dormant time of most such devices is much longer than their usage time (the duty cycle is very low), so even the energy requirements to support digital twin processing for such devices can be managed according to the demand curve. Instances of physical devices (or configured genetic instances) can be activated (energy resources can be allocated) based on demand prediction. Consider the scenario of a particular model of electric bicycle. Although these electric bicycles may be scattered in different locations and can be used at any time, their actual usage or "duty cycle" may not be frequent, and these devices are dormant for long periods. Understanding this unique feature, the platform 102 is configured in such a way that the platform 102 can activate digital twins for these devices based on predicted demand, rather than maintaining continuous digital twins for each electric bicycle. For example, in an urban environment, if the platform 102 predicts a surge in demand for electric bicycles during, for example, the morning rush hour, the digital twins of the electric bicycles can be activated during that period. These digital twins can facilitate energy management, ensuring that the electric bicycles are fully charged and ready for use. After the peak period, these digital twins can be deactivated to save processing energy. This demand-driven approach ensures that the energy resources for processing digital twins are optimally utilized.

[0533] In an embodiment, the platform 102 can provide and / or facilitate the sharing, exchange, and / or aggregation of energy consumption data provided by physical device instances to digital twins. This energy consumption data can be collected to establish a set of energy demand parameters for use in predicting an energy demand model, etc. For example, the platform 102 is designed to facilitate the exchange and aggregation of energy consumption data from various physical device instances and direct it to their corresponding digital twins. For instance, consider a community with multiple smart homes, each equipped with multiple smart devices. Although each household may have its unique energy consumption pattern, the collective data from all these households can reveal broader trends. By aggregating this data, the platform 102 can identify patterns, such as increased energy consumption during holidays or decreased demand during holidays. These insights can then inform the prediction model, ensuring that energy providers are well-prepared to meet the expected demand.

[0534] In an embodiment, the platform 102 may be configured such that the energy consumption data provided to the digital twin may also contribute to predicting energy-related demands, such as, for example, the maintenance of energy supply infrastructure. For example, the need to address energy production waste can be better predicted not only based on consumption but also on the supply sources available to the digital twin. In other words, physical devices not only consume energy but also must be supplied with energy (or must generate energy themselves). The digital twin can use energy supply and / or sources to indicate the time / region / specific source (waste removal, refurbishment, etc.) that supports energy production. For example, if a local energy production facility mainly relies on non-renewable resources, higher associated waste will be generated. By predicting this, the digital twin can ensure that appropriate waste management measures are in place. Additionally, the digital twin of a local energy production facility can utilize the predicted demand from the energy consumption digital twin to address not only production issues but also upstream procurement issues. For example, if the predicted demand for the utilization of electric bicycles for an upcoming event (graduation, birthday, etc.) can be predicted together with, for example, the expected availability of energy generated by solar power, the local energy supply station can supply upstream energy only when needed and / or as required. For example, if the solar prediction is favorable, the supply station can mainly rely on solar power; otherwise, the supply station can obtain energy from an upstream energy supplier to meet the demand.

[0535] Energy simulation system

[0536] In an embodiment, a set of energy simulation systems 136 is provided, for example, for developing and evaluating detailed simulations of energy generation, demand response, and charge management, including a simulation environment that simulates the results of using various algorithms that can manage the generation of various power generation assets, the consumption of devices and systems that require energy, and the storage of energy. Data can be used to simulate the interaction of uncontrollable loads and optimize the charging process, as well as other use cases. The simulation environment can provide output to, integrate with, or share data with the set of adaptive energy digital twin systems 134. For example, if a city plans to transition to renewable energy, the city can use a set of energy simulation systems 136 to simulate various outcomes. Such simulations can predict how solar panels respond to changing weather conditions, how wind turbines operate in different seasons, or how energy storage solutions need to be managed during peak demand periods.

[0537] In embodiments, as more and more enterprises adopt hybrid infrastructure, uptime becomes more complex and requires backup and failover strategies across cloud, colocation, on-premises, and edge infrastructure. This can include AI-based algorithms for automatically managing the energy of devices and systems within such devices. For example, artificial intelligence can support autonomous data center cooling and industrial control. In embodiments, distributed energy or DER 128 can be integrated into or with, for example, AI-driven computing infrastructure, intelligent power distribution units (PDUs), uninterruptible power supply (UPS) systems, energy-supported airflow management systems, and HVAC systems. By simulating energy scenarios, the group of energy simulation systems 136 ensures that enterprises (regardless of their infrastructure models) operate seamlessly and sustainably.

[0538] Introduction to the main subsystems and modules of an AI-based energy coordination, optimization, and automation system

[0539] A group of AI-based energy coordination, optimization, and automation systems 114 can include a group of energy generation coordination systems 138, a group of energy consumption coordination systems 140, a group of energy storage coordination systems 142, a group of energy market coordination systems 146, and a group of energy delivery coordination systems 147, etc. For example, a group of energy delivery coordination systems 147 can support the coordination of delivering energy to points of consumption such as through fixed transmission lines, wireless energy transfer, fuel delivery, delivery of stored energy (e.g., chemical or nuclear batteries), and can involve autonomously optimizing the mix of energy types among the aforementioned available resources based on various factors, such as the consumption location (e.g., based on the distance from the power grid), purpose or type (e.g., whether very high peak energy delivery is required, e.g., for power-intensive production processes), etc. Consider a remote industrial unit far from the main power grid whose production process requires electricity. The group of energy generation coordination systems 138 can analyze the location and determine that connecting such a unit to the main power grid may not be feasible. Instead, the group of energy generation coordination systems 138 can suggest that a combination of wireless energy transfer and chemical battery delivery may be most suitable in this case.

[0540] Configurable data and intelligent modules and services

[0541] In an embodiment, platform 102 may include a set of configurable data and intelligent modules and services 118. These may include a set of energy trading support systems 144, a set of stakeholder energy digital twins 148, a set of data integration microservices 150, etc. Each module or service (optionally configured in a microservices architecture) may exchange data with various data resources to provide relevant outputs, e.g., a set of internal functions or capabilities that support platform 102 and / or a set of functions or capabilities that support one or more of a set of configured stakeholder energy edge solutions 108. As an example among many, a service may be configured to obtain event data from an IoT device having a camera or sensor monitoring a generator and integrate it with weather data from a public data resource 162 to provide a weather-related timeline of the generator's energy generation data, which in turn may be consumed by a set of configured stakeholder energy edge solutions 108, e.g., to assist with the generator's day-ahead energy generation based on a day-ahead weather forecast. Platform 102 may support a wide range of such configured data and intelligent modules and services 118, representing various outputs such as the fusion or combination of a wide range of energy edge data sources processed by the platform, higher-level analysis outputs resulting from expert analysis of the data, forecasts and predictions based on data patterns, automation and control outputs, etc.

[0542] In an embodiment, platform 102 may be configured such that energy-consuming devices and / or systems (e.g., a set of energy-consuming devices in a home) may locally arbitrate access to energy sources, e.g., mains energy, first-level stored energy (e.g., at the device), local stored energy (e.g., a local battery that may supply energy to multiple devices), etc. Additionally, the devices may consume energy for various purposes such as consumption, storage, balancing sources, acting as an agent for other devices, etc. Further, the energy-consuming devices may be configured / can be configured to use multiple energy types, e.g., the grid, solar, geothermal, fossil fuel (internal combustion engine), hydrogen, etc. Additionally, in an energy-consuming system (a set of devices as described above), energy consumption may span a range of energies (e.g., hydrogen for cooking, solar for energy storage, waste energy recovery, etc.). By way of example, consider a home equipped with multiple energy-consuming devices, each with its unique energy requirements and preferences. Platform 102 may facilitate a dynamic environment in which these devices may locally arbitrate access to various energies based on their immediate needs and available resources. For example, on a sunny day, the solar panels in the house may generate excess energy, in which case platform 102 may primarily utilize the energy from the solar panels, reducing the energy consumption from the grid.

[0543] In an embodiment, the platform 102 can capture energy consumption information from / via edge devices and develop a dataset representing multiple viewpoints regarding the consumed energy. Edge devices that can communicate with (e.g., locally or very close to) a range of energy consumption devices and device types can collect data about the devices, including, for example, which sources the devices can consume, which source the devices consumed, the purpose / use of the consumed energy, etc. Further examples can include situations where it appears as if the devices perform any kind of optimization, such as leveraging local storage during high energy costs (including high transmission costs that may be measured based on delivery efficiency, etc.), consuming energy during off-peak hours to replenish storage, and / or leveraging low-cost sources (e.g., solar energy) when readily available. A wide variety of analytics, etc. can be generated, captured, and used in the energy management system. For example, consider a smart plug connected to a refrigerator that can provide insights into its energy consumption patterns, revealing details such as its preference for leveraging local storage during high energy costs. By aggregating this data from various edge devices, the platform 102 can identify patterns, predict future energy demands, and optimize energy consumption across devices.

[0544] Energy trading support system

[0545] The configurable data and intelligent modules and services 118 can include a set of energy trading support systems 144. This set of energy trading support systems 144 can include a set of smart contracts that can operate on data stored in this set of distributed ledgers or blockchains, for example, to record energy-related transaction events such as energy purchases and sales (in spot, forward, and peer-to-peer markets, as well as direct counterparty transactions), associated service fees; transaction-related energy events such as consumption, generation, distribution, and / or storage events, and other transaction-related events typically related to energy such as carbon generation or emission reduction events, renewable energy credit events, pollution generation or emission reduction events, etc. This set of smart contracts can consume any data types and entities among the data types and entities described in this disclosure as a set of inputs, undertake a set of computations (optionally configured in a flow that obtains inputs from different systems in a multi-step transaction), and provide a set of outputs that enable the completion of transactions, reporting (optionally recorded on a set of distributed ledgers), etc. This set of energy trading support systems 144 can be supported or enhanced by artificial intelligence, including autonomously discovering, configuring, and executing transactions according to policies and / or providing automation or semi-automation of transactions based on the training and / or supervision of a set of trading experts. Autonomy and / or automation (supervised or semi-supervised) can be achieved through robotic process automation, for example, by training a set of intelligent agents to achieve transaction discovery, configuration, or execution interactions between a set of trading experts and trading support systems (e.g., software systems for configuring and executing energy trading activities).

[0546] As more and more energy is produced and consumed in local decentralized markets, the energy market is likely to follow the patterns of other peer-to-peer or sharing economy markets, such as carpooling, co-living apartments, and second-hand goods markets. Technology can bypass top-down or centralized energy supply and enable operators to create platforms that can manage and monetize idle capacity, for example, through the leasing and trading of assets and outputs.

[0547] As more distributed or peer-to-peer trading energy markets develop, Platform 102 can include systems, or be linked to, integrated with, or support other platforms to facilitate P2P trading, wholesale contracts, renewable energy certificate (REC) tracking, and broader distributed energy supply, payment management, and other trading elements. In an embodiment, the foregoing can use blockchain, distributed ledger, and / or smart contract system 132. For example, a homeowner with excess solar energy may decide to sell this excess energy. This transaction is securely recorded on the blockchain.

[0548] In an embodiment, with increased transparency, choice, and flexibility, consumers will be able to actively participate in the energy market by generating, storing, selling, and consuming electricity. For example, a local community may decide to utilize its collective solar power generation. Platform 102 enables households with solar panels to exchange excess energy with households without solar panels, ensuring that the entire community benefits.

[0549] In an embodiment, the trading elements can be configured by the energy trading support system 144 to optimize energy generation, storage, or consumption, such as when utilities use time-of-use charges. Using IoT-based platforms, energy demand can be shifted from high-cost periods, and these platforms can identify the periods when energy costs are cheapest. For example, in areas where utility charges vary according to the time of use, Platform 102 can shift energy demand to periods when energy is cheaper. In one example, smart home devices linked to Platform 102 can identify the periods with the lowest energy costs and adjust their operations accordingly, thus ensuring efficient and cost-effective energy consumption.

[0550] Stakeholder Energy Digital Twin

[0551] The configurable data and intelligent modules and services 118 can include a set of stakeholder energy digital twins 148, which in embodiments can include a set of digital twins configured to represent a set of energy-related stakeholder entities, including energy generation resources owned and operated by stakeholders, energy distribution resources, and / or energy distribution resources (including representing these resources by type, e.g., indicating renewable energy systems, carbon production systems, etc.); stakeholder information technology and network infrastructure entities (e.g., edge and IoT devices and systems, network systems, data centers, cloud data systems, internal information technology systems, etc.); energy-intensive stakeholder production facilities, e.g., machines and systems used in manufacturing; stakeholder transportation systems; market conditions (e.g., related to current and forward market pricing of energy, stakeholders' supply chains, stakeholders' products and services, etc.), and so on. This set of stakeholder energy digital twins 148 can provide real-time information about status, operating conditions, etc., e.g., sensor data, event logs, and other information flows provided from IoT and edge devices, particularly related to energy consumption, generation, storage, and / or distribution.

[0552] This set of stakeholder energy digital twins 148 can provide a visual, real-time view of the impact of energy on all aspects of an enterprise. The digital twins can be role-based, e.g., providing visual and analytical metrics suitable for the user role, such as financial reporting information for a Chief Financial Officer (CFO); operating parameter information for a power plant manager; and energy market information for an energy trader. For example, a CFO may need a visual representation highlighting the financial cost of energy consumption, e.g., how shifting operations to off-peak hours would affect energy costs. In contrast, a power plant manager may be more interested in operating parameters, e.g., the efficiency of power generation resources. On another aspect, an energy trader may want to understand the energy market, e.g., tracking prices. Thus, by providing insights suitable for an individual's role, a set of stakeholder energy digital twins 148 ensures that different stakeholders have the relevant information they need to make informed decisions.

[0553] Data integration microservices

[0554] The configurable data and intelligent modules and services 118 can include a set of data integration microservices 150, e.g., organized in a service-oriented architecture such that the various microservices can be chained, run in parallel, or grouped in more complex flows to create higher-level, more complex services, each of which provides a defined set of outputs by processing a defined set of inputs in order to support a set of configured specific stakeholder energy edge solutions 108 or to facilitate an AI-based coordination, optimization, and / or automation system 114. The configurable data and intelligent modules and services 118 can be configured (but not limited to) by the various functions and capabilities of a set of intelligent data layers 130, which in turn operate on the internal event logs, outputs, data streams, etc. of the data resources 110 and / or the platform 102 for various energy edge coordination.

[0555] Figures 2A to 2B : Introduction to the major subsystems of the major ecosystem components

[0556] Data resources for energy edge coordination

[0557] Reference Figure 2A , the data resources 110 for energy edge coordination can include a set of edge and IoT network systems 160, public data resources 162, and / or a set of enterprise data resources 168, which in an embodiment can use or be supported by an adaptive energy data pipeline 164 that automatically handles data processing, filtering, compression, storage, routing, transmission, error correction, security, extraction, transformation, loading, normalization, cleaning, and / or other data processing capabilities involved in transmitting data over a network or communication system. This can include adjusting one or more of these aspects of data processing based on data content (e.g., by packet inspection or other mechanisms for understanding packets), network conditions (e.g., congestion, latency / delay, packet loss, error rate, transport cost, quality of service (QoS), etc.), usage context (e.g., based on user, system, use case, application, etc., including based on its priority), market factors (e.g., price or cost factors), user configuration, or other factors and based on their various combinations. For example, among many other routes, the lowest cost route can be automatically selected for data related to the management of low-priority uses of energy, e.g., heating a swimming pool, while the fastest or highest QoS route can be selected for data supporting priority uses or energy, e.g., supporting critical healthcare infrastructure.

[0558] Reference Figure 2B, the platform 102 and coordination can include, integrate, link to, integrate into, use, create, or otherwise process extensive data resources for advanced energy resources and systems 104, a set of configured stakeholder energy edge solutions 108, and / or energy edge coordination 110. In an embodiment, the elements of the advanced energy resources and systems 104, a set of configured stakeholder energy edge solutions 108, and / or energy edge coordination 110 can be the same as, similar to, or different from the corresponding elements shown in Figure 1 . The data resources can include individual databases, distributed databases, and / or federated data resources, etc.

[0559] Edge and IoT Network Systems

[0560] can collect and process extensive energy-related data (including through artificial intelligence services and other capabilities), and can process control instructions by a set of edge and IoT network systems 160. For example, network systems integrated into devices, components, or systems, network systems located in IoT devices and systems, network systems located in edge devices and systems, etc. For example, the foregoing network systems are located in or around energy-related entities, such as network systems used by consumers or enterprises, such as network systems involved in energy generation, storage, transmission, or use. These include any of the extensive software, data, and network systems described herein.

[0561] Public Data Resources

[0562] In an embodiment, the platform 102 can track public data resources 162, such as weather data. Weather conditions affect energy use, especially when the weather conditions are related to HVAC systems. Collecting, compiling, and analyzing weather data related to other building information enables building managers to proactively understand HVAC energy consumption. The public data resources 162 can include satellite data, demographic and psychographic data, population data, census data, market data, website data, e-commerce data, and many other types of data.

[0563] Enterprise Data Resources

[0564] A set of enterprise data resources 168 can include extensive enterprise resources, such as enterprise resource planning data, sales and marketing data, financial planning data, accounting data, tax data, customer relationship management data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, operation data, etc.

[0565] Subsystems and Modules of Advanced Energy Resources and Systems

[0566] In an embodiment, the advanced energy resources and systems 104 can include distributed energy resources or "DERs" 128. More decentralized energy resources would mean that more individuals, network groups, and energy communities would be able to generate and share their own energy and coordinate the systems for ultimate efficiency. The DERs 128 can be small-scale or medium-scale power generation and / or storage units that operate locally and can be connected to a larger power grid at the distribution level. For example, the DER 128 can be connected to a local power grid or isolated from the grid in an independent application.

