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, coordinating and coordinating with distributed systems to support important use cases for a series of enterprises. An Al-based energy edge platform may include a graphical neural network including a set of nodes representing at least one distributed energy resource (DER), respectively, and a set of edges interconnecting the set of nodes, respectively, where each edge represents at least one energy related feature between at least two nodes of the set of nodes. 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 employ intelligent provisioning, data summarization, and analysis to utilize an energy market connection, communication, and transaction support platform.

Description

Cross Reference to Related Applications

[0001] This application is a continuation-in-part of PCT Application No. PCT / IB2023 / 058962, filed September 10, 2023, which claims priority to IN 202311057688, filed August 28, 2023, and claims benefit of: U.S. Provisional Patent Application No. 63 / 375,225, filed September 10, 2022, and U.S. Provisional Patent Application No. 63 / 537,478, filed September 8, 2023.

[0002] This application claims priority to IN 202311057688, filed August 28, 2023. This application claims benefit of: U.S. Provisional Patent Application No. 63 / 461,810, filed April 25, 2023, U.S. Provisional Patent Application No. 63 / 472,225, filed June 9, 2023, U.S. Provisional Patent Application No. 63 / 535,747, filed August 31, 2023, U.S. Provisional Patent Application No. 63 / 537,478, filed September 8, 2023, U.S. Provisional Patent Application No. 63 / 610,870, filed December 15, 2023, U.S. Provisional Patent Application No. 63 / 621,540, filed January 16, 2024, U.S. Provisional Patent Application No. 63 / 625,613, filed January 26, 2024.

[0003] The entire contents of all of the above applications are hereby incorporated by reference. BACKGROUND

[0004] Energy remains a key factor in the world economy, undergoing an evolution and transition involving changes in energy production, storage, planning, demand management, consumption, and delivery systems and processes. These changes are enabled by the development and integration 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 by a range of technologies that will facilitate the management of energy in more decentralized systems, including edge and Internet of Things technologies, advanced computing and artificial intelligence technologies, transaction- supporting technologies (e.g., blockchain, distributed ledgers, and smart contracts), and the like. The integration of these more decentralized energy technologies with these networking, computing, and intelligence technologies is referred to herein as the “energy edge.”

[0005] The energy market is expected to evolve and transform over the coming decades from a highly centralized model that relies on fossil fuels and a managed grid to a more distributed and decentralized model that involves more localized generation, storage, and consumption systems. During this transition, hybrid systems are likely to persist for years, with traditional grids becoming more intelligent and distributed systems playing an increasingly larger role. A platform is needed that facilitates the coordinated management and improvement of traditional infrastructure with distributed systems to support a range of enterprise important use cases. SUMMARY

[0006] An AI-based energy edge platform is provided herein with a broad range of features, components, and capabilities for managing and improving traditional infrastructure and coordinating with distributed systems to support a range of enterprise important use cases. The platform can employ emerging technologies to improve the efficiency, flexibility, engagement, and profitability of the ecosystem and individual energy edge nodes. Embodiments can be guided by and, in some cases, integrated with methods and systems for forecasting, planning, and managing energy demand and utilization in larger distributed environments. Embodiments can use AI and AI enablers (e.g., IoT) that can be deployed in much denser data environments, reflecting the proliferation of intelligent energy systems and sensors in IoT, as well as technologies that more effectively filter, process, and move data across communication networks. Embodiments of the platform can leverage an energy market connectivity, communication, and transaction support platform. Embodiments can employ intelligent provisioning, data aggregation, and analytics. 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 use new power generation and energy storage technologies and assets (distributed energy resources or “DER”), optimize energy utilization of existing networks, and digitize existing infrastructure and support systems.

[0007] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of power and energy, comprising: an adaptive energy data pipeline configured to communicate data across a set of nodes in a network, wherein each node in the set of nodes is adapted to operate on a set of energy data 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 set of rules to filter, compress, transform, error correct, and / or route at least a portion of the set of energy data based on at least one of a set of network conditions, data size, data granularity, or data content.

[0008] In some aspects, the technology described herein relates to an AI-based platform, wherein the adaptive energy data pipeline is further configured to adapt data transmission through a network and / or communication system based on one or more of: 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.

[0009] In some aspects, the technology described herein relates to an AI-based platform further comprising an adaptive energy digital twin, the adaptive energy digital twin representing one or more of: an energy stakeholder entity; an energy distribution resource; a stakeholder information technology; a network infrastructure entity; an energy-related stakeholder production facility; a stakeholder transportation system; market conditions; or energy usage priority conditions.

[0010] In some aspects, the technology described herein relates to an AI-based platform further comprising an adaptive energy digital twin, the adaptive energy digital twin configured to perform one or more of: providing visual and / or analytical indicators of energy consumption for one or more energy consumers; filtering energy data; highlighting energy data; or adjusting energy data.

[0011] In some aspects, the technology described herein relates to an AI-based platform further comprising an adaptive energy digital twin, the adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by one or more of: one or more machines; one or more factories; or one or more vehicles in a vehicle fleet.

[0012] In some aspects, the technology described herein relates to an AI-based platform, wherein the adaptive energy data pipeline is further configured to perform one or more of: 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 security of energy-related data.

[0013] In some aspects, the technology described herein relates to an AI-based platform, wherein the energy data set is based on one or more public data resources, the public data resources comprising one or more of: weather data resources; satellite data resources; census, population, demographic, and / or psychographic data resources; market data resources; or e-commerce data resources.

[0014] In some aspects, the technology described herein relates to an AI-based platform, wherein the energy dataset is based on one or more enterprise data resources, the enterprise data resources comprising one or more 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 operational data.

[0015] In some aspects, the technology described herein relates 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 dataset, and the training dataset is based on one or more of: 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.

[0016] In some aspects, the technology described herein relates to an AI-based platform, wherein at least one node of the set of nodes is configured to coordinate delivery of energy to one or more consumption points, and the energy delivery comprises one or more of: 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.

[0017] In some aspects, the technology described herein relates to an AI-based platform, wherein at least one node of the set of nodes 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 comprising one or more of: an energy procurement and / or sale event; a service fee associated with the energy procurement 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.

[0018] In some aspects, the technology described herein relates to an AI-based platform, wherein at least one node of the set of nodes is deployed in an off-grid environment, and the off-grid environment comprises one or more of: an off-grid energy generation system; an off-grid energy storage system; or an off-grid energy mobilization system.

[0019] In some aspects, the technology described herein relates to an AI-based platform, wherein the adaptive energy data pipeline is further configured to monitor one or both of: a total energy consumption of at least a portion of the set of nodes; or a 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: managing energy consumption of the set of nodes; predicting energy consumption of the set of nodes; or provisioning resources associated with energy consumption of the set of nodes.

[0020] In some aspects, the technology described herein relates to an AI-based platform, wherein the set of nodes in the network comprising the adaptive energy data pipeline comprises a set of edge networking devices, the edge networking devices managing at least one of energy consumption, energy storage, energy delivery, or energy consumption through a set of operational devices controlled via the edge networking devices.

[0021] In some aspects, the technology described herein relates to an AI-based platform, wherein the adaptive energy data pipeline is further configured to automatically select a least-cost route for data to be communicated across the set of nodes, the selection based on low-priority energy usage associated with the data.

[0022] In some aspects, the technology described herein relates to an AI-based platform, wherein the adaptive energy data pipeline is further configured to automatically select a high-quality service route for data to be communicated across the set of nodes, the selection based on high-priority energy usage associated with the data.

[0023] In some aspects, the technology described herein relates to an AI-based platform, wherein the adaptive energy data pipeline comprises a set of artificial intelligence capabilities configured to adjust the pipeline to enable optimization of elements of data transfer according to energy coordination needs.

[0024] In some aspects, the technology described herein relates to an AI-based platform, wherein the adaptive energy data pipeline comprises a self-organizing data store, the data store configured to store data on devices based on one or more of data patterns, data content, or data context.

[0025] In some aspects, the technology described herein relates to an AI-based platform, wherein the adaptive energy data pipeline is configured to perform automated adaptive networking, the adaptive networking comprising 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.

[0026] In some respects, the technology described herein relates to an AI-based platform in which an adaptive energy data pipeline is configured to perform enterprise environmental adaptability by automatically processing data based on one or more of the enterprise's operational environment, transactional environment, or financial environment.

[0027] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of nodes is further configured to adjust communication with at least one other node in the set of nodes to adapt the reporting of data associated with at least one of energy generation, energy storage, energy transmission or energy consumption to at least one other node.

[0028] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of nodes is further configured to adapt reported data to at least one other node in the set of nodes, wherein adapting the reported data to a priority based on the consumption of the reported data.

[0029] In some respects, the technology described herein relates to an AI-based platform in which a set of nodes comprises a heterogeneous group, the heterogeneous group comprising at least one energy producer and at least one energy consumer, and an adaptive energy data pipeline is further configured to instruct one or both of the at least one energy producer and at least one energy consumer to communicate with at least one other node in the set of nodes via at least one communication route.

[0030] In some respects, the technology described herein relates to an AI-based platform in which the adaptive energy data pipeline is also configured to request reported data from at least one of a set of nodes, the reported data being based on a granular level, and the granular level being based on the priority of the machine associated with the reported data.

[0031] In some respects, the technology described herein relates to an AI-based platform in which the adaptive energy data pipeline is also 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.

[0032] In some respects, the technology described herein relates to an AI-based platform for enabling 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 transmission from a set of edge devices under the operational control of a set of distributed energy sources, and is configured to autonomously adjust a set of operational parameters for such operational control based on the collected data.

[0033] In some respects, the technology described herein relates to an AI-based platform in which each adaptive autonomous data processing system is further configured to adapt to 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 factors conditions; or user configuration conditions.

[0034] In some respects, the technology described herein relates to an AI-based platform in which each adaptive autonomous data processing system 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 use priorities.

[0035] In some respects, the technology described herein relates to an AI-based platform in which each adaptive autonomous data processing system includes an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical metrics of energy consumption for one or more energy consumers; filtering energy data; highlighting energy data; or adjusting energy data.

[0036] In some respects, the technology described herein relates to an AI-based platform in which each adaptive autonomous data processing system 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 plants; or one or more vehicles in a fleet.

[0037] In some respects, the technology described herein relates to an AI-based platform in which each adaptive autonomous data processing 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.

[0038] In some respects, the technology described herein relates to an AI-based platform in which energy edge data is based on one or more public data resources, including one or more of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0039] In some respects, the technology described herein relates to an AI-based platform in which 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 operational data.

[0040] In some respects, the technology described herein relates to an AI-based platform, which also includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained 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.

[0041] In some respects, the technology described herein relates to an AI-based platform in which each adaptive autonomous data processing system is also configured to coordinate the delivery of energy to one or more points of consumption, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy delivery instances; one or more fuel delivery systems; or one or more stored energy delivery systems.

[0042] In some respects, the technology described herein relates to an AI-based platform in which each adaptive autonomous data processing system is also configured to record one or more energy-related events in a distributed ledger and / or blockchain, including 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 distribution events; energy storage events; carbon emission generation events; carbon emission reduction events; renewable energy credit events; pollution generation events; or pollution reduction events.

[0043] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0044] In some respects, the technology described in this paper relates to an AI-based platform, which also includes an adaptive energy data pipeline configured to transmit data across a set of nodes in a network.

[0045] In some respects, the technology described herein relates to an AI-based platform in which a set of nodes in a network including an adaptive energy data pipeline includes a set of edge networking devices that manage at least one of energy consumption, energy storage, energy transmission, or energy dissipation through a set of operating devices controlled via the edge networking devices.

[0046] In some respects, the technology described in this paper relates to an AI-based platform in which the adaptive energy data pipeline is also configured to automatically select the lowest-cost route for data to be transmitted across a set of nodes, based on low-priority energy usage associated with the data.

[0047] In some respects, the technology described in this paper relates to an AI-based platform in which the adaptive energy data pipeline is also configured to automatically select a high-quality service route for data to be transmitted across a set of nodes, based on high-priority energy usage associated with the data.

[0048] In some respects, the technology described herein relates to an AI-based platform in which the adaptive energy data pipeline includes a set of artificial intelligence capabilities configured to adjust the pipeline to enable elements that optimize data transmission based on energy coordination needs.

[0049] In some respects, the technology described herein relates to an AI-based platform in which an adaptive energy data pipeline includes a self-organizing data store configured to store data on the device based on one or more of a data pattern, data content, or data context.

[0050] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0051] In some respects, the technology described herein relates to an AI-based platform in which an adaptive energy data pipeline is configured to perform enterprise environmental adaptation by automatically processing data based on one or more of the enterprise's operational environment, transactional environment, or financial environment.

[0052] In some respects, the technology described herein relates to an AI-based platform in which 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 a set of distributed energy sources.

[0053] In some respects, the technology described herein relates to an AI-based platform in which 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 transmission of the distributed energy sources.

[0054] In some respects, the technology described herein relates to an AI-based platform in which at least one adaptive autonomous data processing system is further configured to issue instructions to at least one edge device in a group of edge devices, the instructions being based on an investigation of energy generation, storage, or transmission of distributed energy, and the instructions causing at least one edge device to adjust the energy generation, storage, or transmission of at least one edge device.