[0567] Converted energy infrastructure

[0568] The advanced energy resources and systems 104 coordinated by the platform 102 can include a set of converted energy infrastructure systems 120. The energy edge will involve an increasing degree of digitization of power generation, transmission, substations, and distribution assets, which in turn will affect the operation, maintenance, and expansion of traditional grid infrastructure. In an embodiment, a set of converted energy infrastructure systems 120 can be integrated with or linked to the platform 102. The transition to improved infrastructure may involve moving from SCADA systems and other existing control, automation, and monitoring systems to an IoT platform with advanced capabilities.

[0569] In an embodiment, new assets added to or coordinated with the power grid (e.g., DER 128) can be compatible with the existing infrastructure to maintain voltage, frequency, and phase synchronization. For example, consider a city that incorporates renewable energy sources such as wind turbines and solar panels (DER 128) into its existing power grid. These new assets need to be integrated with the old infrastructure to ensure consistent power delivery. This compatibility ensures that even as the city transitions to a greener energy source, residents do not experience fluctuations in voltage, frequency, or phase synchronization, thus ensuring a stable power supply.

[0570] In an embodiment, any improvements to traditional grid assets, new grid-connected equipment, and support systems can comply with regulatory standards from NERC, FERC, NIST, and other relevant agencies; positively impact the reliability of the power grid; reduce the vulnerability of the power grid to cyberattacks and other security threats; increase the power grid's ability to accommodate widespread two-way energy flow (i.e., DER proliferation); and provide interoperability with technologies that improve grid efficiency (i.e., by enabling and facilitating demand response, reducing grid congestion, etc.).

[0571] The digitization of traditional grid assets can involve assets used for power generation, transmission, storage, distribution, etc., including power stations, substations, transmission lines, etc.

[0572] In an embodiment, to maintain and improve existing energy infrastructure, platform 102 may include various capabilities, including fully integrated predictive maintenance across assets owned by utility companies (i.e., generation, transmission, substations, and distribution); intelligent (AI / ML-based) outage detection and response; and / or intelligent (AI / ML-based) load forecasting, including optional integration of DER 128 with the existing power grid. For example, consider a scenario where a utility company owns a network of generation and distribution assets, some of which are decades old. To ensure the lifespan and efficiency of these assets, platform 102 may provide predictive maintenance to warn the utility company before potential problems become severe.

[0573] In an embodiment, grid maintenance may be provided. Through proactive maintenance, utility companies can accurately detect defects and reduce unplanned outages, thus better serving customers. AI systems deployed with IoT and / or edge computing can help monitor energy assets and reduce maintenance costs. For example, if a transmission line shows signs of wear, platform 102 can alert the utility company to repair it in a timely manner. This proactive approach not only reduces unplanned outages but also lowers maintenance costs, resulting in a more efficient and cost-effective power grid.

[0574] Digital resources

[0575] In an embodiment, platform 102 may leverage the digital transformation of a wide range of digital resources. Machines are becoming increasingly intelligent, and software intelligence is embedded in all aspects of business, helping to drive new levels of operational efficiency and innovation. Additionally, the digital transformation is underway, including intelligent devices and systems with data processing and communication capabilities, the increasing prevalence of sensors almost everywhere in the edge, IoT, and other devices, and the generation of large volumes of dense data streams, all of which provide opportunities to increase intelligence, automation, optimization, and flexibility as information continuously flows between the physical and digital worlds. Such devices and systems require a large amount of energy. For example, data centers consume a large amount of energy, and edge and IoT devices can be deployed in off-grid environments that require alternative forms of energy generation, storage, or movement. In an embodiment, a set of digital resources may be integrated, accessed, or used to optimize the energy for computing, storage, and other resources in data centers and at the edge, etc. In an embodiment, as more and more devices are embedded with sensors and controllers, information can flow continuously between the physical and digital worlds as machines "talk" to each other. Products can be tracked from the source to the customer or during use, enabling rapid response to internal and external changes. Personnel responsible for managing or regulating such systems can obtain detailed data from these devices to optimize the operation of the entire process. This trend transforms big data into intelligent data, capable of significantly improving cost and process efficiency.

[0576] In embodiments, advancements in digital technology enable monitoring and operational performance levels that were previously impossible. With sensors and other smart assets, service providers can collect extensive data on multiple parameters for 24 / 7 real-time monitoring.

[0577] In embodiments, DER 128 will be integrated into computing networks and infrastructure devices and systems to enhance existing power grids and be used to reduce costs and improve reliability. For example, platform 102 can significantly enhance existing power grids by integrating DER 128 (e.g., a localized solar power plant or wind turbine) into urban infrastructure. For instance, during peak demand periods, platform 102 can enable a city's energy management system to utilize local energy rather than relying solely on traditional power plants, which can reduce the stress on the main power grid and also result in significant cost savings.

[0578] Mobile Energy Resources

[0579] In embodiments, DER can be integrated into mobile energy resource 124, such as electric vehicles (EVs) and their charging networks / infrastructure, to enhance existing power grids and be used to reduce costs and improve reliability. Given the rise of EVs (all types), charging infrastructure and vehicle charging planning need to be optimized to match supply and demand. Additionally, the growing electricity demand and the development of EV infrastructure will require optimization using other related technologies such as edge and IoT. EV charging can be integrated into decentralized infrastructure and can even be used as DER 128 by adding to the power grid (e.g., through bidirectional charging stations) or by powering another system locally. Vehicle power electronics systems and batteries can benefit the power grid by providing system and grid services. Excess energy can be stored in the vehicle as needed and discharged when required. This flexibility option not only avoids expensive load peaks during short-term high energy demand periods but also increases the share of renewable energy used.

[0580] In embodiments, to generally integrate electric vehicles and charging infrastructure into the distribution network, coordination with various other normalization communication protocols is required. Platform 102 can include, integrate, and / or link to a set of communication protocols that enable the management, supply, governance, and control of energy edge devices and systems using such protocols. Here, platform 102 can serve as a central hub, integrating various protocols to ensure smooth, efficient, and coordinated communication between the vehicle, charging station, and power grid when the EV is docked at the charging station.

[0581] Configured Stakeholder Energy Edge Solutions

[0582] A set of configured stakeholder energy edge solutions 108 can include a set of mobile demand solutions 152, a set of enterprise optimization solutions 154, a set of energy supply and governance solutions 156, and / or a set of localized production solutions 158, etc. These solutions use various advanced energy resources and systems 104 and / or various configurable data and intelligent modules and services 118 to achieve benefits for specific stakeholders (e.g., private enterprises, non-governmental organizations, independent service organizations, government organizations, etc.). All such solutions can utilize edge intelligence, for example, using data collected from on-board or integrated sensors, IoT systems, and edge devices located near entities that generate, store, convey, and / or use energy, to feed models, expert systems, analysis systems, data services, intelligent agents, robotic process automation systems, and other artificial intelligence systems, in order to facilitate solutions for specific stakeholder needs. For example, in the case of a city, the set of mobile demand solutions 152 can be used to predict peak travel times and adjust the public transportation schedule accordingly. Similarly, in the case of a large enterprise campus, the set of enterprise optimization solutions 154 can be used to manage its energy consumption, ensuring that office buildings are fully powered during working hours while conserving energy during non-working hours.

[0583] Enterprise optimization solutions

[0584] In an embodiment, the DER 128 will be integrated with or into enterprises and shared resources, enhancing the existing power grid and used to reduce costs and improve reliability. The increasing level of digitization will assist in the integration activities and promote new ways to optimize buildings / operations and energy in campuses and enterprises. For example, by integrating the DER 128, a campus can supplement its electricity demand with renewable energy. The digitization of energy management can help a campus monitor and adjust its energy consumption in real time. In an embodiment, this can enable the effective management of buildings by leveraging big data and plug load analysis, thus increasing the operating bottom line of for-profit enterprises. For example, a campus can efficiently manage its buildings, ensuring that energy is used where it is needed, thereby optimizing operating costs.

[0585] In an embodiment, IoT sensors and building automation control systems can be configured to help optimize floor space, identify unused equipment, automate efficient energy consumption, enhance security, and reduce the environmental impact of buildings. For example, in a multi-story office building equipped with IoT sensors and building automation control systems, these systems can monitor the energy consumption on each floor, ensuring that lighting and HVAC systems are optimized for the number of occupants. In one example, unused conference rooms can automatically turn off the lights and adjust the temperature, reducing energy waste.

[0586] In an embodiment, platform 102 may manage the total energy consumption of systems and devices connected to the power grid or a group of DERs 128. Some systems are almost always in operation, while other devices and machines may be connected only occasionally. By maintaining an understanding of the building's total daily power consumption and the role that individual devices play in the total energy use of a particular system, platform 102 can optionally predict, provide, manage, and control the total consumption through AI or algorithms. For example, platform 102, through AI and algorithms, can monitor and adjust energy consumption based on the specific needs of each building, thus optimizing energy use.

[0587] In an embodiment, platform 102 may track and utilize an understanding of occupant behavior. The occupant's activity level, behavior patterns, and comfort preferences can be taken into account as energy efficiency measures. This may include tracking various periodic or seasonal factors. Over time, the building's energy generation, storage, and / or consumption can follow predictable patterns that an IoT-based analytics platform can consider when generating recommended solutions. For example, in the winter, if the platform notices that residents tend to stay at home in the evening, it can adjust the heating accordingly. Over time, the system learns from these patterns to ensure that energy is used efficiently.

[0588] In an embodiment, platform 102 may support or integrate with systems or platforms for autonomous operation. For example, industrial sites, such as oil rigs and power plants, require extensive monitoring for efficiency and safety, as liquid, steam, or oil leaks can be catastrophic, costly, and wasteful. Artificial intelligence and machine learning can provide autonomous capabilities for power plants, such as power plants served by edge devices, IoT devices, and on-site cameras and sensors. Models can be deployed at the edge of the power plant or on DER 128, for example, using real-time inference and pattern detection to identify faults, such as leaks, vibrations, stress, etc. Operators can use computer vision, deep learning, and intelligent video analysis (IVA) to monitor heavy machinery, detect potential hazards, and alert workers in real time to protect their health and safety, prevent accidents, and assign maintenance technicians for maintenance. For example, in a factory with multiple machines, through AI and machine learning, platform 102 can monitor the health of the machines in real time, predict potential weaknesses, and recommend timely maintenance and repair.

[0589] In an embodiment, platform 102 may support or integrate with systems or platforms for pipeline optimization. For example, oil and gas companies may rely on finding the most suitable routes to transport oil to refineries and ultimately to gas stations. Edge AI can calculate the optimal flow of oil to ensure production reliability and protect the long-term health of the pipeline. In an embodiment, the company can inspect the pipeline for defects that may cause dangerous failures and automatically alert the pipeline operator.

[0590] Energy supply and governance solutions

[0591] Energy supply and governance solution 156 may include solutions for governing mining operations. Cobalt, nickel, and other metals are essential components of the batteries required for the green EV revolution. The capital needed to support the growing market will place economic pressure on mining operations. Companies are exploring cobalt in regions such as Greenland, partly because the region can offer reliable enforcement of labor laws, tax compliance, etc. Such commitments can be made more reliably there and in other jurisdictions through a set of mining governance solutions 542. This set of mining governance solutions 542 may include mine-level IoT sensing of the mining environment, ground-penetrating sensing of unmined sections, mass spectrometry- and computer vision-based sensing of mined materials, asset tagging of smart containers (e.g., detecting and recording opening and closing events to ensure that the materials placed in the container are the same as those delivered at the end point), wearable devices for detecting the physiological state of miners, secure (e.g., blockchain- and DLT-based) recording and resolution of transactions and transaction-related events, smart contracts for automatically distributing proceeds (e.g., to tax authorities, workers), and automated systems for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements. All of the above can be selectively represented in a digital twin on behalf of each mine owner or operated by an enterprise, from basic sensors to compliance reporting.

[0592] Energy supply and governance solution 156 may also include a set of carbon-aware energy solutions, where data on the current carbon generation or emission status is collected through edge and IoT devices, and the operation entities are managed by automatically generating a set of recommendations and / or control instructions to meet policies, e.g., by keeping operations within the range offset by available carbon offset allowances, etc., thereby managing the control of operation entities that generate (or capture) carbon.

[0593] More details on various energy supply and governance solutions 156 are provided below.

[0594] Localized production solutions

[0595] In an embodiment, a set of localized production solutions 158 may be integrated with, linked to, or managed by platform 102, such that localized production needs can be met, especially for goods with very high transportation costs (e.g., food) or services where the cost of energy distribution has a large adverse impact on the profit of the product or service (e.g., where intensive computing is required in places where the power grid does not exist, lacks capacity, is unreliable, or is too expensive). Platform 102 may manage the energy consumption of this set of localized production solutions 158, optimizing usage based on available resources, especially in places where the traditional power grid may not exist or is unreliable.

[0596] In an embodiment, the power management system can converge with other systems (e.g., building management systems, operations management systems, production systems, service systems, data centers, etc.) to allow enterprise-wide energy management. By integrating power management with building management systems, operations management systems, production systems, service systems, data centers, etc., platform 102 can ensure the overall optimal use of energy in the enterprise. For example, during off-peak hours, when the building management system reduces lighting, the data center can shift its heavy computing to balance the overall energy load.

[0597] Figure 3 : More detailed information about distributed energy generation systems

[0598] Reference Figure 3 , the distributed energy generation system 302 can include wind turbines, solar photovoltaics (PV), flexible and / or floating solar systems, fuel cells, modular nuclear reactors, nuclear batteries, modular hydropower systems, microturbines and turbine arrays, reciprocating engines, gas turbines, and thermal power plants, etc. The distributed energy storage system 304 can include battery energy storage (including chemical batteries, etc.), molten salt energy storage, electro-thermal energy storage (ETES), gravity-based storage, compressed fluid energy storage, pumped hydroelectric energy storage (PHES), and liquid air energy storage (LAES), etc. The distributed energy storage system 304 can be managed by platform 102. In an embodiment, the distributed energy storage system 304 can be portable, such that energy units can be transported to the point of use, including points of use not connected to the traditional power grid or points of use where the traditional power grid does not fully meet the demand (e.g., points of use requiring greater peak power, more reliable continuous power, or other capabilities). Management can include integrating, coordinating, and maximizing the return on investment (ROI) of distributed energy resources (DERs), while providing reliability and flexibility for energy demand.

[0599] In an embodiment, the DER 128 can use various distributed energy delivery methods and systems 308 with various energy delivery capabilities, including transmission lines (e.g., traditional power grids and building infrastructure), wireless energy transmission (including resonant transmission through coupling, high-Q resonators, near-field energy transmission, and other methods), transportation of fluids, batteries, fuel cells, small nuclear systems, etc.

[0600] Mobile energy resource 124 includes various resources for generating, storing, or delivering energy at various scales; thus, mobile energy resource 124 may include a subcategory of DER 128 having a mobility attribute, e.g., where mobile energy resource 124 is integrated into a vehicle 310 (e.g., an electric vehicle, a hybrid electric vehicle, a hydrogen fuel cell vehicle, etc., and in embodiments including a group of autonomous vehicles, the autonomous vehicle may be an unmanned aerial vehicle (UAV), a drone, etc.); where the resource is integrated into or used by a mobile electronic device 312 or other mobile system; where mobile energy resource 124 is a portable resource 314 (including that the mobile energy resource is removable and replaceable from the vehicle or other system), etc. As mobile energy resource 124 and the supporting infrastructure (e.g., charging stations) scale in capacity and availability, the coordination of mobile energy resource 124 with other DER 128 (optionally in coordination with available grid resources) becomes increasingly important.

[0601] Resources related to the generation, storage, and transmission of energy are increasingly being digitally transformed. These digital resources 122 can include smart resources 318 (e.g., smart devices (e.g., thermostats), smart home devices (e.g., speakers), smart buildings, smart wearables, and many other devices enabled with processors, network connectivity, smart agents, and other on-board intelligent features), where the intelligent features of the smart resources 318 can be used for energy coordination, optimization, autonomy, control, etc. and / or for providing data for artificial intelligence and analytics related to the foregoing. The digital resources 122 can also include IoT digital resources and edge digital resources 320, where sensors or other data collectors (e.g., data collectors that monitor event logs, network packets, network traffic patterns, connected device location patterns, or other available data) provide additional energy-related intelligence, e.g., intelligence related to the energy generation, storage, transmission, or consumption of traditional infrastructure systems and devices, which range from large-scale generators and transformers to consumer or commercial devices, appliances, and other systems in the vicinity of a set of IoT or edge devices that can monitor these. Thus, IoT and edge devices can provide digital information about the energy state and flow of such devices and systems, whether or not these devices and systems have on-board intelligent features; for example, IoT devices can also deploy current sensors on the power lines to appliances to detect usage patterns, or edge-connected devices can detect whether another device or system connected to that device is in use (and in what state) by monitoring the network traffic from another device. The digital resources 122 can also include cloud aggregation resources 322 related to energy generation, storage, transmission, or use, e.g., by aggregating data across a fleet of similar resources owned or operated by an enterprise and used in conjunction with defined workflows or activities. The cloud aggregation resources 322 can consume data from various data resources 110, from crowdsourcing, from sensor data collection, from edge device data collection, and many other sources.

[0602] In an embodiment, the digital resources 122 can be used for a wide range of purposes related to or benefiting from real-time information about the properties, states, or flows of energy generation, storage, transmission, or consumption, including enabling digital twins, e.g., a set of adaptive energy digital twin systems 134 and / or a set of stakeholder energy digital twins 148, and for a set of configured stakeholder energy edge solutions 108. For example, a digital twin of an urban public transportation system can predict energy demand based on commuting patterns and accordingly adjust the operation of electric buses. Similarly, digital twins can be applied to various fields, e.g., manufacturing units that monitor mechanical energy consumption. The integration of the platform 102 with these digital twins ensures that energy is always used optimally to adapt to the real-time needs of the corresponding systems.