[0055] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: a system configured to perform automated 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 distributed edge energy resource is operationally independent of the energy grid.

[0056] In some respects, the technology described herein relates to an AI-based platform in which the system is also configured to adapt to 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 factors conditions; or user configuration conditions.

[0057] In some respects, the technology described herein relates to an AI-based platform, and also 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 use priorities.

[0058] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical metrics of energy consumption for one or more energy consumers; filtering energy data; highlighting energy data; or adjusting energy data.

[0059] In some respects, the technology described herein relates to an AI-based platform, which also includes 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 plants; or one or more vehicles in a fleet.

[0060] In some respects, the technology described herein relates to an AI-based platform in which the system is also 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.

[0061] In some respects, the technology described herein relates to an AI-based platform, which also includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained 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.

[0062] In some respects, the technology described herein relates to an AI-based platform in which the system is also configured to coordinate the delivery of energy to one or more points of consumption, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy delivery instances; one or more fuel delivery instances; or one or more stored energy delivery instances.

[0063] In some respects, the technology described herein relates to an AI-based platform in which the system is also 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 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.

[0064] In some respects, the technology described herein relates to an AI-based platform in which 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 dispatch system.

[0065] In some respects, the technology described in this paper relates to an AI-based platform configured to facilitate the governance of the mining environment.

[0066] In some respects, the technology described herein relates to an AI-based platform in which the system includes mine-grade Internet of Things (IoT) sensing of the mining environment, ground penetration 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 miners' physiological states, secure recording and resolution of transactions and transaction-related events, smart contracts for automatically distributing revenues derived from the mining environment, and automated systems for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements.

[0067] In some respects, the technology described in this paper relates to an AI-based platform in which the system includes a set of carbon-sensing energy edge solutions that include exploring, configuring, and implementing a set of strategies for carbon generation.

[0068] In some respects, the technology described in this paper involves an AI-based platform where the solution requires monitoring energy production in the mining environment to track carbon emissions generated by the mining environment.

[0069] In some respects, the technology described in this paper involves an AI-based platform in which the solution requires the mining environment to produce energy to offset the carbon generated by the mining environment.

[0070] In some respects, the technology described herein relates to an AI-based platform, wherein the platform includes a user interface and the system includes a set of automated energy strategy deployment solutions that can be configured via user interaction with the user interface.

[0071] In some respects, the technology described in this paper relates to an AI-based platform in which the system includes an intelligent agent trained to generate policies relevant to the governance of the mining environment, the intelligent agent being trained on a training set of historical data, feedback from results, and human policy setting interactions.

[0072] In some respects, the technology described herein relates to an AI-based platform in which the system promotes governance of the mining environment by implementing one or more of the following strategies: setting maximum energy use for an entity over a period of time; setting maximum energy costs for an entity over a period of time; setting maximum carbon production for an entity over a period of time; setting maximum pollution emissions for an entity over a period of time; setting carbon offsetting requirements; setting renewable energy credit requirements; setting energy blending requirements; setting minimum profit margins based on the energy and other marginal costs of producing entities; or setting minimum storage baselines for energy storage entities.

[0073] In some respects, the technology described herein relates to an AI-based platform in which 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 degree of management for a set of energy transactions.

[0074] In some respects, the technology described herein relates to an AI-based platform in which the system includes a set of automated energy financial control solutions configured to allow users of the platform to design, generate, configure, or deploy strategies relating to one or more relevant financial factors in energy generation, storage, transmission, or utilization.

[0075] In some respects, the technology described herein relates to an AI-based platform in which the system is also configured to determine priorities associated with at least one of a set of energy entities or a set of distributed edge energy resources, and the priorities are based on strategies associated with at least one of the set of energy entities or a set of distributed energy resources.

[0076] In some respects, the technology described herein relates to an AI-based platform in which the system is also configured to perform monitoring of energy productivity through a set of energy entities and to adjust the automated and coordinated governance of the set of energy entities based on the monitoring of productivity.

[0077] In some respects, the technology described herein relates to an AI-based platform in which the system is also configured to allocate processing to a set of distributed edge energy sources based on at least one energy measurement and / or prediction associated with a set of energy entities.

[0078] In some respects, the technology described herein relates to an AI-based platform for enabling 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 the data communication associated with the adaptive energy data pipeline by at least one of rules or algorithms, and the at least one parameter is based on a set of metrics of the current network conditions in order to optimize the energy used in the data communication.

[0079] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0080] In some respects, the technology described herein relates to an AI-based platform in which the adaptive energy data pipeline is also configured to adapt to 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 factors conditions; or user configuration conditions.

[0081] In some respects, the technology described herein relates to an AI-based platform, and also 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 use priorities.

[0082] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical metrics of energy consumption for one or more energy consumers; filtering energy data; highlighting energy data; or adjusting energy data.

[0083] In some respects, the technology described herein relates to an AI-based platform, which also includes 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 plants; or one or more vehicles in a fleet.

[0084] In some respects, the technology described herein relates to an AI-based platform in which the adaptive energy data pipeline is also 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.

[0085] In some respects, the technology described herein relates to an AI-based platform in which data is based on one or more public data resources, including one or more of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0086] In some respects, the technology described herein relates to an AI-based platform in which data is based on 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.

[0087] In some respects, the technology described herein relates to an AI-based platform, which also includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained 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.

[0088] In some respects, the technology described herein relates to an AI-based platform in which an adaptive energy data pipeline is also configured to coordinate the delivery of energy to one or more points of consumption, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy delivery instances; one or more fuel delivery instances; or one or more stored energy delivery instances.

[0089] In some respects, the technology described herein relates to an AI-based platform in which an adaptive energy data pipeline is also 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 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.

[0090] In some respects, the technology described herein relates to an AI-based platform in which at least a portion of an 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 dispatch system.

[0091] In some respects, the technology described herein relates to an AI-based platform in which the adaptive energy data pipeline is also configured to: monitor one or both of the following: the total energy consumption of at least a portion of a group of nodes; or the role of at least one node in the total energy consumption of at least a portion of the group of nodes; and based on the monitoring, perform one or more of the following: manage the energy consumption of the group of nodes; predict the energy consumption of the group of nodes; or provide resources associated with the energy consumption of the group of nodes.

[0092] In some respects, the technology described herein relates to an AI-based platform in which a set of nodes in a network including an adaptive energy data pipeline includes a set of edge networking devices that manage at least one of energy consumption, energy storage, energy transmission, or energy dissipation through a set of operating devices controlled via the edge networking devices.

[0093] In some respects, the technology described in this paper relates to an AI-based platform in which the adaptive energy data pipeline is also configured to automatically select the lowest-cost route for data to be transmitted across a set of nodes, based on low-priority energy usage associated with the data.

[0094] In some respects, the technology described in this paper relates to an AI-based platform in which the adaptive energy data pipeline is also configured to automatically select a high-quality service route for data to be transmitted across a set of nodes, based on high-priority energy usage associated with the data.

[0095] In some respects, the technology described herein relates to an AI-based platform in which the adaptive energy data pipeline includes a set of artificial intelligence capabilities configured to adjust the pipeline to enable elements that optimize data transmission based on energy coordination needs.

[0096] In some respects, the technology described herein relates to an AI-based platform in which an adaptive energy data pipeline includes a self-organizing data store configured to store data on the device based on one or more of a data pattern, data content, or data context.

[0097] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0098] In some respects, the technology described herein relates to an AI-based platform for enabling 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 in the mining environment.

[0099] In some respects, the technology described herein relates to an AI-based platform in which at least one parameter is associated with one or more of the following: an unmined portion of a mining environment; material extraction from the mining environment; smart container events relating 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 complying with one or more contracts, regulations, and / or legal policies.

[0100] In some respects, the technology described herein relates to an AI-based platform in which 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 use priorities.

[0101] In some respects, the technology described herein relates to an AI-based platform in which the digital twin system is also 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; or adjust energy data.

[0102] In some respects, the technology described herein relates to an AI-based platform in which the digital twin system is also 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.

[0103] In some respects, the technology described herein relates to an AI-based platform in which parameters are based on one or more public data resources, including one or more of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0104] In some respects, the technology described herein relates to an AI-based platform in which parameters are based on 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.

[0105] In some respects, the technology described herein relates to an AI-based platform in which a 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 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.

[0106] In some respects, the technology described herein relates to an AI-based platform in which a digital twin system is also configured to coordinate the delivery of energy to one or more points of consumption, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy delivery instances; one or more fuel delivery instances; or one or more stored energy delivery instances.

[0107] In some respects, the technology described herein relates to an AI-based platform in which the digital twin system is also 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 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.

[0108] In some respects, the technology described herein relates to an AI-based platform in which a 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 dispatch system.

[0109] In some respects, the technology described in this paper relates to an AI-based platform, where the mining environment is a data mining environment.

[0110] In some respects, the technology described in this paper relates to an AI-based platform in which the mining environment is a set of resources used for computational operations.

[0111] In some respects, the technologies described herein relate to an AI-based platform comprising mine-grade Internet of Things (IoT) sensing of the mining environment, ground penetration 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 miners' physiological states, secure recording and resolution of transactions and transaction-related events, smart contracts for automatically distributing revenues derived from the mining environment, and automated systems for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements.

[0112] In some respects, the technology described in this paper relates to an AI-based platform that includes a set of carbon-sensing energy edge solutions that include exploring, configuring, and implementing a set of strategies for carbon generation.

[0113] In some respects, the technology described in this paper involves an AI-based platform where the solution requires monitoring energy production in the mining environment to track carbon emissions generated by the mining environment.

[0114] In some respects, the technology described in this paper involves an AI-based platform in which the solution requires the mining environment to produce energy to offset the carbon generated by the mining environment.

[0115] In some respects, the technology described herein relates to an AI-based platform, wherein the platform includes a user interface and a set of automated energy strategy deployment solutions that can be configured via user interaction with the user interface.

[0116] In some respects, the technology described in this paper relates to an AI-based platform, wherein the platform includes an intelligent agent trained to generate policies relevant to the governance of the mining environment, the intelligent agent being trained on a training set of historical data, feedback from results, and human policy setting interactions.

[0117] In some respects, the technology described herein relates to an AI-based platform that promotes governance of the mining environment by implementing one or more of the following strategies: setting maximum energy use for an entity over a period of time; setting maximum energy costs for an entity over a period of time; setting maximum carbon production for an entity over a period of time; setting maximum pollution emissions for an entity over a period of time; setting carbon offsetting requirements; setting renewable energy credit requirements; setting energy blending requirements; setting minimum profit margins based on the energy and other marginal costs of producing entities; or setting minimum storage baselines for energy storage entities.

[0118] In some respects, the technology described herein relates to an AI-based platform in which at least one parameter includes a measurement performed by a sensor, and the measurement is associated with at least one piece of equipment included in an industrial operation in a mining environment.

[0119] In some respects, the technology described herein relates to an AI-based platform in which 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 industrial operations in a mining environment, and the schedule is based on at least one parameter detected by sensors.

[0120] In some respects, the technology described herein relates to an AI-based platform in which at least one parameter included in the digital twin comprises at least one attribute of at least one dataset associated with a mining environment.

[0121] In some respects, the technology described herein relates to an AI-based platform for enabling 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 in the mine of the mining operation, wherein the at least one parameter is associated with the mining operation's compliance with a set of labor standards.

[0122] In some respects, the technology described herein relates to an AI-based platform in which the reporting system is also configured to adapt to 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 factors conditions; or user configuration conditions.

[0123] In some respects, the technology described herein relates to an AI-based platform, and also 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 use priorities.

[0124] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical metrics of energy consumption for 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 plants; or one or more vehicles in a fleet.

[0125] In some respects, the technology described herein relates to an AI-based platform in which the reporting system is also 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.

[0126] In some respects, the technology described herein relates to an AI-based platform in which the reporting system is also 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 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.

[0127] In some respects, the technology described herein relates to an AI-based platform, wherein at least one of at least one parameter is based on one or more of the following: one or more public 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; or 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 operational data.

[0128] In some respects, the technology described herein relates to an AI-based platform, which also includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained 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.

[0129] In some respects, the technology described herein relates to an AI-based platform in which the governance system is also configured to coordinate the delivery of energy to one or more points of consumption, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy delivery instances; one or more fuel delivery instances; or one or more stored energy delivery instances.

[0130] In some respects, the technology described herein relates to an AI-based platform in which a set of labor standards are associated with at least one activity performed by a miner, and transmitting at least one parameter sensed by a sensor includes transmitting an indication of the worker’s performance on at least one activity sensed by the sensor.

[0131] In some respects, the technology described herein relates to an AI-based platform in which a set of labor standards is associated with at least one object, the at least one object being associated with a miner, and transmitting at least one parameter sensed by a sensor includes transmitting an indication of the sensor's detection of the at least one object.

[0132] In some respects, the technology described herein relates to an AI-based platform in which a set of labor standards includes thresholds for mine attributes, and a reporting system is also configured to transmit determinations based on a comparison of at least one parameter sensed by a sensor with the thresholds.

[0133] In some respects, the technology described herein relates to an AI-based platform, which also includes 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 non-compliance with a set of labor standards.