[0603] In recent decades, energy production, storage, and consumption, particularly energy involving green or renewable energy, have been the subject of intensive research and development, resulting in higher peak power generation capabilities, increased storage capacity, reduced size and weight, improved intelligence and autonomy, etc. Advanced energy resources and systems 104 can include a wide range of advanced energy infrastructure systems and devices resulting from a combination of features and capabilities. In an embodiment, a flexible hybrid energy system 324 can be provided, which is adapted to meet varying energy consumption requirements. For example, more than one type of energy (e.g., solar or wind energy) can be provided to meet the energy consumption requirements for the baseline of off-grid operation, and a nuclear battery can also be provided to meet much higher peak power requirements, e.g., for temporary resource-intensive activities such as operating a drill in a mine or periodically running large factory machines. A variety of flexible hybrid energy systems 324 are envisioned herein, including systems configured for modular interconnection with various types of localized production infrastructure as described herein and elsewhere. In an embodiment, advanced energy resources and systems 104 can include advanced energy generation systems that draw power from fluid flows, such as, for example, a portable turbine array 328 that can be transported to a point of consumption near wind or water currents to replace or augment grid resources. Advanced energy resources and systems 104 can also include a modular nuclear system 330, including nuclear systems configured to use nuclear batteries and nuclear systems configured to have mechanical, electrical, and data interfaces to work with various consumption systems, the consumption systems including vehicles, localized production systems (as described elsewhere herein), smart buildings, etc. The modular nuclear system 330 can include SMRs and other reactor types. Advanced energy resources and systems 104 can include advanced storage systems 332, including advanced batteries and fuel cells, including batteries with on-board intelligence for autonomous management, batteries with network connectivity for remote management, batteries with alternative chemistries (including green chemistries such as nickel-zinc), batteries made of alternative materials or structures (e.g., diamond batteries), batteries incorporating power generation capabilities (e.g., nuclear batteries), advanced fuel cells (e.g., cathode layer fuel cells, alkaline fuel cells, polymer electrolyte fuel cells, solid oxide fuel cells, and many others).

[0604] Figure 4 : More detailed information regarding data resources

[0605] Reference Figure 4, data resources 110 for energy edge coordination can include a wide range of public data sets as well as private or proprietary data sets of enterprises or individuals. This can include data sets generated by or transmitted through edge and IoT network systems 160, such as sensor data 402 (e.g., from sensors integrated into or placed on machines or devices, sensors in wearable devices, etc.); network data 404 (e.g., data on network traffic, latency, congestion, quality of service (QoS), packet loss, error rate, etc.); event data 408 (e.g., data from event logs of edge and IoT devices, data from event logs of enterprise's operational assets, event logs of wearable devices, event data detected by inspecting traffic on application programming interfaces, event streams published by devices and systems, user interface interaction events (e.g., captured by tracking clicks, eye tracking, etc.), user behavior events, transaction events (including financial transactions, database transactions, etc.), events within workflows (including directed flows, acyclic flows, iterative flows, and / or cyclic flows, etc.)); status data 410 (e.g., data indicating the historical, current, or predicted / expected status of entities (e.g., machines, systems, devices, users, objects, individuals, and many others), and including a wide range of attributes and parameters related to the energy generation, storage, delivery, or utilization of such entities); and / or combinations of the foregoing (e.g., data indicating the status of an entity and the status of a workflow involving that entity).

[0606] In an embodiment, the data resources may include energy-related public data resources 162. For example, energy grid data 422 (e.g., historical, current, and expected / forecasted maintenance status, operating status, energy production status, capacity, efficiency, or other attributes of energy grid assets involved in the generation, storage, or transmission of energy); energy market data 424 (e.g., historical, current, and expected / forecasted pricing data of energy or energy-related entities, including spot market prices of energy based on location, consumption type, generation type, etc., its day-ahead or other futures market pricing, fuel costs, costs of raw materials involved (e.g., material costs used in battery production), costs of energy-related activities (e.g., mineral extraction, etc.)); location and mobility data 428 (e.g., data indicating the historical, current, and / or expected / forecasted location or movement of individual groups (e.g., crowds attending large events, such as concerts, festivals, sports events, conferences, etc.), data indicating the historical, current, and / or expected / forecasted location or movement of vehicles (e.g., for transporting people, goods, fuel, materials, etc.), data indicating the historical, current, and / or expected / foreca...

Claims

1. An AI-based platform for realizing intelligent coordination and management of electricity and energy, comprising: An adaptive energy data pipeline configured to transmit data across a set of nodes in a network, wherein each node in the set of nodes is adapted to operate on an energy data set associated with at least one of energy generation, energy storage, energy delivery, or energy consumption, and wherein at least one node in the set of nodes is configured by one or both of an algorithm or a rule set to filter, compress, transform, correct errors, and / or route at least a portion of the energy data set based on at least one of a set of network conditions, data size, data granularity, or data content.

2. The AI-based platform according to claim 1, wherein, The adaptive energy data pipeline is further configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on one or more of the following: Congestion conditions; Delay and / or latency conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

3. The AI-based platform according to claim 1, further comprising an adaptive energy digital twin representing one or more of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

4. The AI-based platform according to claim 1, further comprising an adaptive energy digital twin configured to perform one or more of the following: Provide visual and / or analytical metrics of energy consumption of one or more energy consumers; Filter energy data; Highlight energy data; or Adjust energy data.

5. The AI-based platform according to claim 1, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption through one or more of the following: One or more machines; One or more factories; or One or more vehicles in a fleet.

6. The AI-based platform according to claim 1, wherein, The adaptive energy data pipeline is further configured to perform one or more of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Transform, convert, normalize, and / or clean energy-related data, Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

7. The AI-based platform according to claim 1, wherein, The energy data set is based on one or more common data resources, the common data resources including one or more of the following: Weather data resources; Satellite data resources; Census, population, demographic, and / or psychographic data resources; Market data resources; or E-commerce data resources.

8. The AI-based platform according to claim 1, wherein The energy dataset is based on one or more enterprise data resources, and the enterprise data resources include one or more of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data.

9. The AI-based platform according to claim 1 further includes at least one AI-based model and / or algorithm, wherein, The at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: One or more human labels and / or markings; One or more human interactions with hardware and / or software systems; One or more results; One or more AI-generated training data samples; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process.

10. The AI-based platform according to claim 1, wherein, At least one node in the set of nodes is configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: One or more fixed transmission lines; One or more wireless energy transmission instances; One or more fuel deliveries; or One or more stored energy deliveries.

11. The AI-based platform according to claim 1, wherein, At least one node in the set of nodes is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events.

12. The AI-based platform according to claim 1, wherein, At least one node in the set of nodes is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: Off-grid energy generation systems; Off-grid energy storage systems; or Off-grid energy mobilization systems.

13. The AI-based platform according to claim 1, wherein, The adaptive energy data pipeline is further configured to: Monitor one or both of the following: The total energy consumption of at least a portion of the set of nodes; or The role of at least one node in the set of nodes in the total energy consumption of at least a portion of the set of nodes; And Based on the monitoring, perform one or more of the following: Manage the energy consumption of the set of nodes, Predict the energy consumption of the set of nodes, or Supply resources associated with the energy consumption of the set of nodes.

14. The AI-based platform according to claim 1, wherein, The set of nodes in the network including the adaptive energy data pipeline includes a set of edge networking devices that manage at least one of energy consumption, energy storage, energy delivery, or energy consumption through a set of operating devices controlled by the edge networking devices.

15. The AI-based platform according to claim 1, wherein, The adaptive energy data pipeline is further configured to automatically select the lowest-cost route for data transmitted across the set of nodes, the selection being based on low-priority energy usage associated with the data.

16. The AI-based platform according to claim 1, wherein, The adaptive energy data pipeline is further configured to automatically select a high-quality service route for data transmitted across the set of nodes, the selection being based on high-priority energy usage associated with the data.

17. The AI-based platform according to claim 1, wherein, The adaptive energy data pipeline includes a set of artificial intelligence capabilities configured to adjust the pipeline to enable components that optimize data transmission according to energy coordination requirements.

18. The AI-based platform according to claim 1, wherein The adaptive energy data pipeline includes a self-organizing data memory configured to store data on a device based on one or more of data patterns, data content, or data context.

19. The AI-based platform according to claim 1, wherein, The adaptive energy data pipeline is configured to perform automated adaptive networking, which includes one or more of adaptive protocol selection, adaptive data routing based on RF conditions, adaptive data filtering, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity, or adaptive use of peer-to-peer network capacity.

20. The AI-based platform according to claim 1, wherein, The adaptive energy data pipeline is configured to perform enterprise environment adaptation by automatically processing data based on one or more of an enterprise's operating environment, an enterprise's trading environment, or an enterprise's financial environment.

21. The AI-based platform according to claim 1, wherein, At least one node of the set of nodes is further configured to adjust communication with at least one other node of the set of nodes to adapt the reporting of data associated with at least one of energy generation, energy storage, energy delivery, or energy consumption to the at least one other node.

22. The AI-based platform according to claim 1, wherein, The at least one node of the set of nodes is further configured to adapt the reported data to at least one other node of the set of nodes, wherein adapting the reported data is based on the priority of consumption of the reported data.

23. The AI-based platform according to claim 1, wherein, The set of nodes includes a heterogeneous group that includes at least one energy producer and at least one energy consumer, and the adaptive energy data pipeline is further configured to direct one or both of the at least one energy producer and at least one energy consumer to communicate with at least one other node of the set of nodes via at least one communication route.

24. The AI-based platform according to claim 1, wherein, The adaptive energy data pipeline is further configured to request reported data from at least one node of the set of nodes, the reported data being based on a granularity level, and the granularity level being based on the priority of the machine associated with the reported data.

25. The AI-based platform according to claim 1, wherein The adaptive energy data pipeline is further configured to prioritize the transmission of reported data through the adaptive energy data pipeline, and the prioritization is based on the monitoring responsibility associated with the reported data.

26. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: A set of adaptive autonomous data processing systems, wherein each adaptive autonomous data processing system is configured to collect data related to energy generation, storage, or delivery from a set of edge devices under the operation control of a set of distributed energy, and is configured to autonomously adjust a set of operating parameters for such operation control based on the collected data.

27. The AI-based platform according to claim 26, wherein, Each adaptive autonomous data processing system is further configured to adapt data transmission through a network and / or communication system, wherein the adaptation is based on one or more of the following: Congestion conditions; Delay and / or latency conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of Service (QoS) conditions; Usage status; Market factor status; or User configuration status.

28. The AI-based platform according to claim 26, wherein, Each adaptive autonomous data processing system includes an adaptive energy digital twin that represents one or more of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority status.

29. The AI-based platform according to claim 26, wherein, Each adaptive autonomous data processing system includes an adaptive energy digital twin that is configured to perform one or more of the following: Provide visual and / or analytical metrics of the energy consumption of one or more energy consumers; Filter energy data; Highlight energy data; or Adjust energy data.

30. The AI-based platform according to claim 26, wherein, Each adaptive autonomous data processing system includes an adaptive energy digital twin that is configured to generate visual and / or analytical metrics of energy consumption through one or more of the following: One or more machines; One or more factories; or One or more vehicles in a fleet.

31. The AI-based platform according to claim 26, wherein, Each adaptive autonomous data processing system is further configured to perform one or more of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Transform, convert, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

32. The AI-based platform according to claim 26, wherein, The energy edge data is based on one or more common data resources, which include one or more of the following: Weather data resources; Satellite data resources; Census, population, demographic, and / or psychographic data resources; Market data resources; or E-commerce data resources.

33. The AI-based platform according to claim 26, wherein, The energy edge data is based on one or more enterprise data resources, which include one or more of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data.

34. The AI-based platform according to claim 26 further includes at least one AI-based model and / or algorithm, wherein, The at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: One or more human labels and / or tags; One or more human interactions with hardware and / or software systems; One or more results; One or more AI-generated training data samples; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process.

35. The AI-based platform according to claim 26, wherein, Each adaptive autonomous data processing system is further configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: One or more fixed transmission lines; One or more wireless energy transmission instances; One or more fuel deliveries; or One or more stored energy deliveries.

36. The AI-based platform according to claim 26, wherein, Each adaptive autonomous data processing system is also configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events.

37. The AI-based platform according to claim 26, wherein, At least one adaptive autonomous data processing system is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: Off-grid energy generation systems; Off-grid energy storage systems; or Off-grid energy mobilization systems.

38. The AI-based platform according to claim 26, wherein, The platform also includes an adaptive energy data pipeline configured to transmit data across a set of nodes in a network.

39. The AI-based platform according to claim 38, wherein, The set of nodes in the network including the adaptive energy data pipeline includes a set of edge networking devices that manage at least one of energy consumption, energy storage, energy delivery, or energy consumption through a set of operating devices controlled by the edge networking devices.

40. The AI-based platform according to claim 38, wherein, The adaptive energy data pipeline is also configured to automatically select the lowest-cost route for data transmitted across the set of nodes, and the selection is based on low-priority energy use associated with the data.

41. The AI-based platform according to claim 38, wherein, The adaptive energy data pipeline is also configured to automatically select a high-quality service route for data transmitted across the set of nodes, and the selection is based on high-priority energy use associated with the data.

42. The AI-based platform according to claim 38, wherein, The adaptive energy data pipeline includes a set of artificial intelligence capabilities configured to adjust the pipeline to enable components that optimize data transmission according to energy coordination requirements.

43. The AI-based platform according to claim 38, wherein, The adaptive energy data pipeline includes a self-organizing data memory configured to store data on devices based on one or more of data patterns, data content, or data context.

44. The AI-based platform according to claim 38, wherein, The adaptive energy data pipeline is configured to perform automated adaptive networking, and the adaptive networking includes one or more of adaptive protocol selection, adaptive data routing based on RF conditions, adaptive data filtering, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity, or adaptive use of peer-to-peer network capacity.

45. The AI-based platform according to claim 38, wherein, The adaptive energy data pipeline is configured to perform enterprise environment adaptation by automatically processing data based on one or more of the enterprise's operating environment, enterprise's trading environment, or enterprise's financial environment.

46. The AI-based platform according to claim 26, wherein, At least one adaptive autonomous data processing system is also configured to determine the scheduling of a set of processes based on at least one priority and / or demand associated with the set of distributed energy.

47. The AI-based platform according to claim 26, wherein, At least one adaptive autonomous data processing system is also configured to adjust communication with at least one edge device in the set of edge devices based on at least one priority and / or demand associated with the set of distributed energy, and the communication is associated with an investigation of energy generation, storage, or delivery of the distributed energy.

48. The AI-based platform according to claim 26, wherein, At least one adaptive autonomous data processing system is also configured to issue instructions to at least one edge device in the set of edge devices, the instructions being based on an investigation of the generation, storage, or delivery of the distributed energy, and the instructions causing the at least one edge device to adjust the generation, storage, or delivery of energy of the at least one edge device.

49. An AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: A system configured to perform automatic and coordinated governance within a set of energy entities and a set of distributed edge energy resources operatively coupled within an energy grid, wherein at least one distributed edge energy resource is operationally independent of the energy grid.

50. The AI-based platform according to claim 49, wherein, The system is also configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on one or more of the following: Congestion conditions; Latency and / or delay conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of Service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

51. The AI-based platform according to claim 49, further comprising an adaptive energy digital twin representing one or more of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

52. The AI-based platform according to claim 49, further comprising an adaptive energy digital twin configured to perform one or more of the following: Provide visual and / or analytical metrics of energy consumption of one or more energy consumers; Filter energy data; Highlight energy data; or Adjust energy data.

53. The AI-based platform according to claim 49, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption through one or more of the following: One or more machines; One or more factories; or One or more vehicles in a fleet.

54. The AI-based platform according to claim 49, wherein, The system is also configured to perform one or more of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Convert, transform, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

55. The AI-based platform according to claim 49 further includes at least one AI-based model and / or algorithm, wherein, The at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of the following: One or more human labels and / or markings; One or more human interactions with hardware and / or software systems; One or more results; One or more AI-generated training data samples; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process.

56. The AI-based platform according to claim 49, wherein, The system is also configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: One or more fixed transmission lines; One or more wireless energy transmission instances; One or more fuel deliveries; or One or more stored energy deliveries.

57. The AI-based platform according to claim 49, wherein, The system is also configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: Energy purchase and / or sales events; Service fees associated with energy purchase and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events.

58. The AI-based platform according to claim 49, wherein, At least one distributed energy edge resource is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: Off-grid energy generation systems; Off-grid energy storage systems; or Off-grid energy mobilization systems.

59. The AI-based platform according to claim 49, wherein, The system is configured to facilitate the governance of a mining environment.

60. The AI-based platform according to claim 59, wherein, The system includes mine-level Internet of Things (IoT) sensing of the mining environment, ground-penetrating sensing of unmined portions of the mining environment, mass spectrometry- and computer vision-based sensing of mined materials, asset tagging of smart containers, wearable devices for detecting the physiological state of miners, secure recording and resolution of transactions and transaction-related events, smart contracts for automatically allocating proceeds obtained from the mining environment, and an automated system for recording, reporting, and evaluating compliance with contractual, regulatory, and legal policy requirements.

61. The AI-based platform according to claim 59, wherein, The system includes a set of carbon-aware energy edge solutions, and the solutions include exploring, configuring, and implementing a set of strategies regarding carbon generation.

62. The AI-based platform according to claim 61, wherein, The solutions require monitoring the production of energy in the mining environment to track carbon emissions generated in the mining environment.

63. The AI-based platform according to claim 61, wherein, The solutions require the mining environment to produce energy to offset the carbon generated in the mining environment.

64. The AI-based platform according to claim 59, wherein, The platform includes a user interface, and the system includes a set of automated energy policy deployment solutions that can be configured via user interaction with the user interface.

65. The AI-based platform according to claim 59, wherein, The system includes a smart agent that is trained to generate strategies related to the governance of a mining environment, and the smart agent is trained on a training set of historical data, feedback from results, and human policy settings interactions.

66. The AI-based platform according to claim 59, wherein, The system promotes the governance of a mining environment by implementing strategies including one or more of the following: Setting a maximum energy usage for an entity over a period of time; Setting a maximum energy cost for an entity over a period of time; Setting a maximum carbon production for an entity over a period of time; Setting a maximum pollution emission for an entity over a period of time; Setting carbon offset requirements; Setting renewable energy credit requirements; Setting energy mix requirements; Setting a minimum profit margin based on the energy and other marginal costs of a production entity; or Setting a minimum storage baseline for an energy storage entity.

67. The AI-based platform according to claim 59, wherein, The system includes a set of energy management smart contract solutions configured to allow users of the platform to design, generate, and deploy smart contracts that automatically provide a level of management for a set of energy transactions.

68. The AI-based platform according to claim 59, wherein, The system includes a set of automated energy financial control solutions configured to allow users of the platform to design, generate, configure, or deploy policies related to controlling financial factors associated with one or more of energy generation, storage, transmission, or utilization.

69. The AI-based platform according to claim 49, wherein, The system is further configured to determine a priority associated with at least one of the set of energy entities or the set of distributed edge energy resources, and the priority is based on a policy associated with at least one of the set of energy entities or the set of distributed energy resources.

70. The AI-based platform according to claim 49, wherein, The system is further configured to perform monitoring of energy productivity through the set of energy entities and adjust the automatic and coordinated governance of the set of energy entities based on the monitoring of productivity.

71. The AI-based platform according to claim 49, wherein, The system is further configured to allocate processing of the set of distributed edge energy based on at least one energy measurement and / or prediction associated with the set of energy entities.

72. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: An adaptive energy data pipeline configured to transmit data across a set of nodes in a network, wherein at least one subset of the set of nodes is configured to set at least one parameter of data communication associated with the adaptive energy data pipeline by at least one of a rule or an algorithm, and the at least one parameter is based on a set of metrics of the current network condition to optimize the energy used in the data communication.

73. The AI-based platform according to claim 72, wherein, The at least one parameter is one or more of the following: Routing instructions, Routing parameters, Error correction parameters, Compression parameters, Storage parameters, or Timing parameters.

74. The AI-based platform according to claim 72, wherein, The adaptive energy data pipeline is further configured to adapt to data transmission through the network and / or communication system, wherein the adaptation is based on one or more of the following: Congestion condition; Delay and / or latency condition; Packet loss condition; Error rate condition; Transport cost condition; Quality of service (QoS) condition; Usage condition; Market factor condition; or User configuration condition.

75. The AI-based platform according to claim 72, further comprising an adaptive energy digital twin representing one or more of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

76. The AI-based platform according to claim 72, further comprising an adaptive energy digital twin configured to perform one or more of the following: Provide visual and / or analytical metrics of energy consumption of one or more energy consumers; Filter energy data; Highlight energy data; or Adjust energy data.

77. The AI-based platform according to claim 72, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption through one or more of the following: One or more machines; One or more factories; or One or more vehicles in a vehicle fleet.

78. The AI-based platform according to claim 72, wherein, The adaptive energy data pipeline is further configured to perform one or more of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Transform, convert, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

79. The AI-based platform according to claim 72, wherein, The data is based on one or more common data resources, which include one or more of the following: Weather data resources; Satellite data resources; Census, population, demographic, and / or psychographic data resources; Market data resources; or E-commerce data resources.

80. The AI-based platform according to claim 72, wherein, The data is based on one or more enterprise data resources, which include one or more of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data.

81. The AI-based platform according to claim 72 further includes at least one AI-based model and / or algorithm, wherein, The at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: One or more human labels and / or markings; One or more human interactions with hardware and / or software systems; One or more results; One or more AI-generated training data samples; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process.

82. The AI-based platform according to claim 72, wherein, The adaptive energy data pipeline is further configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: One or more fixed transmission lines; One or more wireless energy transmission instances; One or more fuel deliveries; or One or more stored energy deliveries.

83. The AI-based platform according to claim 72, wherein, The adaptive energy data pipeline is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events.

84. The AI-based platform according to claim 72, wherein, At least a portion of the adaptive energy data pipeline is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: Off-grid energy generation systems; Off-grid energy storage systems; or Off-grid energy mobilization systems.

85. The AI-based platform according to claim 72, wherein, The adaptive energy data pipeline is further configured to: Monitor one or both of the following: The total energy consumption of at least a portion of the set of nodes; or The role of at least one node of the set of nodes in the total energy consumption of at least a portion of the set of nodes; And Based on the monitoring, perform one or more of the following: Manage the energy consumption of the set of nodes, Predict the energy consumption of the set of nodes, or Provide resources associated with the energy consumption of the set of nodes.

86. The AI-based platform according to claim 72, wherein, The set of nodes in the network that includes the adaptive energy data pipeline includes a set of edge networking devices, and the edge networking devices manage at least one of energy consumption, energy storage, energy delivery, or energy consumption through a set of operating devices controlled via the edge networking devices.

87. The AI-based platform according to claim 72, wherein, The adaptive energy data pipeline is further configured to automatically select the lowest cost route for data transmitted across the set of nodes, and the selection is based on low-priority energy usage associated with the data.

88. The AI-based platform according to claim 72, wherein, The adaptive energy data pipeline is further configured to automatically select a high-quality service route for data transmitted across the set of nodes, and the selection is based on high-priority energy usage associated with the data.

89. The AI-based platform according to claim 72, wherein, The adaptive energy data pipeline includes a set of artificial intelligence capabilities, and the capabilities are configured to adjust the pipeline to enable components for optimizing data transmission according to energy coordination requirements.

90. The AI-based platform according to claim 72, wherein, The adaptive energy data pipeline includes a self-organizing data memory, and the data memory is configured to store data on devices based on one or more of data patterns, data content, or data context.

91. The AI-based platform according to claim 72, wherein, The adaptive energy data pipeline is configured to perform automated adaptive networking, and the adaptive networking includes one or more of adaptive protocol selection, adaptive data routing based on RF conditions, adaptive data filtering, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity, or adaptive use of peer-to-peer network capacity.

92. An AI-based platform for realizing intelligent coordination and management of electricity and energy, comprising: A digital twin system having a digital twin of a mining environment, wherein the digital twin includes at least one parameter detected by sensors of the mining environment.

93. The AI-based platform according to claim 92, wherein, The at least one parameter is associated with one or more of the following: An unmined portion of the mining environment; Mining materials from the mining environment, Smart container events related to smart containers associated with the mining environment, The physiological state of miners associated with the mining environment; Transaction-related events associated with the mining environment, or The mining environment complies with one or more contracts, regulations, and / or legal policies.

94. The AI-based platform according to claim 92, wherein, The digital twin system also represents one or more of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

95. The AI-based platform according to claim 92, wherein, The digital twin system is further configured to perform one or more of the following: Provide visual and / or analytical metrics of the energy consumption of one or more energy consumers; Filter energy data; Highlight energy data; or Adjust energy data.

96. The AI-based platform according to claim 92, wherein, The digital twin system is further configured to generate visual and / or analytical metrics of energy consumption by one or more of the following: One or more machines; One or more factories; or One or more vehicles in a fleet.

97. The AI-based platform according to claim 92, wherein, The parameters are based on one or more common data resources, which include one or more of the following: Weather data resources; Satellite data resources; Census, population, demographic, and / or psychographic data resources; Market data resources; or E-commerce data resources.

98. The AI-based platform according to claim 92, wherein, The parameters are based on one or more enterprise data resources, which include one or more of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data.

99. The AI-based platform according to claim 92, wherein, The digital twin system includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: One or more human labels and / or markings; One or more human interactions with a hardware and / or software system; One or more results; One or more AI-generated training data samples; Supervised learning training processes; Semi-supervised learning training processes; or Deep learning training processes.

100. The AI-based platform according to claim 92, wherein, The digital twin system is further configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: One or more fixed transmission lines; One or more wireless energy transmission instances; One or more fuel deliveries; or One or more stored energy deliveries.

101. The AI-based platform according to claim 92, wherein, The digital twin system is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events.

102. The AI-based platform according to claim 92, wherein, The digital twin system is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: Off-grid energy generation systems; Off-grid energy storage systems; or Off-grid energy mobilization systems.

103. The AI-based platform according to claim 92, wherein, The mining environment is a data mining environment.

104. The AI-based platform according to claim 92, wherein, The mining environment is a set of resources for performing computational operations.

105. The AI-based platform according to claim 92, wherein, The platform includes mine-level Internet of Things (IoT) sensing of the mining environment, ground-penetrating sensing of unmined portions of the mining environment, mass spectrometry- and computer vision-based sensing of mining materials, asset tagging of smart containers, wearable devices for detecting the physiological state of miners, secure recording and resolution of transactions and transaction-related events, smart contracts for automatically allocating revenues obtained from the mining environment, and an automated system for recording, reporting, and evaluating compliance with contractual, regulatory, and legal policy requirements.

106. The AI-based platform according to claim 92, wherein, The platform includes a set of carbon-aware energy edge solutions that include exploring, configuring, and implementing a set of strategies regarding carbon generation.

107. The AI-based platform according to claim 106, wherein, The solutions require monitoring the energy production of the mining environment to track the carbon emissions generated by the mining environment.

108. The AI-based platform according to claim 106, wherein, The solutions require the mining environment to produce energy to offset the carbon generated by the mining environment.

109. The AI-based platform according to claim 92, wherein, The platform includes a user interface, and the platform includes a set of automated energy policy deployment solutions that can be configured via user interaction with the user interface.

110. The AI-based platform according to claim 92, wherein, The platform includes a smart agent that is trained to generate strategies related to the governance of the mining environment, and the smart agent is trained on a training set of historical data, feedback from results, and human policy settings interactions.

111. The AI-based platform according to claim 92, wherein, The platform promotes the governance of the mining environment by implementing one or more of the following strategies: Setting a maximum energy usage for an entity over a period of time; Setting a maximum energy cost for an entity over a period of time; Setting a maximum carbon production for an entity over a period of time; Setting a maximum pollution emission for an entity over a period of time; Setting carbon offset requirements; Setting renewable energy quota requirements; Setting energy mix requirements; Setting a minimum profit margin based on the energy and other marginal costs of the production entity; or Setting a minimum storage baseline for an energy storage entity.

112. The AI-based platform according to claim 92, wherein, The at least one parameter includes measurements made by the sensors, and the measurements are associated with at least one piece of equipment included in the industrial operations of the mining environment.

113. The AI-based platform according to claim 92, wherein, The digital twin system includes a scheduler that is configured to determine a schedule for generating, storing, and / or delivering energy to at least one piece of equipment associated with the industrial operations of the mining environment, and the schedule is based on the at least one parameter detected by the sensors.

114. The AI-based platform according to claim 92, wherein, The at least one parameter included in the digital twin includes at least one attribute of at least one data set associated with the mining environment.

115. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: A governance system for mining operations; And A reporting system for transmitting at least one parameter sensed by sensors of a mine of the mining operations, wherein the at least one parameter is associated with the mining operations' compliance with a set of labor standards.

116. The AI-based platform according to claim 115, wherein, The reporting system is further configured to adapt data transmission over a network and / or communication system, wherein the adaptation is based on one or more of the following: Congestion conditions; Latency and / or delay conditions; Packet loss conditions; Error rate conditions; Transportation cost conditions; Quality of Service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

117. The AI-based platform according to claim 115, further comprising an adaptive energy digital twin, the adaptive energy digital twin representing one or more of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

118. The AI-based platform according to claim 115, further comprising an adaptive energy digital twin, the adaptive energy digital twin being configured to perform one or more of the following: Providing visual and / or analytical metrics of energy consumption of one or more energy consumers; Filtering energy data; Highlighting energy data; Adjusting energy data, or Generating visual and / or analytical metrics of energy consumption by one or more of the following: One or more machines; One or more factories; or One or more vehicles in a fleet.

119. The AI-based platform according to claim 115, wherein, The reporting system is further configured to perform one or more of the following: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Converting, transforming, normalizing, and / or cleaning energy-related data; Parsing energy-related data; Detecting patterns, content, and / or objects in energy-related data; Compressing energy-related data; Streaming energy-related data; Filtering energy-related data; Loading and / or storing energy-related data; Routing and / or transmitting energy-related data; Or Maintaining the security of energy-related data.

120. The AI-based platform according to claim 115, wherein, The reporting system is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, the one or more energy-related events including one or more of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events.

121. The AI-based platform according to claim 115, wherein, At least one of the at least one parameter is based on one or more of the following: One or more public data resources, the one or more public data resources including one or more of the following: Weather data resources; Satellite data resources; Census, population, demographic, and / or psychographic data resources; Market data resources; or E-commerce data resources, or One or more enterprise data resources, the one or more enterprise data resources including one or more of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operational data.

122. The AI-based platform according to claim 115 further includes at least one AI-based model and / or algorithm, wherein, The at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: One or more human labels and / or tags; One or more human interactions with hardware and / or software systems; One or more results; One or more AI-generated training data samples; A supervised learning training process; A semi-supervised learning training process; or A deep learning training process.

123. The AI-based platform according to claim 115, wherein, The governance system is further configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: One or more fixed transmission lines; One or more wireless energy transmission instances; One or more fuel deliveries; or One or more stored energy deliveries.

124. The AI-based platform according to claim 115, wherein, The set of labor standards is associated with at least one activity performed by a mine worker, and transmitting the at least one parameter sensed by the sensor includes transmitting an indication of the worker's performance of the at least one activity sensed by the sensor.

125. The AI-based platform according to claim 115, wherein, The set of labor standards is associated with at least one object associated with the mine worker, and transmitting the at least one parameter sensed by the sensor includes transmitting an indication of the detection of the at least one object by the sensor.

126. The AI-based platform according to claim 115, wherein, The set of labor standards includes thresholds for attributes of the mine, and the reporting system is further configured to transmit a determination based on a comparison of the at least one parameter sensed by the sensor with the thresholds.

127. The AI-based platform according to claim 115, further comprising a compliance recovery system configured to perform at least one compliance recovery action based on a determination that the at least one parameter sensed by the sensor indicates a condition of non-compliance with the set of labor standards.

128. The AI-based platform according to claim 115, further comprising an emergency response system configured to perform at least one emergency response action based on a determination that the at least one parameter sensed by the sensor indicates the occurrence of an emergency event associated with the mine.

129. The AI-based platform according to claim 115, further comprising a sensor configuration system configured to determine a configuration of the sensor to perform sensing of the at least one parameter, wherein, The configuration is based on the mining operation complying with the set of labor standards. The AI-based platform according to claim 129, wherein, The set of labor standards is accessible by the sensor configuration system and specified in natural language, and the sensor configuration system is configured to determine the configuration of the sensor based on a natural language parsing of the set of labor standards.

131. The AI-based platform according to claim 115, further comprising a sensor repair system configured to perform at least one sensor repair measure based on a determination that the sensor has not sensed the at least one parameter, wherein, The at least one sensor repair measure includes one or more of the following: Initiating replacement of the sensor, Initiating a diagnostic operation involving the sensor, Initiating reconfiguration of the sensor to detect the at least one parameter in a different manner, Initiating a request to a mine worker to perform manual sensing of the at least one parameter, or Initiating replacement of the sensor in the mine with at least one other sensor in the mine to sense the at least one parameter.

132. The AI-based platform according to claim 115 further includes a compliance verification system configured to verify that the at least one parameter sensed by the sensor indicates that the mining operation complies with the set of labor standards, wherein, The verification includes one or more of the following: Verifying the calibration of the sensor in the mine, Verifying the at least one parameter sensed by the sensor in the mine based on a comparison of the at least one parameter with at least one parameter sensed by at least one other sensor in the mine, Requesting a mine worker to manually verify the at least one parameter, or Requesting a compliance officer to verify that the at least one parameter indicates that the mining operation complies with the set of labor standards.

133. The AI-based platform according to claim 115 further includes a worker communication interface configured to participate in the communication of the mine workers based on the at least one parameter sensed by the sensor, wherein, The communication is associated with the mining operation complying with the set of labor standards.

134. The AI-based platform according to claim 115 further includes a user interface configured to display a map of the mining operation, wherein, The map includes indicating that the mining operation complies with the set of labor standards based on the at least one parameter sensed by the sensor.

135. The AI-based platform according to claim 115, wherein, The set of labor standards includes a set of work requirements for a worker to perform tasks associated with the mining operation, and the reporting system is further configured to adapt the assignment of the worker to the tasks based on the set of work requirements.

136. The AI-based platform according to claim 115, wherein, The at least one parameter includes a schedule for a worker to perform tasks associated with the mining operation, and the reporting system is further configured to adapt the schedule based on the mining operation complying with the set of labor standards.

137. The AI-based platform according to claim 115, wherein, The reporting system is further configured to initiate at least one protocol in response to the at least one parameter sensed by the sensor, and the at least one protocol is based on adjusting the at least one parameter sensed by the sensor to maintain or restore the mining operation complying with the set of labor standards.

138. The AI-based platform according to claim 115, wherein, The reporting system is further configured to maintain a digital record of the training status and / or certification status of at least one worker associated with at least one task of the mining operation.

139. An AI-based platform for realizing intelligent coordination and management of power and energy, comprising: A set of edge devices, wherein each edge device in the set of edge devices is configured to maintain awareness of carbon generation and / or emissions of at least one entity linked to the set of edge devices and / or managed by the set of edge devices among a set of entities of energy usage.