[0134] In some respects, the technology described herein relates to an AI-based platform, which also includes an emergency response system configured to perform at least one emergency response action based on a determination that at least one parameter sensed by sensors indicates the occurrence of an emergency event associated with the mine.

[0135] In some respects, the technology described herein relates to an AI-based platform, which also includes a sensor configuration system configured to determine the configuration of sensors to perform sensing of at least one parameter, wherein the configuration is based on mining operations conforming to a set of labor standards.

[0136] In some respects, the technology described herein relates to an AI-based platform in which a set of labor standards is accessible and specified in natural language by a sensor configuration system, and the sensor configuration system is configured to determine sensor configuration based on natural language parsing of the set of labor standards.

[0137] In some respects, the technology described herein relates to an AI-based platform, which also includes 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. The at least one sensor repair measure includes one or more of the following: initiating a sensor replacement; initiating a diagnostic operation involving the sensor; initiating a sensor reconfiguration to detect at least one parameter in a different manner; initiating a request to a mine worker to perform manual sensing of at least one parameter; or initiating the replacement of a mine sensor with at least one other sensor in the mine to sense at least one parameter.

[0138] In some respects, the technology described herein relates to an AI-based platform, which also includes a compliance verification system configured to verify that at least one parameter sensed by a sensor indicates that mining operations comply with a set of labor standards. The verification includes one or more of the following: verifying the calibration of sensors in the mine; verifying at least one parameter sensed by sensors in the mine based on a comparison of at least one parameter sensed by at least one other sensor in the mine; requesting manual verification of at least one parameter by mine workers; or requesting a compliance officer to verify that at least one parameter indicates that mining operations comply with a set of labor standards.

[0139] In some respects, the technology described herein relates to an AI-based platform, which also includes a worker communication interface configured to participate in communication between miners based on at least one parameter sensed by sensors, wherein the communication is associated with mining operations in accordance with a set of labor standards.

[0140] In some respects, the technology described herein relates to an AI-based platform, which also includes a user interface configured to display a map of mining operations, wherein the map includes indications that the mining operations comply with a set of labor standards based on at least one parameter sensed by sensors.

[0141] In some respects, the technology described herein relates to an AI-based platform in which a set of labor standards comprises a set of work requirements for workers performing tasks associated with mining operations, and a reporting system is also configured to adapt worker assignments to tasks based on the set of work requirements.

[0142] In some respects, the technology described herein relates to an AI-based platform in which at least one parameter includes a schedule of workers performing tasks associated with mining operations, and a reporting system is also configured to adapt the schedule based on the mining operations conforming to a set of labor standards.

[0143] In some respects, the technology described herein relates to an AI-based platform in which the reporting system is also configured to initiate at least one protocol in response to at least one parameter sensed by a sensor, and the at least one protocol is based on adjusting at least one parameter sensed by the sensor to maintain or restore mining operations to compliance with a set of labor standards.

[0144] In some respects, the technology described herein relates to an AI-based platform in which the reporting system is also configured to maintain digital records of the training and / or certification status of at least one worker associated with at least one task in a mining operation.

[0145] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: a set of edge devices, wherein each of the edge devices is configured to maintain awareness of carbon generation and / or emissions of at least one of a set of entities linked to and / or managed by the set of edge devices for energy use.

[0146] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is configured to simulate carbon generation and / or emissions of at least one of a group of energy-using entities.

[0147] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0148] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0149] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is further configured to adapt to 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 factors conditions; or user configuration conditions.

[0150] In some respects, the technology described herein relates to an AI-based platform, and also 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 use priorities.

[0151] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical metrics of energy consumption for 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 plants; or one or more vehicles in a fleet.

[0152] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group 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.

[0153] In some respects, the technology described herein relates to an AI-based platform, 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 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.

[0154] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is configured to coordinate the delivery of energy to one or more points of consumption, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy delivery instances; one or more fuel delivery instances; or one or more stored energy delivery instances.

[0155] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is also 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 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.

[0156] In some respects, the technology described herein relates to an AI-based platform in which 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 dispatch system.

[0157] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is also configured to determine changes in carbon generation and / or emissions over a period of time based on a comparison of current measures of carbon generation and / or emissions with historical measures of carbon generation and / or emissions.

[0158] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is also configured to determine carbon generation and / or emission targets based on carbon generation and / or emission strategies.

[0159] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is also configured to: perform a comparison of carbon generation and / or emission metrics with carbon generation and / or emission targets; and determine, based on the comparison, the compliance of carbon generation and / or emissions with carbon generation and / or emission strategies.

[0160] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is also configured to determine the environmental impact of carbon generation and / or emissions based on carbon generation and / or emission metrics and carbon generation and / or emission targets.

[0161] In some respects, the technology described herein relates to an AI-based platform in which carbon generation and / or emissions are associated with a set of activities, and at least one edge device in the set is configured to assign at least a portion of the carbon generation and / or emissions to at least one of the activities in the set.

[0162] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is further configured to correlate at least one indicator with carbon generation and / or emission metrics to carbon generation and / or emission targets, wherein the indicator includes one or more of the following: the date, time, and / or time period of carbon generation and / or emission; the source location of carbon generation and / or emission; the direction and / or velocity of carbon generation and / or emission transport; the affected location of carbon generation and / or emission; the physical measure of carbon generation and / or emission; the chemical composition of carbon generation and / or emission; weather patterns occurring in the area associated with carbon generation and / or emission; wildlife populations in the area associated with carbon generation and / or emission; or human activities affected by carbon generation and / or emission.

[0163] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is also configured to transmit alerts associated with carbon generation and / or emissions based on a comparison of carbon generation and / or emission metrics with alert thresholds associated with carbon generation and / or emissions.

[0164] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device in the group is also configured to adjust activities associated with carbon generation and / or emissions based on carbon generation and / or emission metrics, and to adjust and modify the future state of carbon generation and / or emissions.

[0165] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of edge devices is also configured to maintain awareness by detecting measurements of carbon generation and / or emissions associated with at least one of a set of energy-using entities based on detection intervals.

[0166] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of edge devices is also configured to maintain awareness by generating at least one local report and / or alarm, and the at least one local report and / or alarm is associated with a carbon generation and / or emission pattern associated with at least one of a set of energy-using entities.

[0167] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of edge devices is further configured to alter the operation of one or more devices and / or processes associated with at least one of a set of energy-using entities, and the alteration of operation is based on at least one measurement of carbon generation and / or emissions associated with at least one of the set of energy-using entities.

[0168] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: a digital twin, which is 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 defined domain, wherein the updates to the digital twin are based on a set of energy demand parameters.

[0169] In some respects, the technology described herein relates to an AI-based platform in which a set of operational entities is controlled via a set of edge-networked devices linked to the set of operational entities, and energy demand parameters are based on one or more of the following: a set of current aggregated data derived from the demands of the set of operational entities, wherein the set of operational entities is controlled via a set of edge-networked devices linked to the set of operational entities; a set of historical aggregated data derived from the demands of the set of operational entities, wherein the set of operational entities is controlled via a set of edge-networked devices linked to the set of operational entities; or a set of simulated aggregated data derived from the demands of the set of operational entities.

[0170] In some respects, the technology described herein relates to an AI-based platform in which the data collection system is further configured to adapt to 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 factors conditions; or user configuration conditions.

[0171] In some respects, the technology described herein relates to an AI-based platform in which a 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 use priorities.

[0172] In some respects, the technology described herein relates to an AI-based platform in which the digital twin is also 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 by one or more of the following: one or more machines; one or more plants; or one or more vehicles in a fleet.

[0173] In some respects, the technology described herein relates to an AI-based platform in which at least one energy demand parameter is based on one or more of the following: on one or more public 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; or 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 operational data.

[0174] In some respects, the technology described herein relates to an AI-based platform in which 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 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.

[0175] In some respects, the technology described herein relates to an AI-based platform in which the digital twin is also configured to coordinate the delivery of energy to one or more points of consumption, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy delivery instances; one or more fuel delivery instances; or one or more stored energy delivery instances.

[0176] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0177] In some respects, the technology described in this paper relates to an AI-based platform in which digital twins are also configured to determine the carbon generation and / or emission effects of delivering energy to one or more consumption points.

[0178] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0179] In some respects, the technology described herein relates to an AI-based platform in which the digital twin is also configured to determine the delivery of energy to one or more points of consumption based on a comparison of the energy availability of each of two or more energy sources, wherein the comparison includes one or more of the following: the amount of current and / or future energy stored by at least one of the two or more energy sources, the current and / or future resource consumption associated with the acquisition, storage and / or delivery of 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.

[0180] In some respects, the technology described herein relates to an AI-based platform in which the digital twin is also 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 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.

[0181] In some respects, the technology described herein relates to an AI-based platform in which a 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 dispatch system.

[0182] In some respects, the technology described herein relates to an AI-based platform configured to measure the performance of a digital twin based on a prediction increment, wherein 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 corresponding measurement within a data collection system.

[0183] In some respects, the technology described herein relates to an AI-based platform 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 the predictive correction of the predictions applied to 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.

[0184] In some respects, the technology described herein relates to an AI-based platform in which the digital twin is also configured to generate: a forecast based on at least one energy demand parameter, and an indication of the impact of at least one energy demand parameter on the forecast.

[0185] In some respects, the technology described herein relates to an AI-based platform in which the digital twin is also configured to determine one or more modifications to a set of energy demand parameters to improve the future forecasts of the digital twin, wherein the one or more modifications include one or more of the following: one or more additional historical, current and / or forecast energy demand parameters associated with a set of fixed entities and a set of mobile entities within a defined domain; or one or more modifications to one or more of the historical, current and / or forecast energy demand parameters associated with a set of fixed entities and a set of mobile entities within a defined domain.

[0186] In some respects, the technology described herein relates to an AI-based platform in which the digital twin is also configured to coordinate the delivery of energy to one or more points of consumption based on one or more entity parameters received from at least one of a set of fixed entities and / or a set of mobile entities within a defined 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 current and / or future activities associated with energy consumption performed by at least one entity.

[0187] In some respects, the technology described herein relates to an AI-based platform in which the digital twin is also configured to transmit a request to at least one of a set of fixed entities and / or a set of mobile entities within a defined domain to adjust one or more entity parameters associated with the at least one entity, 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 current and / or future activities performed by the at least one entity in connection with energy consumption.

[0188] In some respects, the technology described herein relates to an AI-based platform in which 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 two of a set of fixed 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.

[0189] In some respects, the technology described herein relates to an AI-based platform in which a digital twin is associated with at least one physical machine, which is associated with one or two of a set of fixed 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 update detection of the output of a process performed by the at least one physical machine.

[0190] In some respects, the technology described in this paper relates to an AI-based platform in which the digital twin is updated by a data collection system based on power-saving and energy-consuming strategies associated with a set of energy demand parameters.

[0191] In some respects, the technology described herein relates to an AI-based platform for enabling 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.

[0192] In some respects, the technology described in this paper relates to an AI-based platform in which local demand requirements are predicted through a set of demand forecasting algorithms operating on a set of edge networked devices linked to a set of energy-consuming systems.

[0193] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0194] In some respects, the technology described herein relates to an AI-based platform in which 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 of local demand requirements.

[0195] In some respects, the technology described herein relates to an AI-based platform in which at least one modular distributed energy system in the group is configured by the AI-based platform to generate energy at local demand points.

[0196] In some respects, the technology described herein relates to an AI-based platform in which at least one modular distributed energy system in the group is configured by the AI-based platform to deliver modular power generation systems to demand locations.

[0197] In some respects, the technology described herein relates to an AI-based platform in which at least one modular distributed energy system in the group is configured by the AI-based platform to route energy delivery from a set of energy delivery facilities to the demand location.

[0198] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0199] In some respects, the technology described herein relates to an AI-based platform in which at least one modular distributed energy system in the group is also configured to adapt to data transmission via 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 factors conditions; or user configuration conditions.

[0200] In some respects, the technology described herein relates to an AI-based platform, and also 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 use priorities.

[0201] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical metrics of energy consumption for 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 plants; or one or more vehicles in a fleet.

[0202] In some respects, the technology described herein relates to an AI-based platform in which at least one modular distributed energy 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.

[0203] In some respects, the technology described herein relates to an AI-based platform where local requirements are based on one or more of the following: on one or more public 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; or 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 operational data.

[0204] In some respects, the technology described herein relates to an AI-based platform, which also includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained 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.

[0205] In some respects, the technology described herein relates to an AI-based platform in which at least one modular distributed energy system is configured to coordinate the delivery of energy to one or more points of consumption, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy delivery instances; one or more fuel delivery instances; or one or more stored energy delivery instances.

[0206] In some respects, the technology described herein relates to an AI-based platform in which a first system of a 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, transmission and / or consumption of one or both of the first or second systems.

[0207] In some respects, the technology described herein relates to an AI-based platform in which at least one modular distributed energy system is configured to adjust the delivery of energy to one or more consumption points based on carbon generation and / or emission strategies.

[0208] In some respects, the technology described herein relates to an AI-based platform in which 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, the one or more energy-related events including 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.

[0209] In some respects, the technology described herein relates to an AI-based platform in which 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 dispatch system.