140. The AI-based platform according to claim 139, wherein, At least one edge device in the set is configured to simulate the carbon generation and / or emissions of at least one entity among the set of entities of energy usage.

141. The AI-based platform according to claim 139, wherein, At least one edge device in the set is configured to execute a set of machine learning algorithms trained on a training dataset of carbon generation data to calculate carbon generation and / or emission metrics of a set of operating entities.

142. The AI-based platform according to claim 139, wherein, At least one edge device in the set is configured to execute a set of machine learning algorithms trained on a training dataset of carbon generation data to calculate carbon generation and / or emission metrics of a set of operating entities.

143. The AI-based platform according to claim 139, wherein, At least one edge device in the set is further configured to adapt data transmission through a network and / or a communication system, wherein the adaptation is based on one or more of the following: Congestion conditions; Delay and / or latency conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of Service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

144. The AI-based platform according to claim 139, further comprising an adaptive energy digital twin, the adaptive energy digital twin representing one or more of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

145. The AI-based platform according to claim 139, further comprising an adaptive energy digital twin, the adaptive energy digital twin being configured to perform one or more of the following: Provide visual and / or analytical metrics of the energy consumption of one or more energy consumers; Filter energy data; Highlight energy data; Adjust energy data, or Generate visual and / or analytical metrics of energy consumption by one or more of the following: One or more machines; One or more factories; or One or more vehicles in a fleet.

146. The AI-based platform according to claim 139, wherein, At least one edge device in the group is further configured to perform one or more of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Convert, transform, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

147. The AI-based platform according to claim 139, wherein, At least one edge device in the group includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of the following: One or more human labels and / or markings; One or more human interactions with a hardware and / or software system; One or more results; One or more AI-generated training data samples; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process.

148. The AI-based platform according to claim 139, wherein, At least one edge device in the group is configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: One or more fixed transmission lines; One or more wireless energy transmission instances; One or more fuel deliveries; or One or more stored energy deliveries.

149. The AI-based platform according to claim 139, wherein, At least one edge device in the group is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events.

150. The AI-based platform according to claim 139, wherein, At least one edge device in the group is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: Off-grid energy generation systems; Off-grid energy storage systems; or Off-grid energy mobilization systems.

151. The AI-based platform according to claim 139, wherein, At least one edge device in the group is further configured to determine the change in carbon generation and / or emission over a period of time based on the comparison of the current metric of carbon generation and / or emission with the historical metric of carbon generation and / or emission.

152. The AI-based platform according to claim 139, wherein, At least one edge device in the group is further configured to determine the carbon generation and / or emission target based on a carbon generation and / or emission strategy.

153. The AI-based platform according to claim 139, wherein, At least one edge device in the group is further configured to: Perform the comparison of the carbon generation and / or emission metric with the carbon generation and / or emission target, and Determine compliance of the carbon generation and / or emissions with the carbon generation and / or emissions strategy based on the comparison.

154. The AI-based platform according to claim 139, wherein, At least one edge device in the group is further configured to determine an environmental impact of the carbon generation and / or emissions based on carbon generation and / or emissions metrics and carbon generation and / or emissions targets.

155. The AI-based platform according to claim 139, wherein, The carbon generation and / or emissions are associated with a group of activities, and at least one edge device in the group is further configured to allocate at least a portion of the carbon generation and / or emissions to at least one activity in the group of activities.

156. The AI-based platform according to claim 139, wherein, At least one edge device in the group is further configured to associate at least one metric with the carbon generation and / or emissions metrics and carbon generation and / or emissions targets, where the metric includes one or more of the following: The date, time, and / or time period of the carbon generation and / or emissions, The source location of the carbon generation and / or emissions, The transport direction and / or speed of the carbon generation and / or emissions, The affected location of the carbon generation and / or emissions, The physical metric of the carbon generation and / or emissions, The chemical composition of the carbon generation and / or emissions, Weather patterns occurring in the area associated with the carbon generation and / or emissions, Wildlife populations in the area associated with the carbon generation and / or emissions, or Human activities affected by the carbon generation and / or emissions.

157. The AI-based platform according to claim 139, wherein, At least one edge device in the group is further configured to transmit an alert associated with the carbon generation and / or emissions based on a comparison of the carbon generation and / or emissions metrics with an alert threshold associated with the carbon generation and / or emissions.

158. The AI-based platform according to claim 139, wherein, At least one edge device in the group is further configured to adjust an activity associated with the carbon generation and / or emissions based on the carbon generation and / or emissions metrics, and the adjustment modifies a future state of the carbon generation and / or emissions.

159. The AI-based platform according to claim 139, wherein, At least one edge device in the group of edge devices is further configured to maintain awareness by detecting a measurement of carbon generation and / or emissions associated with at least one entity in the group of energy-using entities based on a detection interval.

160. The AI-based platform according to claim 139, wherein, At least one edge device in the group of edge devices is further configured to maintain awareness by generating at least one local report and / or alert, and the at least one local report and / or alert is associated with a carbon generation and / or emissions pattern associated with the at least one entity in the group of energy-using entities.

161. The AI-based platform according to claim 139, wherein, At least one edge device in the group of edge devices is further configured to change an operation of one or more devices and / or processes associated with at least one entity in the group of energy-using entities, and the change of the operation is based on at least one measurement of carbon generation and / or emissions associated with at least one entity in the group of energy-using entities.

162. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: A digital twin, the digital twin being updated by a data collection system that dynamically maintains a set of historical, current, and / or predicted energy demand parameters for a set of fixed entities and a set of mobile entities within a domain, wherein the update of the digital twin is based on the set of energy demand parameters.

163. The AI-based platform according to claim 162, wherein, A set of operating entities is controlled via a set of edge networking devices linked to the set of operating entities, and the energy demand parameters are based on one or more of the following: A set of current summary data derived from the demands of the set of operating entities, wherein the set of operating entities is controlled via a set of edge networking devices linked to the set of operating entities, A set of historical summary data derived from the demands of the set of operating entities, wherein the set of operating entities is controlled via a set of edge networking devices linked to the set of operating entities, or A set of simulated summary data derived from the demands of the set of operating entities.

164. The AI-based platform according to claim 162, wherein, The data collection system is further configured to adapt to data transmission via a network and / or a communication system, wherein the adaptation is based on one or more of the following: Congestion conditions; Latency and / or delay conditions; Packet loss conditions; Error rate conditions; Transportation cost conditions; Quality of Service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

165. The AI-based platform according to claim 162, wherein, The digital twin represents one or more of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

166. The AI-based platform according to claim 162, wherein, The digital twin is further configured to perform one or more of the following: Provide visual and / or analytical metrics of the energy consumption of one or more energy consumers; Filter energy data; Highlight energy data; Adjust energy data, or Generate visual and / or analytical metrics of energy consumption by one or more of the following: One or more machines; One or more factories; or One or more vehicles in a fleet.

167. The AI-based platform according to claim 162, wherein, At least one energy demand parameter is based on one or more of the following: On one or more public data resources, the one or more public data resources including one or more of the following: Weather data resources; Satellite data resources; Census, population, demographic, and / or psychographic data resources; Market data resources; or E-commerce data resources, or One or more enterprise data resources, the one or more enterprise data resources including one or more of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data.

168. The AI-based platform according to claim 162, wherein, The digital twin includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of the following: One or more human labels and / or markings; One or more human interactions with a hardware and / or software system; One or more results; One or more AI-generated training data samples; A supervised learning training process; A semi-supervised learning training process; or A deep learning training process.

169. The AI-based platform according to claim 162, wherein, The digital twin is further configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: One or more fixed transmission lines; One or more wireless energy transmission instances; One or more fuel deliveries; or One or more stored energy deliveries. The AI-based platform according to claim 169, wherein, The digital twin is further configured to adjust the delivery of energy to the one or more consumption points based on an energy delivery and / or consumption strategy.

171. The AI-based platform according to claim 169, wherein, The digital twin is further configured to determine the carbon generation and / or emission effect of delivering energy to the one or more consumption points.

172. The AI-based platform according to claim 169, wherein, The digital twin is further configured to adjust the delivery of energy to the one or more consumption points based on the probability of insufficient available energy at the one or more consumption points and the consequences of insufficient available energy at the one or more consumption points.

173. The AI-based platform according to claim 169, wherein, The digital twin is further configured to determine the delivery of energy to the one or more consumption points based on a comparison of the energy availability of each of two or more energy sources, where the comparison includes one or more of the following: The current and / or future amount of energy stored by at least one of the two or more energy sources, The current and / or future resource consumption associated with obtaining, storing, and / or delivering energy by at least one of the two or more energy sources, or The current and / or future demand of other energy consumers for at least one of the two or more energy sources.

174. The AI-based platform according to claim 162, wherein, The digital twin is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, and the one or more energy-related events include one or more of the following: Energy purchase and / or sale events; Service fees associated with energy purchase and / or sale events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events.

175. The AI-based platform according to claim 162, wherein, The digital twin is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: An off-grid energy generation system; An off-grid energy storage system; or An off-grid energy mobilization system.

176. The AI-based platform according to claim 162, wherein, The AI-based platform is configured to measure the performance of the digital twin based on a prediction increment, and the prediction increment is based on a comparison of a prediction generated by the digital twin based on the set of energy demand parameters with a measurement corresponding to the prediction within the data collection system.

177. The AI-based platform according to claim 176, wherein, The AI-based platform is configured to update the digital twin based on the prediction increment, and the update includes one or more of the following: Retraining the digital twin based on the prediction increment, Adjusting the prediction correction applied to the prediction of the digital twin based on the prediction increment, Supplementing the digital twin with at least one other trained machine learning model, or Replacing the digital twin with an alternative digital twin.

178. The AI-based platform according to claim 162, wherein, The digital twin is also configured to generate: A prediction based on at least one energy demand parameter, and An indication of the impact of at least one energy demand parameter on the prediction. The AI-based platform according to claim 162, wherein, The digital twin is also configured to determine one or more modifications to the set of energy demand parameters to improve future predictions of the digital twin, where the one or more modifications include one or more of the following: One or more additional historical, current, and / or predicted energy demand parameters associated with a set of stationary entities and a set of mobile entities within the domain, or One or more modifications to one or more of the historical, current, and / or predicted energy demand parameters associated with the set of stationary entities and the set of mobile entities within the domain. The AI-based platform according to claim 162, wherein, The digital twin is also configured to coordinate the delivery of energy to one or more consumption points based on one or more entity parameters received from at least one entity within the set of stationary entities and / or the set of mobile entities within the domain, and the one or more entity parameters include one or more of the following: The current and / or future energy state of the at least one entity, The current and / or future energy consumption of the at least one entity, or The current and / or future activities associated with energy consumption performed by the at least one entity.

181. The AI-based platform according to claim 162, wherein, The digital twin is also configured to transmit a request to adjust one or more entity parameters associated with the at least one entity to at least one entity within the set of stationary entities and / or the set of mobile entities within the domain, and the one or more entity parameters include one or more of the following: The current and / or future energy state of the at least one entity, The current and / or future energy consumption of the at least one entity, or The current and / or future activities associated with energy consumption performed by the at least one entity.

182. The AI-based platform according to claim 162, wherein, The digital twin is also configured to: Perform a simulation of at least one process of at least one physical machine associated with one or both of the set of stationary entities or the set of mobile entities, and Based on the simulation, output at least one energy demand parameter generated by the at least one process.

183. The AI-based platform according to claim 162, wherein, The digital twin is associated with at least one physical machine, the at least one physical machine is associated with one or both of the set of stationary entities or the set of mobile entities, and the digital twin is updated by the data collection system to generate an output of a process corresponding to an updated detection of the output of the process performed by the at least one physical machine.

184. The AI-based platform according to claim 162, wherein, The digital twin is updated by the data collection system based on a strategy for power savings and energy consumption associated with the set of energy demand parameters.

185. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: A set of modular distributed energy systems that can be configured based on local demand requirements.

186. The AI-based platform according to claim 185, wherein, Predicting the local demand requirements by a demand prediction algorithm operating on a set of edge networking devices linked to a set of systems consuming energy.

187. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system in the group is configured by the AI-based platform to be located near the location and time of demand.

188. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system in the group is configured by the AI-based platform to be located based on the location and type required by local demand.

189. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system in the group is configured by the AI-based platform to generate energy at the local demand point. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system in the group is configured by the AI-based platform to deliver a modular power generation system to the demand location.

191. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system in the group is configured by the AI-based platform to route the energy delivery of a group of energy delivery facilities to the demand location. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system in the group is coordinated by the AI-based platform to store energy near the location and time of demand. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system in the group is also configured to adapt to data transmission through a network and / or communication system, where the adaptation is based on one or more of the following: Congestion conditions; Latency and / or delay conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

194. The AI-based platform according to claim 185 further includes an adaptive energy digital twin, which represents one or more of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

195. The AI-based platform according to claim 185 further includes an adaptive energy digital twin, which is configured to perform one or more of the following: Provide visual and / or analytical metrics of the energy consumption of one or more energy consumers; Filter energy data; Highlight energy data; Adjust energy data, or Generate visual and / or analytical metrics of energy consumption through one or more of the following: One or more machines; One or more factories; or One or more vehicles in a fleet. The AI-based platform according to claim 185, wherein At least one modular distributed energy system is also configured to perform one or more of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Convert, transform, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

197. The AI-based platform according to claim 185, wherein, The local demand requirements are based on one or more of the following: On one or more public data resources, the one or more public data resources include one or more of the following: Weather data resources; Satellite data resources; Census, population, demographic, and / or psychographic data resources; Market data resources; or E-commerce data resources, or One or more enterprise data resources, where the one or more enterprise data resources include one or more of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data. The AI-based platform according to claim 185 further includes at least one AI-based model and / or algorithm, wherein, The at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on one or more of the following: One or more human labels and / or markings; One or more human interactions with a hardware and / or software system; One or more results; One or more AI-generated training data samples; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system is configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: One or more fixed transmission lines; One or more wireless energy transmission instances; One or more fuel deliveries; or One or more stored energy deliveries. The AI-based platform according to claim 199, wherein, The first system of the modular distributed energy system is configured to communicate with the second system of the modular distributed energy system to coordinate the delivery of energy to the one or more consumption points by adjusting the energy generation, storage, delivery, and / or consumption in one or both of the first system or the second system. The AI-based platform according to claim 199, wherein, At least one modular distributed energy system is configured to adjust the delivery of energy to the one or more consumption points based on a carbon generation and / or emissions strategy. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, where the one or more energy-related events include one or more of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: Off-grid energy generation systems; Off-grid energy storage systems; or Off-grid energy mobilization systems. The AI-based platform according to claim 185, wherein, At least one modular distributed energy system is associated with a digital twin, which is configured to model and / or predict one or more attributes and / or operations of the at least one modular distributed energy system. The AI-based platform according to claim 185, wherein, The set of modular distributed energy systems can be configured to change the amount of reserved capacity to accommodate an energy demand pattern associated with the local demand requirements. The AI-based platform according to claim 185, wherein, The set of modular distributed energy systems can be configured to change the location of energy supply and / or access resources based on the measurement and / or prediction of local demand requirements. The AI-based platform according to claim 185, wherein, The set of modular distributed energy systems can be configured to change the energy production schedule based on measurements and / or predictions of local demand requirements. The AI-based platform according to claim 185, wherein, The set of modular distributed energy systems can be configured to change the resource allocation associated with the set of modular distributed energy systems, and the allocation is based on a subset of the local demand requirements.

209. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: An artificial intelligence system configured to: Perform an analysis of energy patterns associated with an operational process involving a set of resources, the set of resources being at least partially independent of the power grid; And Output a set of operating parameters to provide energy generation, storage, and / or consumption to achieve the operational process, wherein the set of operating parameters is based on the analysis. The AI-based platform according to claim 209, wherein, At least one of the set of operating parameters is the generation output level of distributed energy generation resources. The AI-based platform according to claim 209, wherein At least one of the set of operating parameters is the target storage level of distributed energy storage resources. The AI-based platform according to claim 209, wherein, At least one of the set of operating parameters is the delivery time of distributed energy delivery resources. The AI-based platform according to claim 209, wherein, The artificial intelligence system is further configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on one or more of the following: Congestion conditions; Delay and / or latency conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

214. The AI-based platform according to claim 209, further comprising an adaptive energy digital twin representing one or more of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

215. The AI-based platform according to claim 209, further comprising an adaptive energy digital twin configured to perform one or more of the following: Provide visual and / or analytical metrics of the energy consumption of one or more energy consumers; Filter energy data; Highlight energy data; Adjust energy data, or Generate visual and / or analytical metrics of energy consumption through one or more of the following: One or more machines; One or more factories; or One or more vehicles in a fleet.

216. The AI-based platform according to claim 209, wherein, The artificial intelligence system is further configured to perform one or more of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Transform, convert, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

217. The AI-based platform according to claim 209, wherein, At least one operating parameter is based on one or more of the following: One or more common data resources, the one or more common data resources including one or more of the following: Weather data resources; Satellite data resources; Census, population, demographic, and / or psychographic data resources; Market data resources; or E-commerce data resources, or One or more enterprise data resources, the one or more enterprise data resources including one or more of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data. The AI-based platform according to claim 209, wherein, The artificial intelligence system is trained based on a training data set, and the training data set is based on one or more of the following: One or more human labels and / or markings; One or more human interactions with a hardware and / or software system; One or more results; One or more AI-generated training data samples; Supervised learning training processes; Semi-supervised learning training processes; or Deep learning training processes. The AI-based platform according to claim 209, wherein, The artificial intelligence system is configured to coordinate the delivery of energy to one or more consumption points, and the energy delivery includes one or more of the following: One or more fixed transmission lines; One or more wireless energy transmission instances; One or more fuel deliveries; or One or more stored energy deliveries. The AI-based platform according to claim 209, wherein, The artificial intelligence system is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, the one or more energy-related events including one or more of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events.