[0210] In some respects, the technology described herein relates to an AI-based platform in which at least one modular distributed energy system is associated with a digital twin, the digital twin being configured to model and / or predict one or more attributes and / or operations of the at least one modular distributed energy system.

[0211] In some respects, the technology described in this paper relates to an AI-based platform in which a set of modular distributed energy systems can be configured to change the amount of reserved capacity to adapt to energy demand patterns associated with local demand requirements.

[0212] In some respects, the technology described herein relates to an AI-based platform in which a set of modular distributed energy systems can be configured to change the location of energy supply and / or access to resources based on measurements and / or forecasts of local demand requirements.

[0213] In some respects, the technology described in this paper relates to an AI-based platform in which a set of modular distributed energy systems can be configured to alter energy production schedules based on measurements and / or forecasts of local demand requirements.

[0214] In some respects, the technology described in this paper relates to an AI-based platform in which 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 allocate a subset based on local demand requirements.

[0215] In some respects, the technology described herein relates to an AI-based platform for enabling 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, at least partially independent of the power grid; and output a set of operational parameters to provide energy generation, storage, and / or consumption to enable the operational process, wherein the set of operational parameters is based on the analysis.

[0216] In some respects, the technology described in this paper relates to an AI-based platform in which at least one of a set of operating parameters is the generation output level of distributed energy generation resources.

[0217] In some respects, the technology described in this paper relates to an AI-based platform in which at least one of a set of operating parameters is the target storage level of the distributed energy storage resource.

[0218] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of operating parameters is the delivery time of a distributed energy transmission resource.

[0219] In some respects, the technology described herein relates to an AI-based platform in which the artificial intelligence system is also configured to adapt to 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 factors conditions; or user configuration conditions.

[0220] In some respects, the technology described herein relates to an AI-based platform, and also 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 use priorities.

[0221] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical metrics of energy consumption for 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 plants; or one or more vehicles in a fleet.

[0222] In some respects, the technology described herein relates to an AI-based platform in which the artificial intelligence system is also 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.

[0223] In some respects, the technology described herein relates to an AI-based platform in which at least one operational parameter is based on one or more of the following: one or more public 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; or 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 operational data.

[0224] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is trained 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.

[0225] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is configured to coordinate the delivery of energy to one or more points of consumption, and the energy delivery includes one or more of the following: one or more fixed transmission lines; one or more wireless energy delivery instances; one or more fuel delivery instances; or one or more stored energy delivery instances.

[0226] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is also 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 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.

[0227] In some respects, the technology described herein relates to an AI-based platform in which an 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 dispatch system.

[0228] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is also 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.

[0229] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is also configured to assess the compliance of an operational process with one or both of the following: carbon generation and / or emission strategies, or a set of labor standards associated with the operational process.

[0230] In some respects, the technology described herein relates to an AI-based platform in which the artificial intelligence system is also configured to adjust a set of operating parameters based on one or both of the following to provide energy generation, storage, and / or consumption associated with the operating process: carbon generation and / or emission strategies, or a set of labor standards associated with the operating process.

[0231] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is also configured to transmit messages to at least one of a set of edge devices associated with an operational process, and the messages include requests to adjust at least one operation of at least one edge device based on a set of operational parameters.

[0232] In some respects, the technology described herein relates to an AI-based platform in which the artificial intelligence system is also configured to receive an indicator of the current and / or predicted state of energy of at least one edge device from a set of edge devices associated with the operation process, and a set of operating parameters is based on the indicator of the current and / or predicted state of energy of at least one edge device.

[0233] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is also configured to determine a set of operational parameters based on the output of a digital twin, the digital twin representing at least one edge device from a set of edge devices associated with the operational process, and the output of the digital twin indicating the current and / or predicted energy state of at least one edge device.

[0234] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is also configured to coordinate a set of modular distributed energy systems to generate, store, and / or deliver energy, wherein coordination is based on a set of operating parameters and local demand requirements.

[0235] In some respects, the technology described herein relates to an AI-based platform in which the analysis of energy patterns associated with the operation process includes analyzing the availability of backup power based on faults in at least a portion of the power grid.

[0236] In some respects, the technology described herein relates to an AI-based platform in which the analysis of energy patterns associated with the operation process includes the analysis of at least one auxiliary function associated with a set of resources, and a set of operating parameters includes at least one operating parameter associated with at least one auxiliary function.

[0237] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: a policy and governance engine configured to deploy a set of rules and / or policies governing a set of energy generation, storage, and / or consumption workloads, wherein the rules and / or policies are associated with a configuration of a set of edge devices that perform local data communication operations with a set of energy generation facilities, energy storage facilities, energy transmission facilities, or energy consumption systems.

[0238] In some respects, the technology described herein relates to an AI-based platform in which, when configured in a policy and governance engine, a policy associated with energy generation instructions is automatically applied by at least one edge device to control energy generation of at least one energy generation system controlled via the edge device.

[0239] In some respects, the technology described herein relates to an AI-based platform in which, when configured in a policy and governance engine, policies associated with energy consumption commands are automatically applied by at least one edge device to control the energy consumption of at least one energy consumption system controlled via the edge device.

[0240] In some respects, the technology described herein relates to an AI-based platform in which, when configured in a policy and governance engine, a policy associated with energy delivery instructions is automatically applied by at least one edge device to control energy delivery of at least one energy delivery system controlled by the edge device.

[0241] In some respects, the technology described herein relates to an AI-based platform in which, 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 energy storage in at least one energy storage system controlled via the edge device.

[0242] In some respects, the technology described in this paper relates to an AI-based platform in which a policy and governance engine is configured to operate on a stored set of policy templates in order to configure policies.

[0243] In some respects, the technology described in this paper relates to an AI-based platform in which a set of recommended policies is automatically generated based on a dataset of historical policies, a dataset representing the operational states and / or configurations of a set of distributed energy resources, and a set of historical results, to be presented in a policy and governance engine.

[0244] In some respects, the technology described herein relates to an AI-based platform in which the strategy and governance engine is also configured to adjust rules and / or strategies based on at least one contextual factor, and the at least one contextual factor includes at least one of the following: historical data on energy trading; at least one operational factor; at least one market factor; at least one expected market behavior; or at least one expected customer behavior.

[0245] In some respects, the technology described herein relates to an AI-based platform in which a policy and governance engine is also configured to adapt to 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 factors conditions; or user configuration conditions.

[0246] In some respects, the technology described herein relates to an AI-based platform, and also includes an adaptive energy digital twin, which 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 use priorities.

[0247] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption for at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0248] In some respects, the technology described herein relates to an AI-based platform, which also includes 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.

[0249] In some respects, the technology described herein relates to an AI-based platform in which the policy and governance engine is also configured to perform at least one 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.

[0250] In some respects, the technology described herein relates to an AI-based platform in which at least one rule and / or policy is based on at least one public data resource, which includes at least one of the following: weather data resources; satellite data resources; census, population, demographic and / or psychological data resources; market data resources; or e-commerce data resources.

[0251] In some respects, the technology described herein relates to an AI-based platform in which at least one rule and / or strategy is based on at least one enterprise data resource, which 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.

[0252] In some respects, the technology described herein relates 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 on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or tag; 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.

[0253] In some respects, the technology described herein relates to an AI-based platform in which a policy and governance engine is 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 instance of wireless energy delivery; at least one fuel delivery; or at least one stored energy delivery.

[0254] In some respects, the technology described herein relates to an AI-based platform in which a policy and governance engine is also configured to record at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one 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.

[0255] In some respects, the technology described herein relates to an AI-based platform in which a 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.

[0256] In some respects, the technology described herein relates to an AI-based platform in which a strategy and governance engine is also configured to generate and / or execute at least one smart contract, each of which applies rules and / or strategies to at least one energy-related transaction.

[0257] In some respects, the technology described herein relates to an AI-based platform in which a set of rules and / or policies are based on at least one objective associated with a set of energy generation, storage, and / or consumption workloads, and a policy and governance engine is also configured to deploy updates to the set of rules and / or policies to a set of edge devices based on the objective.

[0258] In some respects, the technology described herein relates to an AI-based platform in which a policy and governance engine is also configured to deploy at least one instruction to a set of edge devices to adapt to at least one operational parameter associated with at least one industrial machine and / or industrial process controlled by the set of edge devices.

[0259] In some respects, the technology described herein relates to an AI-based platform for enabling 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 to automatically execute a set of pre-configured policies that manage the energy generation, energy storage, or energy consumption of the respective energy generation facility, energy storage facility, or energy consumption system.

[0260] In some respects, the technology described in this paper relates to an AI-based platform in which the automatically executed strategy is a set of contextual strategies that are adjusted based on the current state of a set of energy-generating entities in the energy grid.

[0261] In some respects, the technology described herein relates to an AI-based platform in which automatically executed strategies are a set of contextual strategies that are adjusted based on the current state of a set of energy-generating entities in an energy-generating environment, including an energy grid and a set of distributed energy sources that operate independently of the energy grid.

[0262] In some respects, the technology described in this paper relates to an AI-based platform in which the automatically executed strategy is a set of contextual strategies that are adjusted based on the current state of a set of energy storage entities in the energy grid.

[0263] In some respects, the technology described herein relates to an AI-based platform in which automatically executed strategies are a set of contextual strategies that are adjusted based on the current state of a set of energy storage entities in an energy storage environment, including an energy grid and a set of distributed energy resources that operate independently of the energy grid, wherein the automatically executed strategies are a set of contextual strategies that are adjusted based on the current state of a set of energy delivery entities in the energy grid.

[0264] In some respects, the technology described herein relates to an AI-based platform in which automatically executed strategies are a set of contextual strategies that are adjusted based on the current state of a set of energy transmission entities in an energy transmission environment, including an energy grid and a set of distributed energy resources that operate independently of the energy grid.

[0265] In some respects, the technology described herein relates to an AI-based platform in which the automatically executed strategy is a set of contextual strategies that are adjusted based on the current state of a set of energy-consuming entities that consume energy from the energy grid.

[0266] In some respects, the technology described herein relates to an AI-based platform in which the automatically executed strategy is a set of contextual strategies that are adjusted based on the current state of a set of energy-consuming entities that consume energy from the energy grid and from a set of distributed energy resources that operate independently of the energy grid.

[0267] In some respects, the technology described herein relates to an AI-based platform in which a set of edge devices is further configured to adjust a set of pre-configured strategies based on at least one contextual factor, and the at least one contextual factor includes at least one of the following: historical data on energy trading; at least one operational factor; at least one market factor; at least one expected market behavior; or at least one expected customer behavior.

[0268] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device is further configured to adapt to 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 factors conditions; or user configuration conditions.

[0269] In some respects, the technology described herein relates to an AI-based platform, and also includes an adaptive energy digital twin, which 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 use priorities.

[0270] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption for at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0271] In some respects, the technology described herein relates to an AI-based platform, which also includes 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.

[0272] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0273] In some respects, the technology described herein relates to an AI-based platform in which at least one pre-configured strategy is based on at least one public data resource, which includes at least one of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0274] In some respects, the technology described herein relates to an AI-based platform in which at least one pre-configured strategy is based on at least one enterprise data resource, which 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.

[0275] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or tag; 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.

[0276] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device is also 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 instance of wireless energy delivery; at least one fuel delivery; or at least one stored energy delivery.

[0277] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one 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.

[0278] In some respects, the technology described herein relates to an AI-based platform in which 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 dispatch system.

[0279] In some respects, the technology described herein relates to an AI-based platform in which a set of edge devices are further configured to: determine at least one energy availability mode based on communication with at least one energy generation facility, energy storage facility and / or energy consumption system, and to update the execution of a set of pre-configured strategies based on at least one mode.

[0280] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of edge devices is configured to manage the operation of an industrial facility, and a set of pre-configured strategies are based on at least one energy target associated with the industrial facility.

[0281] In some respects, the technology described herein relates to an AI-based platform in which at least one energy generation facility, energy storage facility, and / or energy consumption system is located in a geographic area, and a set of pre-configured strategies are based on at least one energy objective associated with the geographic area.

[0282] In some respects, the technology described herein relates to an AI-based platform in which a set of edge devices is configured to automatically execute a set of pre-configured strategies by adjusting at least one of the schedules of processes performed by at least one energy generation facility, energy storage facility, and / or energy consumption system, or by adjusting the allocation of energy resources associated with at least one energy generation facility, energy storage facility, and / or energy consumption system.

[0283] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: a machine learning system trained on a set of energy intelligence data and deployed on edge devices, wherein the machine learning system is configured to receive additional training from the edge devices to improve energy management.

[0284] In some respects, the technology described in this paper relates to an AI-based platform in which energy management includes managing the energy generation of a set of distributed energy generation resources.

[0285] In some respects, the technology described in this paper relates to an AI-based platform in which energy management includes managing energy storage of a set of distributed energy storage resources.

[0286] In some respects, the technology described in this paper relates to an AI-based platform in which energy management includes managing the energy delivery of a set of distributed energy delivery resources.

[0287] In some respects, the technology described in this paper relates to an AI-based platform in which energy management includes managing the energy consumption of a set of distributed energy consumption resources.

[0288] In some respects, the technology described herein relates to an AI-based platform in which 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 transmission facilities, or energy consumption systems.

[0289] In some respects, the technology described herein relates to an AI-based platform in which the machine learning system is further configured to adapt to 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 factors conditions; or user configuration conditions.

[0290] In some respects, the technology described herein relates to an AI-based platform, and also includes an adaptive energy digital twin, which 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 use priorities.

[0291] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption for at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0292] In some respects, the technology described herein relates to an AI-based platform, which also includes 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.

[0293] In some respects, the technology described herein relates to an AI-based platform in which the machine learning 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.

[0294] In some respects, the technology described herein relates to an AI-based platform in which energy intelligence data is based on at least one public data resource, which includes at least one of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0295] In some respects, the technology described herein relates to an AI-based platform in which energy intelligence data is based on at least one enterprise data resource, which 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.

[0296] In some respects, the technology described herein relates to an AI-based platform in which the machine learning system is also trained on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or tag; at least one human interaction with the 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.

[0297] In some respects, the technology described herein relates to an AI-based platform in which a machine learning system is also 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 instance of wireless energy delivery; at least one fuel delivery; or at least one stored energy delivery.

[0298] In some respects, the technology described herein relates to an AI-based platform in which a machine learning system is also configured to record at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one 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.

[0299] In some respects, the technology described herein relates to an AI-based platform in which 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 dispatch system.

[0300] In some respects, the technology described herein relates to an AI-based platform in which edge devices are located near at least one entity that generates, stores, delivers, and / or uses energy.

[0301] In some respects, the technology described herein relates to an AI-based platform in which edge devices provide information about the energy status and / or energy flow of at least one entity that generates, stores, transmits, and / or uses energy.

[0302] In some respects, the technology described herein relates to an AI-based platform in which an edge device includes and / or governs at least one of a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transmit and / or use energy.

[0303] In some respects, the technology described herein relates to an AI-based platform in which edge devices are associated with situations and / or environments, and the edge devices are also configured to perform additional training of a machine learning system in response to changes in situations and / or environments.

[0304] In some respects, the technology described in this paper relates to an AI-based platform in which edge devices are also configured to perform additional training of the machine learning system based on the machine learning system's determination of model drift.

[0305] In some respects, the technology described in this paper relates to an AI-based platform in which additional training is based on a set of energy intelligence data initially trained by the machine learning system and additional energy intelligence data not yet trained by the machine learning system.

[0306] In some respects, the technology described herein relates to an AI-based platform in which additional training involves adding a machine learning system to a collection that includes at least one other artificial intelligence system.

[0307] In some respects, the technology described herein relates to an AI-based platform in which a set of energy intelligence data is based on at least one energy-related policy and / or rule, and additional training is based on variations of at least one energy-related policy and / or rule.

[0308] In some respects, the technology described herein relates to an AI-based platform for enabling 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, the set of artificial intelligence systems being configured to: process data processed by the edge devices; and based on the data, determine a mixture of energy generation, storage, transmission and / or consumption characteristics of the set of systems communicating locally with the edge devices, and output a dataset representing the compositional proportions of the mixture.

[0309] In some respects, the technology described in this paper relates to an AI-based platform in which the output dataset indicates the fraction of energy generated by the energy grid and the fraction of energy generated by a set of distributed energy sources that operate independently of the energy grid.

[0310] In some respects, the technology described in this paper relates to an AI-based platform in which the output dataset indicates the fraction of energy generated from renewable energy sources and the fraction of energy generated from non-renewable resources.

[0311] In some respects, the technology described in this paper relates to an AI-based platform in which the output dataset indicates the energy generated by type for each interval in a series of time intervals.

[0312] In some respects, the technology described herein relates to an AI-based platform in which the output dataset indicates carbon generation associated with energy generation of each energy type in an energy mix during each interval of a series of time intervals.

[0313] In some respects, the technology described in this paper relates to an AI-based platform in which the output dataset indicates the carbon emissions associated with energy generation for each type of energy in an energy mix during each interval of a series of time intervals.

[0314] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device is further configured to adapt to 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 factors conditions; or user configuration conditions.

[0315] In some respects, the technology described herein relates to an AI-based platform, and also includes an adaptive energy digital twin, which 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 use priorities.

[0316] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption for at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0317] In some respects, the technology described herein relates to an AI-based platform, which also includes 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.

[0318] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0319] In some respects, the technology described herein relates to an AI-based platform in which data is based on at least one public data resource, which includes at least one of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0320] In some respects, the technology described herein relates to an AI-based platform in which data is based on at least one enterprise data resource, which 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.

[0321] In some respects, the technology described herein relates to an AI-based platform in which 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 being trained on a training dataset, and the training dataset being based on at least one of the following: at least one human label and / or tag; 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.

[0322] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device is also 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 instance of wireless energy delivery; at least one fuel delivery; or at least one stored energy delivery.

[0323] In some respects, the technology described herein relates to an AI-based platform in which at least one edge device is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one 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.

[0324] In some respects, the technology described herein relates to an AI-based platform in which 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 dispatch system.

[0325] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0326] In some respects, the technology described herein relates to an AI-based platform in which a set of edge devices provides information on the energy status and / or energy flow of at least one entity that generates, stores, transmits, and / or uses energy.

[0327] In some respects, the technology described herein relates to an AI-based platform in which a set of edge devices includes and / or governs at least one of a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transmit and / or use energy.

[0328] In some respects, the technology described herein relates to an AI-based platform in which a mixture of energy generation, storage, transmission, and / or consumption characteristics is based on at least one energy demand requirement associated with a set of edge devices.

[0329] In some respects, the technology described herein relates to an AI-based platform in which a blend of energy generation, storage, transmission, and / or consumption characteristics is based on the prioritization of energy collection, storage, transmission, and / or use associated with each energy source and a set of edge devices.

[0330] In some respects, the technology described herein relates to an AI-based platform in which a blend of energy generation, storage, transmission, and / or consumption characteristics is based on a schedule of storage, transmission, and / or use associated with each energy source and a set of edge devices.

[0331] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: a data processing system configured to merge at least one entity of a power grid entity generating, storing, transmitting, or consuming a power grid dataset with at least one entity of an off-grid energy entity generating, storing, transmitting, and / or consuming a power grid dataset.

[0332] In some respects, the technology described in this paper relates to an AI-based platform in which a data processing system is configured to automatically time-align energy grid entity data with off-grid energy entity data.

[0333] In some respects, the technology described herein relates to an AI-based platform in which a data processing system is configured to automatically collect sensor data from a set of edge devices and control a set of off-grid energy entities via the set of edge devices.

[0334] In some respects, the technology described in this paper relates to an AI-based platform in which a data processing system is configured to automatically normalize energy grid entity data and off-grid energy entity data in order to present the data according to a set of common units.

[0335] In some respects, the technology described herein relates to an AI-based platform in which the data processing system is further configured to adapt to 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 factors conditions; or user configuration conditions.

[0336] In some respects, the technology described herein relates to an AI-based platform, and also includes an adaptive energy digital twin, which 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 use priorities.

[0337] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption for at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0338] In some respects, the technology described herein relates to an AI-based platform, which also includes 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.

[0339] In some respects, the technology described herein relates to an AI-based platform in which the 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.

[0340] In some respects, the technology described herein relates 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 on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or tag; 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.

[0341] In some respects, the technology described herein relates to an AI-based platform in which a data processing system is also 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 instance of wireless energy delivery; at least one fuel delivery; or at least one stored energy delivery.

[0342] In some respects, the technology described herein relates to an AI-based platform in which a data processing system is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one 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.

[0343] In some respects, the technology described herein relates to an AI-based platform in which 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.

[0344] In some respects, the technology described herein relates to an AI-based platform in which a data processing system is also configured to intelligently coordinate and manage electricity and / or energy based on a dataset of energy generation, storage, and / or consumption data from a set of infrastructure assets, and the dataset is generated at least in part by a set of sensors contained in and / or managed by a set of edge devices.

[0345] In some respects, the technology described herein relates to an AI-based platform in which the data processing system is also configured to manage at least one of the following: generating energy from a set of distributed energy generation resources; storing energy from a set of distributed energy storage resources; transporting energy from a set of distributed energy transmission resources; or consuming energy from a set of distributed energy consumption resources.

[0346] In some respects, the technology described herein relates to an AI-based platform in which a data processing system is also configured to intelligently coordinate and manage the electricity and / or energy of a set of entities, wherein the set of entities includes at least one of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0347] In some respects, the technology described herein relates to an AI-based platform in which a data processing system is further configured to execute at least one algorithm that simulates the energy consumption of at least one entity, wherein the simulation is based on a dataset that includes alternative state or event parameters of at least one entity that reflect alternative consumption, and the algorithm accesses a demand response model that describes how energy demand responds to changes in energy prices or the prices of energy-consuming operations or activities.

[0348] In some respects, the technology described herein relates to an AI-based platform in which a 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.

[0349] In some respects, the technology described herein relates to an AI-based platform in which a data processing system includes an analysis system that represents a set of operational parameters and current state of at least one entity based on a set of sensed parameters 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 the at least one entity or at least one additional available entity.

[0350] In some respects, the technology described herein relates to an AI-based platform in which a data processing system includes an artificial intelligence system trained on a historical dataset relating to energy generation, storage, and / or utilization of operational processes associated with at least one entity, and the data processing system is further configured to: analyze energy patterns of the operational processes and, based on the current state and / or information associated with at least one entity, output an energy demand forecast for the operational processes.

[0351] In some respects, the technology described herein relates to an AI-based platform in which a data processing system is further configured to merge power grid datasets of energy grid entities generating, storing, transmitting, or consuming power grid data and off-grid energy entity datasets of generating, storing, transmitting, and / or consuming power grid data with at least one entity of a backup and / or auxiliary energy generation, storage, transmission, or consumption power grid dataset.

[0352] In some respects, the technology described herein relates to an AI-based platform in which a data processing system is also configured to coordinate the development of energy grid resources and / or off-grid energy resources based on fused energy grid entity generation, storage, transmission, or consumption grid datasets and off-grid energy entity generation, storage, transmission, and / or consumption datasets.

[0353] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: a set of autonomous coordination systems for delivering a set of heterogeneous energy types to consumption points based on improvements to: the location of the consumption points, and a set of consumption attributes including at least one of: peak electricity demand at the consumption point; continuity of electricity demand at the consumption point; and the type of energy available at the consumption point.

[0354] In some respects, the technology described in this paper relates to an AI-based platform in which a set of autonomously coordinated systems coordinates the delivery of energy generation capacity of a defined type to consumption points.

[0355] In some respects, the technology described in this paper involves an AI-based platform in which a set of autonomously coordinated systems coordinates the delivery of energy storage capacity of a defined type to the point of consumption.

[0356] In some respects, the technology described in this paper relates to an AI-based platform in which the type of energy that can be used is determined, at least in part, based on a set of operational compatibility parameters.

[0357] In some respects, the technology described in this paper relates to an AI-based platform in which the type of energy available is determined, at least in part, based on a set of governance parameters.

[0358] In some respects, the technology described in this paper relates to an AI-based platform in which a set of governance parameters pertains to the use of renewable energy.

[0359] In some respects, the technology described in this paper involves an AI-based platform in which a set of governance parameters relates to carbon generation or emissions.

[0360] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of autonomously coordinated systems is further configured to adapt to 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 factors conditions; or user configuration conditions.

[0361] In some respects, the technology described herein relates to an AI-based platform, and also includes an adaptive energy digital twin, which 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 use priorities.

[0362] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption for at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0363] In some respects, the technology described herein relates to an AI-based platform, which also includes 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.

[0364] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of autonomously coordinated systems 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.

[0365] In some respects, the technology described herein relates to an AI-based platform in which at least one consumer attribute is based on at least one public data resource, which includes at least one of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0366] In some respects, the technology described herein relates to an AI-based platform in which at least one consumer attribute is based on at least one enterprise data resource, which 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.

[0367] In some respects, the technology described herein relates 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 on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or tag; 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.

[0368] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of autonomously coordinated systems 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 instance of wireless energy delivery; at least one fuel delivery; or at least one stored energy delivery.

[0369] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of autonomously coordinated systems is further configured to record at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one 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.

[0370] In some respects, the technology described herein relates to an AI-based platform in which at least one of a set of autonomously coordinated 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 dispatch system.

[0371] In some respects, the technology described herein relates to an AI-based platform in which a set of autonomous coordination systems is also configured to determine the delivery of a set of heterogeneous energy types based on a set of rules and / or policies for managing 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.

[0372] In some respects, the technology described herein relates to an AI-based platform in which a set of autonomously coordinated systems are also configured to determine the delivery of a set of heterogeneous energy types based on energy consumption simulations of at least one energy consumer, the simulations being based on a dataset including at least one of the at least one energy consumer’s alternative state or event parameters reflecting alternative consumption, and the simulations being based on a demand response model that describes how energy demand responds to changes in energy prices or the prices of energy-consuming operations or activities.

[0373] In some respects, the technology described in this paper relates to an AI-based platform in which a set of autonomously coordinated systems improves the delivery of a set of heterogeneous energy types to a point of consumption by matching each of the set of heterogeneous energy types with at least one consumer associated with the point of consumption.