221. The AI-based platform according to claim 209, wherein, The artificial intelligence system is deployed in an off-grid environment, and the off-grid environment includes one or more of the following: Off-grid energy generation systems; Off-grid energy storage systems; or Off-grid energy mobilization systems. The AI-based platform according to claim 209, wherein, The artificial intelligence system is further configured to determine the environmental impact of carbon generation and / or emissions associated with the operation process on the area associated with the operation process.

223. The AI-based platform according to claim 209, wherein, The artificial intelligence system is further configured to evaluate the compliance of the operation process with one or both of the following: Carbon generation and / or emission strategies, or A set of labor standards related to the operation process. The AI-based platform according to claim 209, wherein, The artificial intelligence system is further configured to adjust the set of operating parameters based on one or both of the following to provide energy generation, storage, and / or consumption associated with the operation process: Carbon generation and / or emission strategies, or A set of labor standards related to the operation process. The AI-based platform according to claim 209, wherein, The artificial intelligence system is further configured to transmit a message to at least one edge device in a set of edge devices associated with the operation process, and the message includes a request to adjust at least one operation of the at least one edge device based on the set of operating parameters. The AI-based platform according to claim 209, wherein, The artificial intelligence system is further configured to receive metrics of the current and / or predicted energy state of at least one edge device from among a group of edge devices associated with the operation process, and the set of operation parameters is based on the metrics of the current and / or predicted energy state of the at least one edge device. The AI-based platform according to claim 209, wherein, The artificial intelligence system is further configured to determine the set of operation parameters based on the output of a digital twin that represents at least one edge device from among a group of edge devices associated with the operation process, and the output of the digital twin indicates the current and / or predicted energy state of the at least one edge device.

228. The AI-based platform according to claim 209, wherein, The artificial intelligence system is further configured to coordinate a group of modular distributed energy systems to generate, store, and / or deliver energy, wherein the coordination is based on the set of operation parameters and local demand requirements. The AI-based platform according to claim 209, wherein, The analysis of the energy pattern associated with the operation process includes analyzing the availability of backup power based on a fault in at least a portion of the power grid. The AI-based platform according to claim 209, wherein, The analysis of the energy pattern associated with the operation process includes an analysis of at least one auxiliary function related to the group of resources, and the set of operation parameters includes at least one operation parameter related to the at least one auxiliary function.

231. An AI-based platform for realizing intelligent coordination and management of power and energy, comprising: A policy and governance engine configured to deploy a set of rules and / or policies that govern a set of energy generation, storage, and / or consumption workloads, wherein the rules and / or policies are associated with the configuration of a group of edge devices that operate in local data communication with a group of energy generation facilities, energy storage facilities, energy delivery facilities, or energy consumption systems. The AI-based platform according to claim 231, wherein When configured in the policy and governance engine, a policy associated with an energy generation instruction is automatically applied by at least one of the edge devices to control the energy generation of at least one energy generation system controlled by the edge device.

233. The AI-based platform according to claim 231, wherein, When configured in the policy and governance engine, a policy associated with an energy consumption instruction is automatically applied by at least one of the edge devices to control the energy consumption of at least one energy consumption system controlled by the edge device.

234. The AI-based platform according to claim 231, wherein, When configured in the policy and governance engine, a policy associated with an energy delivery instruction is automatically applied by at least one edge device to control the energy delivery of at least one energy delivery system controlled by the edge device. The AI-based platform according to claim 231, wherein, When configured in the policy and governance engine, a policy associated with an energy storage instruction is automatically applied by at least one edge device to control the energy storage of at least one energy storage system controlled by the edge device. The AI-based platform according to claim 231, wherein, The policy and governance engine is configured to operate on a stored set of policy templates to configure policies. The AI-based platform according to claim 231, wherein, Automatically generate a set of recommended policies based on a dataset of historical policies, a dataset representing the operation state and / or configuration of a group of distributed energy, and a set of historical results for presentation in the policy and governance engine. The AI-based platform according to claim 231, wherein, The policy and governance engine is also configured to adjust the rules and / or policies based on at least one context factor, and the at least one context factor includes at least one of the following: Historical data of energy transactions; At least one operational factor; At least one market factor; At least one expected market behavior; or At least one expected customer behavior. The AI-based platform according to claim 231, wherein, The policy and governance engine is also configured to accommodate data transmission over a network and / or communication system, wherein the accommodation is based on at least one of the following: Congestion conditions; Latency and / or delay conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

240. The AI-based platform according to claim 231 further includes an adaptive energy digital twin that represents at least one of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

241. The AI-based platform according to claim 231 further includes an adaptive energy digital twin that is configured to perform at least one of the following: Provide visual and / or analytical metrics of the energy consumption of at least one energy consumer, Filter energy data; Highlight energy data; or Adjust energy data.

242. The AI-based platform according to claim 231 further includes an adaptive energy digital twin that is configured to generate visual and / or analytical metrics of energy consumption through at least one of the following: At least one machine; At least one factory; or At least one vehicle in a fleet.

243. The AI-based platform according to claim 231, wherein, The policy and governance engine is also configured to perform at least one of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Convert, transform, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

244. The AI-based platform according to claim 231, wherein, At least one rule and / or policy is based on at least one common data resource, and the at least one common data resource includes at least one of the following: Weather data resources; Satellite data resources; Census, population, demographic, and / or psychographic data resources; Market data resources; or E-commerce data resources. The AI-based platform according to claim 231, wherein, At least one rule and / or policy is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operational data. The AI-based platform according to claim 231 further includes at least one AI-based model and / or algorithm, wherein, The at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on at least one of the following: At least one human label and / or annotation; At least one human interaction with a hardware and / or software system; At least one result; At least one AI-generated training data sample; A supervised learning training process; A semi-supervised learning training process; or A deep learning training process.

247. The AI-based platform according to claim 231, wherein, The policy and governance engine is configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: At least one fixed transmission line; At least one wireless energy transmission instance; At least one fuel delivery; or At least one stored energy delivery.

248. The AI-based platform according to claim 231, wherein, The policy and governance engine is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events.

249. The AI-based platform according to claim 231, wherein, The policy and governance engine is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: An off-grid energy generation system; An off-grid energy storage system; or An off-grid energy mobilization system. The AI-based platform according to claim 231, wherein, The policy and governance engine is further configured to generate and / or execute at least one smart contract, wherein each of the at least one smart contracts applies the rules and / or policies to at least one energy-related transaction.

251. The AI-based platform according to claim 231, wherein, The set of rules and / or policies is based on at least one goal associated with the set of energy generation, storage, and / or consumption workloads, and the policy and governance engine is further configured to deploy updates to the set of rules and / or policies to the set of edge devices based on the goal.

252. The AI-based platform according to claim 231, wherein, The policy and governance engine is further configured to deploy at least one instruction to the set of edge devices to adapt at least one operating parameter associated with at least one industrial machine and / or industrial process controlled by the set of edge devices.

253. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: A set of edge devices configured to: Communicate with at least one energy generation facility, energy storage facility, and / or energy consumption system, and Automatically execute a set of pre-configured policies that manage the energy generation, energy storage, or energy consumption of the corresponding energy generation facility, energy storage facility, or energy consumption system.

254. The AI-based platform according to claim 253, wherein, The automatically executed policies are a set of context policies that are adjusted based on the current state of a set of energy generation entities in the energy grid. The AI-based platform according to claim 253, wherein, The automatically-executed policy is a set of context policies that are adjusted based on the current states of a set of energy generation entities in an energy generation environment, the energy generation environment including an energy grid and a set of distributed energy sources operating independently of the energy grid.

256. The AI-based platform according to claim 253, wherein, The automatically-executed policy is a set of context policies that are adjusted based on the current states of a set of energy storage entities in an energy grid. The AI-based platform according to claim 253, wherein, The automatically-executed policy is a set of context policies that are adjusted based on the current states of a set of energy storage entities in an energy storage environment, the energy storage environment including an energy grid and a set of distributed energy resources operating independently of the energy grid, wherein the automatically-executed policy is a set of context policies that are adjusted based on the current states of a set of energy delivery entities in an energy grid.

258. The AI-based platform according to claim 253, wherein, The automatically-executed policy is a set of context policies that are adjusted based on the current states of a set of energy transmission entities in an energy transmission environment, the energy transmission environment including an energy grid and a set of distributed energy resources operating independently of the energy grid.

259. The AI-based platform according to claim 253, wherein, The automatically-executed policy is a set of context policies that are adjusted based on the current states of a set of energy consumption entities that consume energy from an energy grid. The AI-based platform according to claim 253, wherein, The automatically-executed policy is a set of context policies that are adjusted based on the current states of a set of energy consumption entities that consume energy from an energy grid and from a set of distributed energy resources operating independently of the energy grid.

261. The AI-based platform according to claim 253, wherein, The set of edge devices is further configured to adjust the set of pre-configured policies based on at least one context factor, and the at least one context factor includes at least one of the following: Historical data of energy transactions; At least one operational factor; At least one market factor; At least one expected market behavior; or At least one expected customer behavior.

262. The AI-based platform according to claim 253, wherein, At least one edge device is further configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on at least one of the following: Congestion conditions; Delay and / or latency conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

263. The AI-based platform according to claim 253, further comprising an adaptive energy digital twin, the adaptive energy digital twin representing at least one of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

264. The AI-based platform according to claim 253, further comprising an adaptive energy digital twin, the adaptive energy digital twin being configured to perform at least one of the following: Provide visual and / or analytical metrics of the energy consumption of at least one energy consumer, Filter energy data; Highlight energy data; or Adjust energy data.

265. The AI-based platform according to claim 253, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by at least one of: At least one machine; At least one factory; or At least one vehicle in a fleet.

266. The AI-based platform according to claim 253, wherein, At least one edge device is further configured to perform at least one of: Extract energy-related data; Detect and / or correct errors in energy-related data; Convert, transform, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

267. The AI-based platform according to claim 253, wherein, At least one pre-configured policy is based on at least one common data resource, and the at least one common data resource includes at least one of: Weather data resource; Satellite data resource; Census, population, demographic, and / or psychographic data resource; Market data resource; or E-commerce data resource.

268. The AI-based platform according to claim 253, wherein, At least one pre-configured policy is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data.

269. The AI-based platform according to claim 253, wherein, At least one of the edge devices includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on at least one of: At least one human label and / or annotation; At least one human interaction with a hardware and / or software system; At least one result; At least one AI-generated training data sample; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process. The AI-based platform according to claim 253, wherein, At least one edge device is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of: At least one fixed transmission line; At least one wireless energy transmission instance; At least one fuel delivery; or At least one stored energy delivery. The AI-based platform according to claim 253, wherein, At least one edge device is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events. The AI-based platform according to claim 253, wherein, At least one edge device is deployed in an off-grid environment, and the off-grid environment includes at least one of: Off-grid energy generation system; An off-grid energy storage system; or An off-grid energy dispatch system. The AI-based platform according to claim 253, wherein, The set of edge devices is further configured to: Determine at least one energy availability pattern based on communication with the at least one energy generation facility, energy storage facility, and / or energy consumption system, and Update the execution of the set of pre-configured policies based on the at least one pattern.

274. The AI-based platform according to claim 253, wherein, At least one edge device in the set of edge devices is configured to manage the operation of an industrial facility, and the set of pre-configured policies is based on at least one energy goal associated with the industrial facility. The AI-based platform according to claim 253, wherein, The at least one energy generation facility, energy storage facility, and / or energy consumption system is located in a geographical area, and the set of pre-configured policies is based on at least one energy goal associated with the geographical area.

276. The AI-based platform according to claim 253, wherein, The set of edge devices is configured to automatically execute the set of pre-configured policies by adjusting at least one of the allocation of energy resources associated with the at least one energy generation facility, energy storage facility, and / or energy consumption system or the schedule of processes performed by the at least one energy generation facility, energy storage facility, and / or energy consumption system.

277. An AI-based platform for realizing intelligent coordination and management of electricity and energy, comprising: A machine learning system that is trained on a set of energy intelligent data and deployed on edge devices, wherein the machine learning system is configured to receive additional training performed by the edge devices to improve energy management. The AI-based platform according to claim 277, wherein, The energy management includes managing the energy generation of a set of distributed energy generation resources. The AI-based platform according to claim 277, wherein, The energy management includes managing the energy storage of a set of distributed energy storage resources. The AI-based platform according to claim 277, wherein, The energy management includes managing the energy delivery of a set of distributed energy delivery resources.

281. The AI-based platform according to claim 277, wherein, The energy management includes managing the energy consumption of a set of distributed energy consumption resources.

282. The AI-based platform according to claim 277, wherein, The energy management is based on a set of rules and / or policies associated with the edge devices and a set of energy generation facilities, energy storage facilities, energy delivery facilities, or energy consumption systems.

283. The AI-based platform according to claim 277, wherein, The machine learning system is further configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on at least one of the following: Congestion conditions; Latency and / or delay conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

284. The AI-based platform according to claim 277, further comprising an adaptive energy digital twin that represents at least one of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

285. The AI-based platform according to claim 277, further comprising an adaptive energy digital twin that is configured to perform at least one of the following: Provide visual and / or analytical metrics of the energy consumption of at least one energy consumer, Filter energy data; Highlight energy data; or Adjust energy data.

286. The AI-based platform according to claim 277, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption through at least one of the following: At least one machine; At least one factory; or At least one vehicle in a fleet. The AI-based platform according to claim 277, wherein, The machine learning system is further configured to perform at least one of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Transform, convert, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

288. The AI-based platform according to claim 277, wherein, The energy intelligent data is based on at least one common data resource, and the at least one common data resource includes at least one of the following: Weather data resource; Satellite data resource; Census, population, demographic, and / or psychographic data resource; Market data resource; or E-commerce data resource. The AI-based platform according to claim 277, wherein, The energy intelligent data is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operation data. The AI-based platform according to claim 277, wherein, The machine learning system is also trained based on a training dataset, and the training dataset is based on at least one of the following: At least one human label and / or annotation; At least one human interaction with a hardware and / or software system; At least one result; At least one AI-generated training data sample; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process. The AI-based platform according to claim 277, wherein, The machine learning system is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: At least one fixed transmission line; At least one wireless energy transmission instance; At least one fuel delivery; or At least one stored energy delivery. The AI-based platform according to claim 277, wherein, The machine learning system is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: Energy procurement and / or sales event; Service fees associated with energy procurement and / or sales events; Energy consumption event; Energy generation event; Energy distribution event; Energy storage event; Carbon emission generation event; Carbon emission reduction event; Renewable energy credit event; Pollution generation event; or Pollution reduction event. The AI-based platform according to claim 277, wherein, The edge device is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: Off-grid energy generation system; Off-grid energy storage system; or Off-grid energy mobilization system. The AI-based platform according to claim 277, wherein, The edge device is located near at least one entity that generates, stores, transports, and / or uses energy. The AI-based platform according to claim 277, wherein, The edge device provides information about the energy status and / or energy flow of at least one entity that generates, stores, transports, and / or uses energy. The AI-based platform according to claim 277, wherein, The edge device includes and / or governs at least one sensor in a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transport, and / or use energy. The AI-based platform according to claim 277, wherein, The edge device is associated with a situation and / or environment, and the edge device is further configured to perform additional training of the machine learning system in response to changes in the situation and / or environment. The AI-based platform according to claim 277, wherein, The edge device is further configured to perform additional training of the machine learning system based on the machine learning system's determination of model drift. The AI-based platform according to claim 277, wherein, The additional training is based on a set of energy intelligence data on which the machine learning system was initially trained and additional energy intelligence data on which the machine learning system has not been trained. The AI-based platform according to claim 277, wherein, The additional training includes adding the machine learning system to a set that includes at least one other artificial intelligence system. The AI-based platform according to claim 277, wherein, The set of energy intelligence data is based on at least one energy-related policy and / or rule, and the additional training is based on changes in the at least one energy-related policy and / or rule.

302. An AI-based platform for achieving intelligent coordination and management of electricity and energy, comprising: A set of edge devices, the set of edge devices including a set of artificial intelligence systems configured to: Process data processed by the edge device; And Based on the data, determine a mix of energy generation, storage, transport, and / or consumption characteristics of a set of systems in local communication with the edge device, and output a data set representing the composition ratios of the mix.

303. The AI-based platform according to claim 302, wherein, The output data set indicates the fraction of energy generated by the energy grid and the fraction of energy generated by a set of distributed energy sources operating independently of the energy grid. The AI-based platform according to claim 302, wherein, The output data set indicates the fraction of energy generated by renewable energy sources and the fraction of energy generated by non-renewable resources. The AI-based platform according to claim 302, wherein The output data set indicates the fraction of energy generation by type for each of a series of time intervals.

306. The AI-based platform according to claim 302, wherein, The output data set indicates the carbon generation associated with the energy generation of each energy type in the energy mix during each of a series of time intervals. The AI-based platform according to claim 302, wherein The output data set indicates the carbon emissions associated with the energy generation of each energy type in the energy mix during each of a series of time intervals. The AI-based platform according to claim 302, wherein, At least one edge device is further configured to adapt data transmission over a network and / or communication system, where the adaptation is based on at least one of: Congestion conditions; Delay and / or latency conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

309. The AI-based platform according to claim 302, further comprising an adaptive energy digital twin representing at least one of: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority status.

310. The AI-based platform according to claim 302, further comprising an adaptive energy digital twin configured to perform at least one of the following: Provide visual and / or analytical metrics of energy consumption of at least one energy consumer, Filter energy data; Highlight energy data; or Adjust energy data.