[0374] In some respects, the technology described in this paper relates to an AI-based platform in which a set of autonomously coordinated systems improves the delivery of a set of heterogeneous energy types to consumption points by determining the development of additional energy sources of one or more energy types and developing forecasts of energy demand associated with consumption points.

[0375] In some respects, the technology described in this paper relates to an AI-based platform in which a set of autonomously coordinated systems improves the delivery of a set of heterogeneous energy types to a point of consumption by comparing the characteristics of energy demand associated with a point of consumption with the characteristics of each of a set of heterogeneous energy types.

[0376] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: an intelligent agent trained on a dataset of interactions with experts in the energy supply system, wherein the intelligent agent is trained to generate at least one recommendation and / or instruction relative to at least one energy objective and at least one other objective.

[0377] In some respects, the technology described in this article involves an AI-based platform where another objective is the operational goal of the enterprise.

[0378] In some respects, the technology described in this paper relates to an AI-based platform in which an intelligent agent operates on state data from a set of edge devices, thereby controlling a set of energy-generating resources via the set of edge devices.

[0379] In some respects, the technology described in this paper relates to an AI-based platform in which an intelligent agent operates on state data from a set of edge devices, thereby controlling a set of energy-consuming resources via the set of edge devices.

[0380] In some respects, the technology described in this paper relates to an AI-based platform in which an intelligent agent operates on state data from a set of edge devices, thereby controlling a set of energy storage resources via the edge devices.

[0381] In some respects, the technology described in this paper relates to an AI-based platform in which an intelligent agent operates on state data from a set of edge devices, thereby controlling a set of energy delivery resources via the set of edge devices.

[0382] In some respects, the technology described herein relates to an AI-based platform in which an intelligent agent is further configured to adapt to 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 factors conditions; or user configuration conditions.

[0383] In some respects, the technology described herein relates to an AI-based platform, and also includes an adaptive energy digital twin, which 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 use priorities.

[0384] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption for at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0385] In some respects, the technology described herein relates to an AI-based platform, which also includes 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.

[0386] In some respects, the technology described herein relates to an AI-based platform in which an intelligent agent is also 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.

[0387] In some respects, the technology described herein relates to an AI-based platform in which the dataset is based on at least one public data resource, which includes at least one of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0388] In some respects, the technology described herein relates to an AI-based platform in which the dataset is based on at least one enterprise data resource, which 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.

[0389] In some respects, the technology described herein relates to an AI-based platform in which an intelligent agent is trained on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or tag; 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.

[0390] In some respects, the technology described herein relates to an AI-based platform in which an intelligent agent is also 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 instance of wireless energy delivery; at least one fuel delivery; or at least one stored energy delivery.

[0391] In some respects, the technology described herein relates to an AI-based platform in which a smart agent is also configured to record at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one 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.

[0392] In some respects, the technology described herein relates to an AI-based platform in which an 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.

[0393] In some respects, the technology described herein relates to an AI-based platform in which an intelligent agent is located near at least one entity that generates, stores, delivers, and / or uses energy.

[0394] In some respects, the technology described herein relates to an AI-based platform in which an intelligent agent provides information on the energy status and / or energy flow of at least one entity that generates, stores, transmits, and / or uses energy.

[0395] In some respects, the technology described herein relates to an AI-based platform in which an intelligent agent manages at least one of a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transmit and / or use energy.

[0396] In some respects, the technology described herein relates to an AI-based platform in which an intelligent agent is also configured to manage at least one processing task associated with at least one device, and at least one suggestion and / or instruction includes adjusting at least one processing task based on at least one energy target and / or at least one other target.

[0397] In some respects, the technology described herein relates to an AI-based platform in which the intelligent agent is also 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.

[0398] In some respects, the technology described herein relates to an AI-based platform in which a smart agent is also configured to exchange information with at least one other smart agent, and the information is based on one or both of at least one suggestion and / or instruction, or at least one energy target and / or at least one other target.

[0399] In some respects, the technology described herein relates to an AI-based platform in which recommendations and / or instructions are associated with at least one device, and smart agents are also configured to exchange collected and / or determined data associated with at least one device with at least one other smart agent.

[0400] In some respects, the technology described herein relates to an AI-based platform for achieving 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 operational parameter that satisfies the energy demand of both mobile entities or fixed locations within a defined domain.

[0401] In some respects, the technology described in this paper relates to an AI-based platform, where the defined domain includes a defined geographic location and a defined time period.

[0402] In some respects, the technology described herein relates to an AI-based platform in which at least one operational parameter indicates generation instructions for a set of energy-generating resources.

[0403] In some respects, the technology described herein relates to an AI-based platform in which at least one operational parameter indicates storage instructions for a set of energy storage resources.

[0404] In some respects, the technology described herein relates to an AI-based platform in which at least one operational parameter indicates delivery instructions for a set of energy delivery resources.

[0405] In some respects, the technology described herein relates to an AI-based platform in which at least one operational parameter indicates consumption instructions for a set of entities that consume energy.

[0406] In some respects, the technology described herein relates to an AI-based platform in which the artificial intelligence system is also configured to adapt to 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 factors conditions; or user configuration conditions.

[0407] In some respects, the technology described herein relates to an AI-based platform, and also includes an adaptive energy digital twin, which 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 use priorities.

[0408] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption for at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0409] In some respects, the technology described herein relates to an AI-based platform, which also includes 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.

[0410] In some respects, the technology described herein relates to an AI-based platform in which the artificial intelligence system is also 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.

[0411] In some respects, the technology described herein relates to an AI-based platform in which the dataset is based on at least one public data resource, which includes at least one of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0412] In some respects, the technology described herein relates to an AI-based platform in which the dataset is based on at least one enterprise data resource, which 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.

[0413] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is trained on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or tag; 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.

[0414] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is also 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 instance of wireless energy delivery; at least one fuel delivery; or at least one stored energy delivery.

[0415] In some respects, the technology described herein relates to an AI-based platform in which the artificial intelligence system is also configured to record at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one 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.

[0416] In some respects, the technology described herein relates to an AI-based platform in which an 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 dispatch system.

[0417] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is located near at least one entity that generates, stores, transmits, and / or uses energy.

[0418] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system provides information on the energy status and / or energy flow of at least one entity that generates, stores, transmits, and / or uses energy.

[0419] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system manages at least one of a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transmit and / or use energy.

[0420] In some respects, the technology described herein relates to an AI-based platform in which the domain includes at least one boundary and the dataset is constrained based on at least one boundary associated with the domain.

[0421] In some respects, the techniques described herein relate to an AI-based platform in which recommendations are made based on at least one constraint associated with at least one operational parameter, and the artificial intelligence system is trained to analyze datasets based on at least one constraint.

[0422] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: an artificial intelligence system configured to: analyze a dataset of monitored local conditions and generate a recommended configuration for at least one of a set of distributed systems, each of which can be configured to produce and consume energy, wherein the configuration causes at least one distributed system to produce and / or consume energy based on the monitored local conditions.

[0423] In some respects, the technology described in this paper relates to an AI-based platform in which multiple distributed systems are configured in a group of artificial intelligence systems, enabling a set of aggregated performance requirements to be met across the multiple distributed systems.

[0424] In some respects, the technology described in this paper relates to an AI-based platform where the aggregate performance requirements are a set of economic performance requirements.

[0425] In some respects, the technology described in this article relates to an AI-based platform where aggregated performance requirements are a set of regulatory performance requirements.

[0426] In some respects, the technology described in this paper involves an AI-based platform in which aggregated performance requirements are related to carbon generation or emissions.

[0427] In some respects, the technology described in this paper relates to an AI-based platform where aggregate performance requirements are a set of consumption requirements.

[0428] In some respects, the technology described herein relates to an AI-based platform in which the artificial intelligence system is also configured to adapt to 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 factors conditions; or user configuration conditions.

[0429] In some respects, the technology described herein relates to an AI-based platform, and also includes an adaptive energy digital twin, which 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 use priorities.

[0430] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption for at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0431] In some respects, the technology described herein relates to an AI-based platform, which also includes 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.

[0432] In some respects, the technology described herein relates to an AI-based platform in which the artificial intelligence system is also 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.

[0433] In some respects, the technology described herein relates to an AI-based platform in which the dataset is based on at least one public data resource, which includes at least one of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0434] In some respects, the technology described herein relates to an AI-based platform in which the dataset is based on at least one enterprise data resource, which 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.

[0435] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is also 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 instance of wireless energy delivery; at least one fuel delivery; or at least one stored energy delivery.

[0436] In some respects, the technology described herein relates to an AI-based platform in which the artificial intelligence system is also configured to record at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one 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.

[0437] In some respects, the technology described herein relates to an AI-based platform in which an 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 dispatch system.

[0438] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is trained on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or tag; 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.

[0439] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system is located near at least one entity that generates, stores, transmits, and / or uses energy.

[0440] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system provides information on the energy status and / or energy flow of at least one entity that generates, stores, transmits, and / or uses energy.

[0441] In some respects, the technology described herein relates to an AI-based platform in which an artificial intelligence system manages at least one of a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, transmit and / or use energy.

[0442] In some respects, the technology described in this paper relates to an AI-based platform in which recommended configurations are based on at least one auxiliary power resource associated with a set of distributed systems.

[0443] In some respects, the technology described herein relates to an AI-based platform in which recommended configurations are based on at least one of the following: the current and / or predicted location of at least one of 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.

[0444] In some respects, the technology described herein relates to an AI-based platform in which the recommended configuration is also based on at least one of the following: local demand conditions associated with the current and / or predicted location of at least one of a set of distributed systems, or local demand conditions associated with the current and / or predicted location of at least one energy resource associated with a set of distributed systems.

[0445] In some respects, the technology described herein relates to an AI-based platform for enabling 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, 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 dataset to identify a set of events and / or signals indicating at least one energy pattern of the set of distributed energy entities.

[0446] In some respects, the technology described in this paper relates to an AI-based platform in which a group of distributed energy entities includes at least one energy generation resource.

[0447] In some respects, the technology described in this paper relates to an AI-based platform in which a group of distributed energy entities includes at least one energy-consuming entity.

[0448] In some respects, the technology described in this paper relates to an AI-based platform in which a group of distributed energy entities includes at least one energy storage resource.

[0449] In some respects, the technology described in this paper relates to an AI-based platform in which a group of distributed energy entities includes at least one energy delivery resource.

[0450] In some respects, the technology described in this paper relates to an AI-based platform in which the training dataset comprises historical energy generation data of a set of entities similar to those controlled via edge networking devices.

[0451] In some respects, the technology described in this paper relates to an AI-based platform in which the training dataset comprises historical energy consumption data of a set of entities similar to those controlled via edge networking devices.

[0452] In some respects, the technology described in this paper relates to an AI-based platform in which the training dataset comprises historical energy delivery data from a set of entities similar to those controlled via edge-networked devices.

[0453] In some respects, the technology described in this paper relates to an AI-based platform in which the training dataset comprises historical energy storage data of a set of entities similar to those controlled via edge networking devices.

[0454] In some respects, the technology described herein relates to an AI-based platform in which at least one adaptive autonomous data processing system is further configured to adapt to 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 factors conditions; or user configuration conditions.

[0455] In some respects, the technology described herein relates to an AI-based platform, and also includes an adaptive energy digital twin, which 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 use priorities.

[0456] In some respects, the technology described herein relates to an AI-based platform, which also includes an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical metrics of energy consumption for at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0457] In some respects, the technology described herein relates to an AI-based platform, which also includes 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.

[0458] In some respects, the technology described herein relates to an AI-based platform in which 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; 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.

[0459] In some respects, the technology described herein relates to an AI-based platform in which the energy edge set is based on at least one public data resource, which includes at least one of the following: weather data resources; satellite data resources; census, population, demographic and / or psychometric data resources; market data resources; or e-commerce data resources.

[0460] In some respects, the technology described herein relates to an AI-based platform in which the energy edge set is based on at least one enterprise data resource, which 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.

[0461] In some respects, the technology described herein relates 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 on a training dataset, and the training dataset is based on at least one of the following: at least one human label and / or tag; 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.

[0462] In some respects, the technology described herein relates to an AI-based platform in which at least one adaptive autonomous data processing system is also 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 instance of wireless energy delivery; at least one fuel delivery; or at least one stored energy delivery.

[0463] In some respects, the technology described herein relates to an AI-based platform in which 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, the at least one energy-related event including at least one 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.

[0464] In some respects, the technology described herein relates to an AI-based platform in which 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 dispatch system.

[0465] In some respects, the technology described herein relates to an AI-based platform in which 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 have been initially trained and additional energy intelligence data on which the data processing systems have not yet been trained.

[0466] In some respects, the technology described herein relates to an AI-based platform in which a set of adaptive autonomous data processing systems is also configured to instruct at least one of a set of edge networking devices to adjust operating 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.

[0467] In some respects, the technology described herein relates to an AI-based platform in which a set of adaptive autonomous data processing systems is also configured to detect events and / or signals based on data collected from a set of edge networking 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 feature over a period of time.

[0468] In some respects, the technology described herein relates to an AI-based platform for enabling 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 the environment and at least one external edge device located outside the environment.

[0469] In some respects, the technology described herein relates 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 vectorized.