311. The AI-based platform according to claim 302, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by at least one of the following: At least one machine; At least one factory; or At least one vehicle in a fleet. The AI-based platform according to claim 302, wherein, At least one edge device is further configured to perform at least one of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Convert, transform, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data. The AI-based platform according to claim 302, wherein, The data is based on at least one common data resource, and the common data resource includes at least one of the following: Weather data resource; Satellite data resource; Census, population, demographic, and / or psychographic data resource; Market data resource; or E-commerce data resource. The AI-based platform according to claim 302, wherein, The data is based on at least one enterprise data resource, and the enterprise data resource includes at least one of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data. The AI-based platform according to claim 302, wherein, At least one of the edge devices includes at least one AI-based model and / or algorithm, and the at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on at least one of the following: At least one human label and / or annotation; At least one human interaction with a hardware and / or software system; At least one result; At least one AI-generated training data sample; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process. The AI-based platform according to claim 302, wherein, At least one edge device is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: At least one fixed transmission line; At least one wireless energy transmission instance; At least one fuel delivery; or At least one stored energy delivery. The AI-based platform according to claim 302, wherein, At least one edge device is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events. The AI-based platform according to claim 302, wherein, At least one edge device is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: Off-grid energy generation system; Off-grid energy storage system; or Off-grid energy mobilization system. The AI-based platform according to claim 302, wherein, At least a portion of the set of edge devices is located near at least one entity that generates, stores, transports, and / or uses energy. The AI-based platform according to claim 302, wherein, The set of edge devices provides information on the energy status and / or energy flow of at least one entity that generates, stores, transports, and / or uses energy.

321. The AI-based platform according to claim 302, wherein, The set of edge devices includes and / or manages at least one sensor from a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transport, and / or use energy.

322. The AI-based platform according to claim 302, wherein, The mix of energy generation, storage, transport, and / or consumption characteristics is based on at least one energy demand requirement associated with the set of edge devices. The AI-based platform according to claim 302, wherein, The mix of energy generation, storage, transport, and / or consumption characteristics is based on the priority of energy collection, storage, transportation, and / or use associated with each energy source associated with the set of edge devices.

324. The AI-based platform according to claim 302, wherein, The mix of energy generation, storage, transport, and / or consumption characteristics is based on a schedule for storage, transportation, and / or use associated with each energy source associated with the set of edge devices.

325. An AI-based platform for achieving intelligent coordination and management of electricity and energy, comprising: A data processing system configured to fuse at least one entity that generates, stores, transports, or consumes grid datasets in an energy grid entity with at least one entity that generates, stores, transports, and / or consumes datasets in an off-grid energy entity.

326. The AI-based platform according to claim 325, wherein, The data processing system is configured to automatically time-align energy grid entity data with off-grid energy entity data. The AI-based platform according to claim 325, wherein, The data processing system is configured to automatically collect off-grid energy entity sensor data from a set of edge devices and control a set of off-grid energy entities via the set of edge devices.

328. The AI-based platform according to claim 325, wherein, The data processing system is configured to automatically normalize the energy grid entity data and the off-grid energy entity data to present the data according to a set of common units. The AI-based platform according to claim 325, wherein, The data processing system is further configured to adapt data transmission through a network and / or communication system, wherein the adaptation is based on at least one of the following: Congestion conditions; Delay and / or latency conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of Service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

330. The AI-based platform according to claim 325, further comprising an adaptive energy digital twin representing at least one of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority status.

331. The AI-based platform according to claim 325, further comprising an adaptive energy digital twin configured to perform at least one of the following: Provide visual and / or analytical metrics of the energy consumption of at least one energy consumer, Filter energy data; Highlight energy data; or Adjust energy data.

332. The AI-based platform according to claim 325, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by at least one of the following: At least one machine; At least one factory; or At least one vehicle in a fleet.

333. The AI-based platform according to claim 325, wherein, The data processing system is further configured to perform at least one of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Convert, transform, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data. The AI-based platform according to claim 325 further includes at least one AI-based model and / or algorithm, wherein, The at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on at least one of the following: At least one human label and / or annotation; At least one human interaction with a hardware and / or software system; At least one result; At least one AI-generated training data sample; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process. The AI-based platform according to claim 325, wherein, The data processing system is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: At least one fixed transmission line; At least one wireless energy transmission instance; At least one fuel delivery; or At least one stored energy delivery. The AI-based platform according to claim 325, wherein, The data processing system is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: Energy purchase and / or sale events; Service fees associated with energy purchase and / or sale events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events. The AI-based platform according to claim 325, wherein, The at least one entity of the off-grid energy generation, storage, and / or consumption dataset is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: Off-grid energy generation systems; Off-grid energy storage systems; or Off-grid energy mobilization systems.

338. The AI-based platform according to claim 325, wherein, The data processing system is further configured to intelligently coordinate and manage electricity and / or energy based on a dataset of energy generation, storage, and / or consumption data of a set of infrastructure assets, and the dataset is at least partially generated by a set of sensors included in and / or managed by a set of edge devices. The AI-based platform according to claim 325, wherein, The data processing system is further configured to manage at least one of the following: generating energy by a set of distributed energy generation resources, storing energy by a set of distributed energy storage resources, delivering energy by a set of distributed energy delivery resources, or consuming energy by a set of distributed energy consumption resources. The AI-based platform according to claim 325, wherein, The data processing system is further configured to intelligently coordinate and manage the electricity and / or energy of a set of entities, where the set of entities includes at least one of the following: weather data resources; satellite data resources; census, population, demographic, and / or psychographic data resources; market data resources; or e-commerce data resources.

341. The AI-based platform according to claim 325, wherein, The data processing system is further configured to execute at least one algorithm that simulates the energy consumption of at least one entity, where the simulation is based on a dataset that includes alternative state or event parameters of at least one entity, the alternative state or event parameters reflect alternative consumption scenarios, and the algorithm accesses a demand response model that describes how energy demand responds to changes in energy prices or the prices of operations or activities that consume energy.

342. The AI-based platform according to claim 325, wherein, The data processing system includes a policy and governance engine configured to deploy a set of rules and / or policies to at least one edge device that communicates locally with at least one entity, and the edge device is configured to govern at least one entity based on the rules and / or policies.

343. The AI-based platform according to claim 325, wherein, The data processing system includes an analysis system that represents a set of operating parameters and the current state of at least one entity based on a set of sensed parameters, the set of sensed parameters is generated by a set of edge devices close to at least one entity, and the analysis system is configured to provide recommendations associated with at least one of at least one entity or at least one additional available entity.

344. The AI-based platform according to claim 325, wherein, The data processing system includes an artificial intelligence system trained on a historical dataset related to the energy generation, storage, and / or utilization of an operating process associated with at least one entity, and the data processing system is further configured to: analyze the energy patterns of the operating process, and output a prediction of the energy demand of the operating process based on the current state and / or information associated with at least one entity. The AI-based platform according to claim 325, wherein, The data processing system is further configured to fuse the grid datasets generated, stored, delivered, or consumed by the energy grid entity and the off-grid energy entity generated, stored, delivered, and / or consumed datasets with at least one entity that generates, stores, delivers, or consumes backup and / or auxiliary energy grid datasets. The AI-based platform according to claim 325, wherein, The data processing system is also configured to coordinate the development of energy grid resources and / or off-grid energy resources based on fusing the power grid data sets generated, stored, transported, or consumed by the energy grid entities and the data sets generated, stored, transported, and / or consumed by the off-grid energy entities.

347. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: A set of autonomous coordination systems for improving the delivery of a set of heterogeneous energy types to a consumption point based on: The location of the consumption point, and A set of consumption attributes including at least one of the following: Peak power demand at the consumption point; Continuity of power demand at the consumption point; and The type of energy that can be used at the consumption point.

348. The AI-based platform according to claim 347, wherein, The set of autonomous coordination systems coordinates the delivery of a defined type of energy generation capacity to the consumption point.

349. The AI-based platform according to claim 347, wherein, The set of autonomous coordination systems coordinates the delivery of a defined type of energy storage capacity to the consumption point. The AI-based platform according to claim 347, wherein, Determine the type of energy that can be used at least in part based on a set of operation compatibility parameters. The AI-based platform according to claim 347, wherein, Determine the type of energy that can be used at least in part based on a set of governance parameters.

352. The AI-based platform according to claim 351, wherein, The set of governance parameters relates to the use of renewable energy.

353. The AI-based platform according to claim 351, wherein, The set of governance parameters relates to carbon generation or emissions.

354. The AI-based platform according to claim 347, wherein, At least one of the set of autonomous coordination systems is also configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on at least one of the following: Congestion conditions; Delay and / or latency conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

355. The AI-based platform according to claim 347, further comprising an adaptive energy digital twin representing at least one of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

356. The AI-based platform according to claim 347, further comprising an adaptive energy digital twin configured to perform at least one of the following: Provide visual and / or analytical metrics of energy consumption of at least one energy consumer, Filter energy data; Highlight energy data; or Adjust energy data.

357. The AI-based platform according to claim 347, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption through at least one of the following: At least one machine; At least one factory; or At least one vehicle in a fleet. The AI-based platform according to claim 347, wherein, At least one of the set of autonomous coordination systems is also configured to perform at least one of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Convert, transform, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

359. The AI-based platform according to claim 347, wherein, At least one consumption attribute is based on at least one common data resource, and the common data resource includes at least one of the following: Weather data resource; Satellite data resource; Census, population, demographic, and / or psychographic data resource; Market data resource; or E-commerce data resource. The AI-based platform according to claim 347, wherein, At least one consumption attribute is based on at least one enterprise data resource, and the enterprise data resource includes at least one of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data. The AI-based platform according to claim 347 further includes at least one AI-based model and / or algorithm, wherein, The at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on at least one of the following: At least one human label and / or annotation; At least one human interaction with a hardware and / or software system; At least one result; At least one AI-generated training data sample; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process.

362. The AI-based platform according to claim 347, wherein, At least one of the set of autonomous coordination systems is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: At least one fixed transmission line; At least one wireless energy transmission instance; At least one fuel delivery; or At least one stored energy delivery.

363. The AI-based platform according to claim 347, wherein, At least one of the set of autonomous coordination systems is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: Energy procurement and / or sales event; Service fees associated with energy procurement and / or sales events; Energy consumption event; Energy generation event; Energy distribution event; Energy storage event; Carbon emission generation event; Carbon emission reduction event; Renewable energy credit event; Pollution generation event; or Pollution reduction event.

364. The AI-based platform according to claim 347, wherein, At least one of the set of autonomous coordination systems is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: Off-grid energy generation system; Off-grid energy storage system; or Off-grid energy mobilization system.

365. The AI-based platform according to claim 347, wherein, The set of autonomous coordination systems is further configured to determine the delivery of the set of heterogeneous energy types based on a set of rules and / or policies that manage a set of energy generation, storage, and / or consumption workloads, and the rules and / or policies are associated with the configuration of a set of edge devices that perform local data communication operations with a set of energy generation facilities, energy storage facilities, energy delivery facilities, or energy consumption systems.

366. The AI-based platform according to claim 347, wherein, The set of autonomous coordination systems is also configured to determine the delivery of the set of heterogeneous energy types based on an energy consumption simulation of at least one energy consumer, the simulation being based on a data set including alternative state or event parameters of at least one of the at least one energy consumer, the alternative state or event parameters reflecting alternative consumption scenarios, and the simulation being based on a demand response model that describes how energy demand responds to changes in energy prices or the prices of operations or activities that consume energy.

367. The AI-based platform according to claim 347, wherein, The set of autonomous coordination systems improves the delivery of the set of heterogeneous energy types to the consumption point by matching each of the set of heterogeneous energy types with at least one consumer associated with the consumption point. The AI-based platform according to claim 347, wherein, The set of autonomous coordination systems improves the delivery of the set of heterogeneous energy types to the consumption point by determining the development of additional energy of one or more energy types, and the development is based on a prediction of the energy demand associated with the consumption point.

369. The AI-based platform according to claim 347, wherein, The set of autonomous coordination systems improves the delivery of the set of heterogeneous energy types to the consumption point by comparing the characteristics of the energy demand associated with the consumption point and the characteristics of each energy type of the set of heterogeneous energy types.

370. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: An intelligent agent trained on a data set that interacts with experts in an energy supply system, wherein the intelligent agent is trained to generate at least one recommendation and / or instruction for optimization with respect to at least one energy goal and at least one other goal.

371. The AI-based platform according to claim 370, wherein, The other goal is an operational goal of the enterprise.

372. The AI-based platform according to claim 370, wherein, The intelligent agent operates on status data from a set of edge devices and controls a set of energy generation resources via the set of edge devices. The AI-based platform according to claim 370, wherein The intelligent agent operates on status data from a set of edge devices and controls a set of energy consumption resources via the set of edge devices. The AI-based platform according to claim 370, wherein The intelligent agent operates on status data from a set of edge devices and controls a set of energy storage resources via the edge devices. The AI-based platform according to claim 370, wherein, The intelligent agent operates on status data from a set of edge devices and controls a set of energy delivery resources via the set of edge devices. The AI-based platform according to claim 370, wherein, The intelligent agent is also configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on at least one of the following: Congestion conditions; Delay and / or latency conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

377. The AI-based platform according to claim 370, further comprising an adaptive energy digital twin that represents at least one of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

378. The AI-based platform according to claim 370 further includes an adaptive energy digital twin configured to perform at least one of the following: Provide visual and / or analytical metrics of the energy consumption of at least one energy consumer, Filter energy data; Highlight energy data; or Adjust energy data.

379. The AI-based platform according to claim 370 further includes an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by at least one of the following: At least one machine; At least one factory; or At least one vehicle in a fleet. The AI-based platform according to claim 370, wherein, The intelligent agent is further configured to perform at least one of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Transform, convert, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data.

381. The AI-based platform according to claim 370, wherein, The data set is based on at least one common data resource, and the common data resource includes at least one of the following: Weather data resource; Satellite data resource; Census, population, demographic, and / or psychographic data resource; Market data resource; or E-commerce data resource.

382. The AI-based platform according to claim 370, wherein, The data set is based on at least one enterprise data resource, and the enterprise data resource includes at least one of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data. The AI-based platform according to claim 370, wherein, The intelligent agent is trained based on a training data set, and the training data set is based on at least one of the following: At least one human label and / or annotation; At least one human interaction with a hardware and / or software system; At least one result; At least one AI-generated training data sample; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process. The AI-based platform according to claim 370, wherein, The intelligent agent is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: At least one fixed transmission line; At least one wireless energy transmission instance; At least one fuel delivery; or At least one stored energy delivery. The AI-based platform according to claim 370, wherein, The intelligent agent is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: Energy procurement and / or sales event; Service fees associated with energy procurement and / or sales events; Energy consumption event; Energy generation event; Energy distribution event; Energy storage event; Carbon emission generation event; Carbon emission reduction event; Renewable energy credit event; Pollution generation event; or Pollution reduction event. The AI-based platform according to claim 370, wherein, The intelligent agent is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: An off-grid energy generation system; An off-grid energy storage system; or An off-grid energy dispatch system. The AI-based platform according to claim 370, wherein, The intelligent agent is located near at least one entity that generates, stores, transports, and / or uses energy.

388. The AI-based platform according to claim 370, wherein, The intelligent agent provides information on the energy status and / or energy flow of at least one entity that generates, stores, transports, and / or uses energy. The AI-based platform according to claim 370, wherein, The intelligent agent manages at least one sensor in a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transport, and / or use energy. The AI-based platform according to claim 370, wherein The intelligent agent is further configured to manage at least one processing task associated with at least one device, and the at least one recommendation and / or instruction includes adjusting the at least one processing task based on the at least one energy goal and / or the at least one other goal.

391. The AI-based platform according to claim 370, wherein, The intelligent agent is further configured to: Migrate between at least two devices, and When residing on each of the at least two devices, apply the at least one recommendation and / or instruction to the device on which the intelligent agent resides.

392. The AI-based platform according to claim 370, wherein, The intelligent agent is further configured to exchange information with at least one other intelligent agent, and the information is based on one or both of the at least one recommendation and / or instruction, or the at least one energy goal and / or the at least one other goal. The AI-based platform according to claim 370, wherein, The recommendation and / or instruction is associated with at least one device, and the intelligent agent is further configured to exchange the collected and / or determined data associated with the at least one device with at least one other intelligent agent.

394. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: An artificial intelligence system that operates on a set of energy generation, energy storage, energy delivery, and / or energy consumption results, wherein the artificial intelligence system is configured to: Analyze a dataset of current energy generation, current energy storage, current energy delivery, and / or current energy consumption information, and Provide recommendations including at least one operating parameter that meets both the energy needs of mobile entities and the energy needs of fixed locations within a defined domain.

395. The AI-based platform according to claim 394, wherein, The defined domain includes a defined geographical location and a defined time period.

396. The AI-based platform according to claim 394, wherein, The at least one operating parameter indicates a generation instruction for a set of energy generation resources.

397. The AI-based platform according to claim 394, wherein, The at least one operating parameter indicates a storage instruction for a set of energy storage resources. The AI-based platform according to claim 394, wherein, The at least one operating parameter indicates a delivery instruction for a set of energy delivery resources. The AI-based platform according to claim 394, wherein, The at least one operating parameter indicates a consumption instruction for a set of entities that consume energy. The AI-based platform according to claim 394, wherein, The artificial intelligence system is further configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on at least one of the following: Congestion conditions; Latency and / or delay conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of Service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

401. The AI-based platform according to claim 394 further includes an adaptive energy digital twin, and the adaptive energy digital twin represents at least one of the following: Energy stakeholder entities; Energy distribution resources; Stakeholder information technology; Network infrastructure entities; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority conditions.

402. The AI-based platform according to claim 394 further includes an adaptive energy digital twin, and the adaptive energy digital twin is configured to perform at least one of the following: Providing visual and / or analytical metrics of the energy consumption of at least one energy consumer, Filtering energy data; Highlighting energy data; or Adjusting energy data.