[0470] In some respects, the technology described herein relates 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.

[0471] In some respects, the technology described herein relates to an AI-based platform in which a data integration module is also 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.

[0472] In some respects, the technology described herein relates to an AI-based platform for enabling 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 an AI-based digital twin updater that updates the dynamic digital twin based on a set of energy parameters.

[0473] In some respects, the technology described herein relates to an AI-based platform in which an AI-based digital twin updater performs dynamic updates to the digital twin to determine energy demand forecasts for a future period and updates energy demand forecasts for a future period based on another AI model.

[0474] In some respects, the technology described herein relates to an AI-based platform in which a dynamic digital twin is associated with a device type, and an AI-based digital twin updater analyzes data associated with the energy consumption of the device type in order to update the dynamic digital twin to model the energy consumption of the device type.

[0475] In some respects, the technology described herein relates to an AI-based platform in which a 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 at least one entity.

[0476] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: an energy access arbitrator that arbitrates access by at least one energy-consuming device to at least one energy source within a group of energy-consuming devices.

[0477] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: 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.

[0478] In some respects, the technology described herein relates to an AI-based platform, which also includes an edge device monitoring system that monitors the energy consumption of at least one downstream device among at least one energy-consuming device and implements energy strategies on at least one downstream device based on the energy consumption.

[0479] In some respects, the technology described in this paper relates to an AI-based platform in which energy strategies are based on generation mechanisms that generate energy associated with energy consumption.

[0480] In some respects, the technology described herein relates to an AI-based platform in which an edge device monitoring system is also configured to determine carbon emissions associated with the energy consumption of at least one downstream device.

[0481] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity 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.

[0482] In some respects, the technology described herein relates to an AI-based platform in which at least one of a group of AGI agents is 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.

[0483] In some respects, the technology described herein relates to an AI-based platform in which at least one AGI agent in a 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 at least one other AGI agent.

[0484] In some respects, the technology described herein relates to an AI-based platform in which at least one AGI agent from a group 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 use pattern; at least one instance of space travel; at least one satellite; at least one asteroid mining operation; at least one banking system; at least one marketing operation; at least one instance of radioactive waste disposal associated with at least one nuclear power plant; at least one cyberattack associated with at least one energy source; at least one land clearing operation; at least one AI entity; or at least one robotic entity.

[0485] In some respects, the technology described herein relates to an AI-based platform in which at least one of a group of AGI agents performs adjustments to data associated with at least one of a data collection process, data storage process, data reporting process, or data transmission process, and adapts to at least one of anonymity requests or privacy requests from individuals associated with the data.

[0486] In some respects, the technology described herein relates to an AI-based platform in which at least one AGI agent in a group monitors the movement of at least one energy resource within a networked element and updates the strategy associated with the at least one energy resource based on the movement.

[0487] In some respects, the technology described herein relates to an AI-based platform in which at least one AGI agent in a group of AGI agents updates energy allocation in response to movement to facilitate energy availability of at least one energy resource.

[0488] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, wherein the AI-based platform includes: a graph neural network comprising: a set of nodes each representing at least one distributed energy resource (DER); and a set of edges interconnecting the set of nodes, wherein each edge represents at least one energy-related feature between at least two nodes of the set of nodes.

[0489] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, wherein the AI-based platform includes: a supply graph neural network, each representing a set of nodes of at least one distributed energy resource (DER), the supply graph neural network being configured to generate, store, convert and / or transmit energy; and a demand graph neural network, each representing a set of nodes of at least one distributed energy resource (DER), the demand graph neural network being configured to consume energy.

[0490] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, wherein the AI-based platform includes: at least one digital twin representing at least one distributed energy resource (DER); and a graph neural network including a set of nodes associated with the at least one digital twin and a set of edges interconnecting the set of nodes.

[0491] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, wherein the AI-based platform includes: a graph neural network comprising a set of nodes representing at least one distributed energy resource (DER) and a set of edges interconnecting the set of nodes; and an attention model indicating at least one attention relationship between at least two nodes of the set of nodes in the graph neural network.

[0492] In some respects, the technology described herein relates to an AI-based platform for enabling intelligent coordination and management of electricity and energy, wherein the AI-based platform includes a graph neural network comprising: a set of nodes representing at least one distributed energy resource (DER) and a set of edges interconnecting the set of nodes; and a large language model configured to generate at least one description of the set of nodes and the set of edges included in the graph neural network. Attached Figure Description

[0493] This disclosure will be more fully understood through detailed description and accompanying drawings.

[0494] Figure 1 This is a schematic diagram illustrating an introduction to a platform and key components according to some embodiments.

[0495] Figure 2A and Figure 2B This is a schematic diagram illustrating the main subsystems of a major ecosystem according to some embodiments.

[0496] Figure 3 This is a schematic diagram illustrating more detailed information about a distributed energy generation system according to some embodiments.

[0497] Figure 4 This is a schematic diagram illustrating more detailed information about data resources according to some embodiments.

[0498] Figure 5 This is a schematic diagram illustrating more detailed information about the configuration of energy edge stakeholders according to some embodiments.

[0499] Figure 6 This is a schematic diagram illustrating more details about an intelligent support system according to some embodiments.

[0500] Figure 7 This is a schematic diagram illustrating further details regarding AI-based energy coordination according to some embodiments.

[0501] Figure 8 This is a schematic diagram illustrating further details regarding configurable data and intelligence according to some embodiments.

[0502] Figure 9 This is a schematic diagram illustrating a dual-process learning function of a dual-process artificial neural network according to some embodiments.

[0503] Figures 10 to 37 This is a schematic diagram of an embodiment of a neural network system according to embodiments of the present disclosure. The neural network system can be connected to a platform, integrated into a platform, and accessed by the platform to enable intelligent transactions. Intelligent transactions 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, support for autonomous control, and other purposes.

[0504] Figure 38 This is a schematic diagram illustrating an exemplary embodiment of a quantum computing service according to some embodiments of the present disclosure.

[0505] Figure 39 A quantum computing service request processing method according to some embodiments of the present disclosure is illustrated.

[0506] Figure 40 This is a diagrammatic view of the thalamic services and how they are coordinated within modules, based on this disclosure.

[0507] Figure 41 This is another illustrated view of the thalamic services and their coordination within the module according to this disclosure.

[0508] Figure 42 This is a schematic diagram illustrating attention determined by a machine learning model according to this disclosure.

[0509] Figure 43 This is a schematic diagram of the transformer model based on this disclosure.

[0510] Figure 44 This is a schematic diagram of the energy edge convergence technology stack based on this disclosure.

[0511] Figure 45 This is a schematic diagram of a set of capabilities of the energy edge convergence technology stack according to this disclosure. Detailed Implementation

[0512] Figure 1 Introduction to the platform and main components In this embodiment, an AI-based energy edge platform 102 is provided. For convenience, the AI-based energy edge platform is referred to herein simply as Platform 102. It includes a set of systems, subsystems, applications, processes, methods, modules, services, layers, devices, components, machines, products, interfaces, connections, and other cooperating elements to enable 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 in some cases as “distributed energy resources” or “DERs”) 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 DERs and other energy resources and can be used to notify, analyze, control, optimize, predict, and otherwise assist in the coordination of distributed energy resources and other energy resources.

[0513] For example, distributed energy (“DER”) can include (but is not limited to): wind turbines (including wind turbine farms), solar photovoltaic (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, natural gas wells, modular nuclear reactors, nuclear batteries, modular hydropower systems, microturbines and turbine arrays, reciprocating engines, gas turbines, combined heat and power plants, biomass generators, municipal solid waste incinerators, battery energy storage (including chemical batteries, etc.), capacitor energy storage, geothermal energy systems, molten salt energy storage, electrothermal 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, natural gas storage tanks, liquefied natural gas (LNG) storage tanks, physical energy storage systems such as flywheels, gravity batteries (e.g., material suspended in a gravity well), fuel transport vehicles, fuel transport pipelines, wired power transmission systems, wireless power transmission systems, etc.

[0514] In this embodiment, platform 102 implements a set of configured stakeholder energy edge solutions 108, which have a wide range of functions, applications, capabilities, and uses, and can be implemented by using or coordinating a set of advanced energy resources and systems 104 (including DERs, etc.), but are not limited thereto. This set of configured stakeholder energy edge solutions 108 can integrate, for example, domain-specific stakeholder data (e.g., proprietary datasets 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 enhance or replace the power grid) into solutions that meet the energy needs and capabilities of stakeholders, including baseline, cyclical, and peak energy demands for operations such as large-scale data processing, transportation, cargo and material production, resource extraction and processing, heating and cooling.

[0515] In this embodiment, platform 102 (and / or its components) and / or the stakeholder energy advantage solution 108 configured with this group can acquire, provide, and / or exchange data with a set of data resources 110 used for energy edge coordination. Platform 102 obtains information from the set of data resources 110 used for energy edge coordination. These data resources may include datasets ranging from real-time energy consumption metrics to predictive analytics of future energy demand. By using these resources, platform 102 is able to make timely and informed decisions. Platform 102 is also equipped to provide data to the set of data resources 110 used for energy edge coordination. This data may 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. Furthermore, the stakeholder energy advantage solution 108, configured with this group to meet the unique needs of various stakeholders, can contribute data to platform 102 and gain insights from platform 102. For example, a stakeholder solution designed for a solar power plant can provide real-time data on solar panel efficiency, which platform 102 can then use to optimize energy distribution. This data exchange between platform 102, a set of configured stakeholder energy edge solutions 108, and a set of data resources 110 for energy edge coordination ensures that optimization is based on the latest available data.

[0516] 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 intelligence modules and services 118, which are integrated with, exchange data with, and / or otherwise linked to. The set of intelligent support systems 112 acts as the cognitive hub of platform 102. Utilizing advanced algorithms and computing tools, the set of intelligent support systems 112 endows platform 102 with the necessary intelligence to analyze large datasets, identify patterns, and make informed decisions. The set of AI-based energy coordination, optimization, and automation systems 114 ensures 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, responding to real-time changes and proactively taking strategic actions. The set of configurable data and intelligence modules and services 118 provides platform 102 with modular and customizable flexibility. Depending on specific use cases, stakeholders can configure these modules to meet their unique needs.

[0517] The intelligent support system 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 transaction and data management; a set of adaptive energy digital twin systems 134 for creating virtual copies of physical energy assets for better monitoring and optimization; and / or a set of energy simulation systems 136 for modeling potential energy scenarios to aid decision-making. These integrated systems work together within the set of intelligent support systems 112 to provide a comprehensive solution for advanced energy management.

[0518] This 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 monitoring and optimizing how energy is used, a set of energy market coordination systems 146 for facilitating energy trade and transactions, a set of energy transmission coordination systems 147 for ensuring efficient and reliable energy distribution, and a set of energy storage coordination systems 142 for managing energy storage. Together, these systems provide a holistic approach to coordinating the entire energy lifecycle.

[0519] The group of configurable data and intelligence modules and services 118 may include a set of energy trading support systems 144 that facilitate and simplify energy-related transactions, a set of stakeholder energy digital twins 148 that provide a virtual representation of stakeholder-specific energy assets for better monitoring and management, and a set of data integration microservices 150 that can support or help support stakeholder energy advantage solutions 108 configured in this group, thereby ensuring an integrated approach to energy management.

[0520] Platform 102 may include one or more artificial intelligence (AI) systems, integrate with it, link with it, exchange data with it, be managed by it, receive input from it and / or provide output to it. The artificial intelligence (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 this disclosure and in references incorporated herein by reference. Unless the context specifically indicates otherwise, references to AI or one or more examples of AI should be understood to encompass these various alternative methods and systems; for example, but not limited to, AI systems described for supporting any 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 manipulating a model or set of rules; by training on training datasets with human labels, tags, etc.; by training on training datasets with human interaction (e.g., human interaction with software interfaces or hardware systems); by training on training datasets of results; by training on training datasets generated by AI (e.g., where the complete training dataset is generated by AI from a seed training dataset); by supervised learning; by semi-supervised learning; by deep learning; and so on. For any given function or capability described herein, various types of neural networks can be used, including any types described herein or in references incorporated herein, and in embodiments, a set of hybrid neural networks may be selected such that within that set, the type of neural network is more conducive to performing each element of a multifunctional or multi-capability system or method. As one example among many, deep learning or black-box systems can use gated recurrent neural networks to achieve functions such as language translation by intelligent agents, where users do not need to understand the underlying mechanisms of AI operation as long as they perceive the results favorably. More transparent models or systems and simpler neural networks can be used for systems that automate governance, where a better understanding of how inputs are transformed into outputs to comply with regulations or policies may be required.

[0521] AI-based energy coordination, optimization, and automation systems In this embodiment, platform 102 may employ demand forecasting, including automated forecasting via artificial intelligence or by acquiring forecast information from a data stream from a third party. Furthermore, forecasting demand helps guide site selection and intelligent planning for network expansion. In this embodiment, machine learning algorithms may generate multiple forecasts—e.g., regarding weather, prices, 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.