403. The AI-based platform according to claim 394 further includes an adaptive energy digital twin, and the adaptive energy digital twin is configured to generate visual and / or analytical metrics of energy consumption through at least one of the following: At least one machine; At least one factory; or At least one vehicle in a fleet. The AI-based platform according to claim 394, wherein, The artificial intelligence system is further configured to perform at least one of the following: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Converting, transforming, normalizing, and / or cleaning energy-related data; Parsing energy-related data; Detecting patterns, content, and / or objects in energy-related data; Compressing energy-related data; Streaming energy-related data; Filtering energy-related data; Loading and / or storing energy-related data; Routing and / or transmitting energy-related data; Or Maintaining the security of energy-related data. The AI-based platform according to claim 394, wherein, The dataset is based on at least one common data resource, and the at least one common data resource includes at least one of the following: Weather data resources; Satellite data resources; Census, population, demographic, and / or psychographic data resources; Market data resources; or E-commerce data resources. The AI-based platform according to claim 394, wherein, The dataset is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operations data. The AI-based platform according to claim 394, wherein, The artificial intelligence system is trained based on a training dataset, and the training dataset is based on at least one of the following: At least one human label and / or annotation; At least one human interaction with a hardware and / or software system; At least one result; At least one AI-generated training data sample; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process. The AI-based platform according to claim 394, wherein, The artificial intelligence system is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: At least one fixed transmission line; At least one wireless energy transmission instance; At least one fuel delivery; or At least one stored energy delivery. The AI-based platform according to claim 394, wherein, The artificial intelligence system is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, where the at least one energy-related event includes at least one of the following: Energy procurement and / or sales event; Service fees associated with energy procurement and / or sales events; Energy consumption event; Energy generation event; Energy distribution event; Energy storage event; Carbon emission generation event; Carbon emission reduction event; Renewable energy credit event; Pollution generation event; or Pollution reduction event. The AI-based platform according to claim 394, wherein, The artificial intelligence system is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: Off-grid energy generation system; Off-grid energy storage system; or Off-grid energy mobilization system. The AI-based platform according to claim 394, wherein, The artificial intelligence system is located near at least one entity that generates, stores, transports, and / or uses energy. The AI-based platform according to claim 394, wherein, The artificial intelligence system provides information on the energy status and / or energy flow of at least one entity that generates, stores, transports, and / or uses energy. The AI-based platform according to claim 394, wherein, The artificial intelligence system manages at least one sensor in a group of sensors, and the group of sensors is associated with a group of infrastructure assets configured to generate, store, transport, and / or use energy. The AI-based platform according to claim 394, wherein, The domain includes at least one boundary, and the data set is restricted based on the at least one boundary associated with the domain. The AI-based platform according to claim 394, wherein, The recommendation is based on at least one constraint associated with the at least one operating parameter, and the artificial intelligence system is trained to analyze the data set based on the at least one constraint.

416. An AI-based platform for achieving intelligent coordination and management of electricity and energy, comprising: An artificial intelligence system configured to: Analyze a data set of monitored local conditions, and Generate a recommended configuration for at least one distributed system in a group of distributed systems, where each distributed system in the group of distributed systems can be configured to produce and consume energy, and where the configuration causes the at least one distributed system to generate and / or consume energy based on the monitored local conditions. The AI-based platform according to claim 416, wherein, The artificial intelligence system configures multiple distributed systems in the group such that a set of aggregated performance requirements is met across the multiple distributed systems. The AI-based platform according to claim 417, wherein, The aggregated performance requirements are a set of economic performance requirements. The AI-based platform according to claim 417, wherein, The aggregated performance requirements are a set of regulatory performance requirements. The AI-based platform according to claim 417, wherein, The aggregated performance requirements are related to carbon generation or emissions.

421. The AI-based platform according to claim 417, wherein, The aggregated performance requirements are a set of consumption requirements. The AI-based platform according to claim 416, wherein, The artificial intelligence system is further configured to adapt to data transmission through a network and / or communication system, where the adaptation is based on at least one of the following: Congestion conditions; Latency and / or delay conditions; Packet loss conditions; Error rate conditions; Transport cost conditions; Quality of service (QoS) conditions; Usage conditions; Market factor conditions; or User configuration conditions.

423. The AI-based platform according to claim 416, further comprising an adaptive energy digital twin representing at least one of the following: Energy stakeholder entity; Energy distribution resources; Stakeholder information technology; Network infrastructure entity; Energy-related stakeholder production facilities; Stakeholder transportation systems; Market conditions; Or Energy usage priority status.

424. The AI-based platform according to claim 416, further comprising an adaptive energy digital twin configured to perform at least one of the following: Provide visual and / or analytical metrics of energy consumption of at least one energy consumer, Filter energy data; Highlight energy data; or Adjust energy data.

425. The AI-based platform according to claim 416, further comprising an adaptive energy digital twin configured to generate visual and / or analytical metrics of energy consumption by at least one of the following: At least one machine; At least one factory; or At least one vehicle in a fleet. The AI-based platform according to claim 416, wherein, The artificial intelligence system is further configured to perform at least one of the following: Extract energy-related data; Detect and / or correct errors in energy-related data; Transform, convert, normalize, and / or clean energy-related data; Parse energy-related data; Detect patterns, content, and / or objects in energy-related data; Compress energy-related data; Stream energy-related data; Filter energy-related data; Load and / or store energy-related data; Route and / or transmit energy-related data; Or Maintain the security of energy-related data. The AI-based platform according to claim 416, wherein The dataset is based on at least one common data resource, and the common data resource includes at least one of the following: Weather data resource; Satellite data resource; Census, population, demographic, and / or psychographic data resource; Market data resource; or E-commerce data resource. The AI-based platform according to claim 416, wherein, The dataset is based on at least one enterprise data resource, and the enterprise data resource includes at least one of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operational data. The AI-based platform according to claim 416, wherein The artificial intelligence system is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: At least one fixed transmission line; At least one wireless energy transmission instance; At least one fuel delivery; or At least one stored energy delivery. The AI-based platform according to claim 416, wherein, The artificial intelligence system is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: Energy procurement and / or sales events; Service fees associated with energy procurement and / or sales events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events. The AI-based platform according to claim 416, wherein, The artificial intelligence system is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: Off-grid energy generation system; Off-grid energy storage system; or Off-grid energy mobilization system. The AI-based platform according to claim 416, wherein The artificial intelligence system is trained based on a training data set, and the training data set is based on at least one of the following: At least one human label and / or annotation; At least one human interaction with a hardware and / or software system; At least one result; At least one AI-generated training data sample; A supervised learning training process; A semi-supervised learning training process; or A deep learning training process. The AI-based platform according to claim 416, wherein, The artificial intelligence system is located near at least one entity that generates, stores, transports, and / or uses energy. The AI-based platform according to claim 416, wherein, The artificial intelligence system provides information about the energy status and / or energy flow of at least one entity that generates, stores, transports, and / or uses energy. The AI-based platform according to claim 416, wherein, The artificial intelligence system manages at least one sensor in a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transport, and / or use energy. The AI-based platform according to claim 416, wherein, The recommended configuration is based on at least one auxiliary power resource associated with the set of distributed systems. The AI-based platform according to claim 416, wherein, The recommended configuration is based on at least one of the following: The current and / or predicted location of the at least one distributed system in the set of distributed systems, or The current and / or predicted location of at least one energy resource associated with the set of distributed systems. The AI-based platform according to claim 437, wherein, The recommended configuration is further based on at least one of the following: The local demand situation associated with the current and / or predicted location of at least one distributed system in the set of distributed systems, or The local demand situation associated with the current and / or predicted location of at least one energy resource associated with the set of distributed systems.

439. An AI-based platform for realizing intelligent coordination and management of electricity and energy, comprising: A set of adaptive autonomous data processing systems for collecting and transmitting energy data from a set of edge-networked devices and controlling a set of distributed energy entities via the set of edge-networked devices, wherein the data processing systems are trained based on a training data set to identify a set of events and / or signals indicating at least one energy pattern of the set of distributed energy entities. The AI-based platform according to claim 439, wherein, The set of distributed energy entities includes at least one energy generation resource. The AI-based platform according to claim 439, wherein, The set of distributed energy entities includes at least one energy consumption entity. The AI-based platform according to claim 439, wherein, The set of distributed energy entities includes at least one energy storage resource. The AI-based platform according to claim 439, wherein, The set of distributed energy entities includes at least one energy transmission resource. The AI-based platform according to claim 439, wherein, The training data set includes historical energy generation data of a set of entities similar to the entities controlled via the edge-networked devices. The AI-based platform according to claim 439, wherein, The training data set includes historical energy consumption data of a set of entities similar to the entities controlled via the edge-networked devices. The AI-based platform according to claim 439, wherein, The training data set includes historical energy transmission data of a set of entities similar to the entities controlled via the edge-networked devices. The AI-based platform according to claim 439, wherein, The training data set includes historical energy storage data of a set of entities similar to the entities controlled via the edge-networked devices. The AI-based platform according to claim 439, wherein, At least one adaptive autonomous data processing system is further configured to adapt to data transmission through a network and / or communication system, wherein the adaptation is based on at least one of the following: Congestion condition; Latency and / or delay condition; Packet loss condition; Error rate condition; Transportation cost condition; Quality of Service (QoS) condition; Usage condition; Market factor condition; or User configuration condition.

449. The AI-based platform according to claim 439, further comprising an adaptive energy digital twin, the adaptive energy digital twin representing at least one of the following: Energy stakeholder entity; Energy distribution resource; Stakeholder information technology; Network infrastructure entity; Energy-related stakeholder production facility; Stakeholder transportation system; Market condition; Or Energy usage priority condition.

450. The AI-based platform according to claim 439, further comprising an adaptive energy digital twin, the adaptive energy digital twin being configured to perform at least one of the following: Providing visual and / or analytical metrics of energy consumption of at least one energy consumer, Filtering energy data; Highlighting energy data; or Adjusting energy data.

451. The AI-based platform according to claim 439, further comprising an adaptive energy digital twin, the adaptive energy digital twin being configured to generate visual and / or analytical metrics of energy consumption through at least one of the following: At least one machine; At least one factory; or At least one vehicle in a fleet. The AI-based platform according to claim 439, wherein, At least one adaptive autonomous data processing system is further configured to perform at least one of the following: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Converting, transforming, normalizing, and / or cleaning energy-related data; Parsing energy-related data; Detecting patterns, content, and / or objects in energy-related data; Compressing energy-related data; Streaming energy-related data; Filtering energy-related data; Loading and / or storing energy-related data; Routing and / or transmitting energy-related data; Or Maintaining the security of energy-related data. The AI-based platform according to claim 439, wherein, The energy edge set is based on at least one common data resource, the common data resource including at least one of the following: Weather data resource; Satellite data resource; Census, population, demographic, and / or psychographic data resource; Market data resource; or E-commerce data resource. The AI-based platform according to claim 439, wherein, The energy edge set is based on at least one enterprise data resource, the at least one enterprise data resource including at least one of the following: Resource planning data; Sales and / or marketing data; Financial planning data; Demand planning data; Supply chain data; Procurement data; Pricing data; Customer data; Product data; or Operation data. The AI-based platform according to claim 439 further includes at least one AI-based model and / or algorithm, wherein, The at least one AI-based model and / or algorithm is trained based on a training data set, and the training data set is based on at least one of the following: At least one human label and / or annotation; At least one human interaction with a hardware and / or software system; At least one result; At least one AI-generated training data sample; Supervised learning training process; Semi-supervised learning training process; or Deep learning training process. The AI-based platform according to claim 439, wherein, At least one adaptive autonomous data processing system is further configured to coordinate the delivery of energy to at least one consumption point, and the energy delivery includes at least one of the following: At least one fixed transmission line; At least one instance of wireless energy transmission; At least one fuel delivery; or At least one stored energy delivery. The AI-based platform according to claim 439, wherein, At least one adaptive autonomous data processing system is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of the following: Energy purchase and / or sale events; Service fees associated with energy purchase and / or sale events; Energy consumption events; Energy generation events; Energy distribution events; Energy storage events; Carbon emission generation events; Carbon emission reduction events; Renewable energy credit events; Pollution generation events; or Pollution reduction events. The AI-based platform according to claim 439, wherein, At least one adaptive autonomous data processing system is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: An off-grid energy generation system; An off-grid energy storage system; or An off-grid energy mobilization system. The AI-based platform according to claim 439, wherein, The set of adaptive autonomous data processing systems is further configured to perform additional training of the data processing systems based on an initial set of energy intelligence data on which the data processing systems were initially trained and additional energy intelligence data on which the data processing systems have not been trained. The AI-based platform according to claim 439, wherein, The set of adaptive autonomous data processing systems is further configured to instruct at least one edge networking device in the set of edge networking devices to adjust operating parameters associated with the set of distributed energy entities based on the identification of events and / or signals in the set of events and / or signals. The AI-based platform according to claim 439, wherein, The set of adaptive autonomous data processing systems is further configured to detect events and / or signals based on data collected from the set of edge networking devices over a period of time, and the data processing systems are trained to identify the set of events and / or signals based on at least one characteristic of the period of time.

462. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: A data integration module that integrates energy intelligence data collected from at least one internal edge device located within an environment and at least one external edge device located outside the environment.

463. The AI-based platform according to claim 462, wherein, Data collected from at least one of the at least one internal edge device or the at least one external edge device is vectorized. The AI-based platform according to claim 462, wherein, Data collected from at least one of the at least one internal edge device or the at least one external edge device is stored in a distributed database. The AI-based platform according to claim 462, wherein, The data integration module is further configured to determine an energy pattern based on a localized energy pattern associated with data collected from the at least one internal edge device and the at least one external edge device.

466. An AI-based platform for implementing intelligent coordination and management of electricity and energy, comprising: A digital dynamic twin configured to model at least one of historical energy demand, current historical energy demand, or predicted energy demand; And AI-based Digital Twin Updater, the AI-based Digital Twin Updater updates the dynamic digital twin based on a set of energy parameters. The AI-based platform according to claim 466, wherein, The AI-based Digital Twin Updater performs the update of the dynamic digital twin to determine the energy demand forecast during a future period of time, and the update is based on the prediction of the energy demand during a future period of time by another AI model. The AI-based platform according to claim 466, wherein, The dynamic digital twin is associated with a device type, and the AI-based Digital Twin Updater analyzes data associated with the energy consumption of devices of the device type to update the dynamic digital twin to model the energy consumption of devices of the device type. The AI-based platform according to claim 466, wherein, The dynamic digital twin is also configured to model the energy demand of at least one entity, wherein the model is based on data indicating the energy consumption of the at least one entity.

470. An AI-based platform for realizing intelligent coordination and management of electric power and energy, comprising: An energy access arbiter, the energy access arbiter arbitrates the access of at least one energy-consuming device in the set of energy-consuming devices to at least one energy source in the set of energy-consuming devices.

471. An AI-based platform for realizing intelligent coordination and management of electric power and energy, comprising: A set of edge devices, the set of edge devices communicate locally with at least one energy-consuming device to determine at least one energy consumption characteristic of the at least one energy-consuming device, wherein at least one edge device in the set of edge devices determines the at least one energy consumption characteristic of the at least one energy-consuming device based on multiple perspectives associated with the energy consumption of the at least one energy-consuming device.

472. The AI-based platform according to claim 471, further comprising an edge device monitoring system, the edge device monitoring system monitors the energy consumption of at least one downstream device in the at least one energy-consuming device, and implements an energy policy on the at least one downstream device based on the energy consumption. The AI-based platform according to claim 472, wherein, The energy policy is based on a generation mechanism, and energy associated with the energy consumption is generated through the generation mechanism. The AI-based platform according to claim 472, wherein The edge device monitoring system is also configured to determine the carbon emissions associated with the energy consumption of the at least one downstream device.

475. An AI-based platform for realizing intelligent coordination and management of electric power and energy, comprising: A set of general artificial intelligence (AGI) agents, wherein each AGI agent is assigned to manage a set of energy generation, storage, and / or consumption workloads of a set of entities. The AI-based platform according to claim 475, wherein, At least one AGI agent in the set of AGI agents is also configured to adjust at least one parameter associated with the AI-based platform based on at least one interaction between the at least one AGI agent and at least one of a human, another AGI agent, or another component of the AI-based platform. The AI-based platform according to claim 475, wherein, At least one AGI agent in the set of AGI agents monitors the decisions of at least one other AGI agent in the set of AGI agents and adjusts at least one parameter associated with the AI-based platform based on the decisions of the at least one other AGI agent. The AI-based platform according to claim 475, wherein, At least one AGI agent in the set of AGI agents monitors energy-related data associated with at least one of the following: At least one interaction between at least one person and at least one component of the AI-based platform, At least one wildlife usage pattern, At least one space travel instance, At least one satellite, At least one asteroid mining operation, At least one banking system, At least one marketing operation, At least one radioactive waste disposal instance related to at least one nuclear power plant, At least one cyber attack associated with at least one type of energy, At least one land clearing operation, At least one AI entity, or At least one robotic entity. The AI-based platform according to claim 475, wherein At least one AGI agent in the set of AGI agents performs an adjustment of data associated with at least one of a data collection process, a data storage process, a data reporting process, or a data transmission process, and the adaptation is based on at least one of an anonymous request of a person associated with the data or a privacy request of a person associated with the data.

480. The AI-based platform according to claim 475, wherein at least one AGI agent in the set of AGI agents monitors the movement of at least one energy resource within a networked element and updates a policy associated with the at least one energy resource based on the movement. The AI-based platform according to claim 480, wherein, At least one AGI agent in the set of AGI agents updates energy distribution in response to the movement to facilitate the energy availability of the at least one energy resource.

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