[0522] In this embodiment, the AI-based energy coordination, optimization, and automation system 114 can optimize energy patterns, for example, by analyzing energy use in a building or other operation and seeking to reshape patterns for optimization (e.g., by modeling demand responses 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 assess how a building's energy consumption varies at different times of day or across different seasons. Using this knowledge, the automation system 114 can then reshape these patterns for optimal energy use. This can be applied in commercial office buildings, where the AI-based energy coordination, optimization, and automation system 114 can notice energy consumption peaks in the early afternoon due to the simultaneous use of lighting, heating, and cooling systems. By modeling how a building might respond to certain stimuli, for example, by optimizing heating, ventilation, and air conditioning (HVAC) systems based on real-time occupancy data, the AI-based energy coordination, optimization, and automation system 114 can suggest measures to distribute energy consumption more evenly throughout the day, thereby reducing peak demand and associated costs.

[0523] The AI-based energy coordination, optimization, and automation system 114 can be implemented by a set of intelligent support systems 112, which provide the functionality and capabilities to support a range of applications and use cases.

[0524] In an embodiment, platform 102 can be configured to integrate data from at least one internal edge device located within the environment (e.g., sensors within buildings, vehicles, machines, utilities) and at least one external edge device located outside the environment (e.g., sensors on weather monitoring stations broadcasting real-time data, vehicles, etc.). Platform 102 can collect real-time energy intelligence data and provide it to an intelligent circuit that is trained on the data and results and automatically executes actions to optimize energy management. For example, an edge device connected to the DER can be combined with an edge device from a local weather monitoring station. Local weather data (e.g., cloud cover, temperature, wind speed, rainfall, etc.) can be correlated with energy output from the DER, and a machine learning model can be trained to use variables from a second edge device to predict actions associated with the environment of the first edge device. By another example, radar characteristics output by a weather station edge device can be used to upshift or downshift energy from the DER.

[0525] In this 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, energy data captured from devices can be further optimized, where only changes affecting the consumption information model are transmitted. This vector can be developed based on the analysis of data from the aforementioned consuming devices. The vector for a complex energy consumption system can be a multi-dimensional vector representing consumption type, purpose, device, etc., to form an efficient way of transmitting complex energy usage environments. For example, consider a smart grid system where thousands of home appliances, HVAC systems, and lighting solutions continuously send energy consumption data. Instead of sending minute-by-minute details, this system analyzes this data and develops vectors based on consumption patterns. This vector, especially for a complex energy consumption system, can include various parameters, such as consumption type, consumption purpose, energy consumption of specific devices, etc.

[0526] In embodiments, energy usage patterns may include local patterns, such as those 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 weather systems to prepare for forecasting the reception of such systems; and so on. Pattern analysis may include not only raw usage but also information about the consumer (e.g., the energy-consuming devices they are operating) that may influence learning. For example, a consumer's daily work schedule may be a local pattern that the system can recognize and adapt to, which could involve turning off all household appliances during work hours and increasing energy consumption at night.

[0527] Demographics and other human activities can play a role in energy pattern analysis. In one example, demographics suggesting that consumers in a region replace older vehicles with newer ones more frequently than in other regions might indicate that local energy demand for electric vehicle charging is likely to increase more rapidly in those areas. When demographics and / or consumer behavior suggest that consumers in a region tend to replace vehicles with used ones, conventional energy maintenance may be indicated as the preferred option in those regions.

[0528] Subsystems and modules of intelligent support system Intelligent Data Layer A set of intelligent support systems 112 may include a set of intelligent data layers 130, such as a set of services (including microservices), APIs, interfaces, modules, applications, programs, etc., which can consume any of the data entities and types described in this disclosure and undertake a wide range of processing functions, such as extraction, cleaning, normalization, computation, transformation, loading, batch processing, streaming, filtering, routing, parsing, conversion, pattern recognition, content recognition, object recognition, etc. Through a set of interfaces, users of platform 102 can configure a set of intelligent data layers 130 or their outputs to meet internal platform needs and / or support further configuration, for example, for a set of configured stakeholder energy edge solutions 108. A set of intelligent data layers 130, more typically a set of intelligent support systems 112 and / or configurable data and intelligent modules and services 118 can access data from various sources throughout platform 102 and, in embodiments, can 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, such as IoT or edge devices producing energy-related event logs or streams. A set of intelligent data layers 130 can be configured for a wide range of energy-related tasks, such as forecasting / predicting parameters of energy consumption, generation, storage, or distribution (e.g., at the level of a single device, subsystem, system, machine, or fleet); optimizing energy generation, storage, distribution, or consumption (also at different levels of optimization); automated discovery, configuration, and / or execution of energy transactions (including micro and / or large transactions in spot and futures markets and peer-to-peer group or single-counter transactions); monitoring and tracking parameters and properties of energy consumption, generation, distribution, and / or storage (e.g., baseline levels, volatility, cyclical patterns, incidental events, peak levels, etc.); monitoring and tracking energy-related parameters and properties (e.g., pollution, carbon production, renewable energy credits, waste heat production, etc.); automatically generating energy-related alerts, recommendations, and other content (e.g., messaging to prompt or encourage beneficial user behavior); and so on.

[0529] In an embodiment, platform 102 can be configured to analyze monitored energy datasets and generate configuration recommendations for energy generation and consumption in a distributed system. Platform 102 can be configured to analyze flows from one or more local power consumption entities and generate recommendations. For example, a manufacturing plant may have a range of needs that are very different from those of a hospital campus. Thus, an AI-based platform can analyze each of the multiple energy consumption scenarios and the associated equipment and needs, and recommend the type of DER (Energy Controller) used to provide energy and regulate the energy demand and needs corresponding to the local power consumption entities. A hospital's ER may have a specific set of needs, such as operating room opening hours or emergency needs 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.) and renewable energy-based production facilities (solar power plants, wind power plants, geothermal generators, tidal generators, hydroelectric power facilities, etc.). Mobile energy resources may include, for example, mobile battery devices, mobile fossil fuel generators, mobile renewable energy producers, mobile transformers and power regulation systems, drone-based power transmission / storage systems, vehicle-based power transmission / storage systems, etc.

[0530] Distributed ledger and smart contract systems A set of smart 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 may operate on data stored in this set of distributed ledgers or blockchains, for example, to record energy-related transaction events, such as energy procurement and sales (in spot, forward, and peer-to-peer markets, as well as direct counterparty transactions), related service fees, etc.; transaction-related energy events, such as consumption, generation, distribution, and / or storage events; and other transaction-related events typically associated with energy, such as carbon generation or reduction events, renewable energy credit events, pollution generation or reduction events, etc. This set of smart contracts processed by smart contract system 132 may consume any data types and entities of the data types and entities described in this disclosure as a set of inputs, undertake a set of computations (optionally configured in a stream of 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 trading support systems 144 may be supported or enhanced by artificial intelligence, including autonomously discovering, configuring and executing trades based on strategies and / or providing automation or semi-automation of trades based on the training and / or supervision of a set of trading experts.

[0531] In this embodiment, the smart contract system 132 may be configured with a set of energy trading support systems 144 (described elsewhere in this disclosure) to provide trading solutions. Each smart contract within the smart contract system 132 is sophisticatedly designed to process data stored within these distributed ledgers or blockchains. The functionality of the smart contracts extends to recording a variety of energy-related transaction events. This includes, but is not limited to, recording peer-to-peer energy transactions, or even direct transactions between parties. Furthermore, data related to energy events associated with service fees and other transactions is captured, including information about energy consumption, generation, distribution, and storage. For example, in a city's energy grid with integrated renewable energy sources such as solar and wind power, the smart contract system 132 can autonomously execute contracts to purchase solar energy during peak sunshine hours and wind energy during windy periods. Simultaneously, each transaction, associated service fees, and even carbon offsetting achieved through the use of renewable resources are recorded.

[0532] Adaptive Energy Digital Twin System In embodiments, any entities, analytical results, AI outputs, states, operational conditions, or other characteristics mentioned in this disclosure may be represented 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 configured to meet the needs of specific stakeholders or stakeholder solutions. A set of adaptive energy digital twin systems 134 may, for example, provide visual or analytical metrics of energy consumption for a set of machines, a group of plants, a fleet of vehicles, 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 may be adaptive, for example, filtering, highlighting, or otherwise adjusting the presented data based on real-time conditions (e.g., changes in energy costs, changes in operational behavior, etc.).

[0533] In embodiments, platform 102 can be configured to create, manage, and / or otherwise provide dynamic digital twins of historical, current, and projected distributed energy demand based on mobile and stationary entities within a domain. For example, relatively large corporate or organizational environments can 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 numerous machines, assembly lines, and automated systems, platform 102 can create a digital twin of that environment, capturing every detail of its energy consumption patterns. Such a digital twin can provide real-time information about the plant's energy demand, from historical energy usage data for each machine to current consumption rates, and even projected future energy demand based on forecasted production schedules. Larger environments can be modeled where costs can be significantly transferred based on energy adjustments across the entire environment. For example, in large, energy-intensive environments, even small adjustments can have a significant financial impact. By having dynamic digital twins, stakeholders can simulate various energy adjustments and analyze their effects. 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 the availability of natural daylight can significantly transfer energy costs.

[0534] In embodiments, platform 102 can be configured to model government entities, such as states, counties, cities, towns, development zones, communities, etc., via one or more digital twins. In one example, for a city with thousands or hundreds of thousands of residents, businesses, public transportation systems, and numerous facilities, platform 102 can create a digital twin of such a city, capturing every aspect of its energy consumption. This digital representation might include everything from lighting in public parks and HVAC systems in government buildings to the energy demands of public transportation systems. By doing so, platform 102 provides city managers with a holistic view of the city's energy footprint, aiding in informed decisions regarding energy management. Platform 102 can even model larger entities, such as states or counties, capturing the varying energy demands across different areas, from city centers to rural regions. On the other hand, platform 102 can also represent smaller entities, such as towns. For example, in a new town being developed for industrial use, platform 102 can model anticipated energy demands based on planned industries, ensuring that energy infrastructure is adequately prepared to meet those demands. In another example, a county planning a transition to renewable energy could use its digital twin to simulate the impact of integrating solar power plants or wind turbines. This simulation can provide insights into potential energy savings, grid stability, and even the environmental benefits of this transition.

[0535] In an embodiment, platform 102 may include an AI-based system for updating a digital twin based on a set of energy parameters. This may include adapting energy consumption data from the physical device for the digital twin to these parameters, for example, by adjusting the costs resulting from energy consumption based on a dynamic energy market where the device provides energy. For instance, consider a device that obtains its energy from a dynamic energy market where energy costs fluctuate based on demand, supply, and other market factors. If the device consumes energy at a time of higher costs, the AI-based system can adjust the digital twin to reflect this, ensuring the virtual representation accurately reflects the financial impact of real-world energy consumption. When updating the device, the AI-based system may also incorporate the energy source preferences of the device's users (optionally, as expressed in the device's digital twin). For example, if a user expresses a preference for green energy through their device's digital twin, the AI ​​system ensures that this preference is incorporated into the energy consumption data update. For shared devices (e.g., e-bikes), energy consumed during user sharing of the device and / or associated with user sharing of the device (when the e-bike is billed in the user's account) may be allocated to / across specific energy sources based on the user profile. For example, when a user checks out an e-bike 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 digital twin can identify the allocation of consumed energy to each of multiple energy sources. For instance, there might be a situation where the device owner has a specific allocation for energy consumed across multiple energy sources. In this case, the AI ​​system ensures that the digital twin accurately reflects this allocation. For example, the owner might specify that 50% of the energy consumed by the device should come from wind power and the remaining 50% from hydropower. When updating the digital twin, the AI ​​system can ensure that this allocation is accurately represented. Therefore, platform 102 with an AI-based system provides a digital twin that is not merely a static representation but dynamic, responsive, and adaptable to individual preferences and real-world scenarios.

[0536] In embodiments, an AI-based system for updating a digital twin based on a set of energy parameters may include adapting energy production and / or distribution controls based on these parameters for an upcoming time period (e.g., during an upcoming high-demand event). This may include adjusting the operation of an energy supply system by relying on AI-based energy demand forecasts for a future period, such as determining energy parameters related to how much energy is stored versus how much is generated and delivered. For example, in the presence of anticipated high-demand events, perhaps due to holidays, an AI-based system can predict such demand surges by analyzing energy parameters and adapt energy production and / or distribution controls accordingly. In another example, based on past data and current trends, an AI-based system can predict increased energy demand during summer months. Beyond AI-based energy demand forecasting, AI-based systems can assess macroeconomic trends / activities based on energy parameters. In one example, an AI-based system updating an energy consumption system can detect pricing patterns that indicate a potential sharp increase in energy costs (e.g., due to major weather events), and this set of energy parameters can guide the AI-based system to adapt energy consumption and / or storage guidelines for at least selected consumers (e.g., public systems (e.g., tax-based systems) to avoid unnecessary burdens on taxpayers). For example, if an AI-based system detects a pattern indicating that energy costs may increase due to an upcoming major weather event, proactive measures can be taken. By analyzing this 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. Therefore, platform 102 with an AI-based system ensures that energy management is proactive and effective.

[0537] In an embodiment, platform 102 can be configured to provide and / or facilitate digital twins of common device types (e.g., e-bikes of the same model). The digital twins can exchange consumption data across a range of use cases to understand how this common device type consumes energy in different environments, at different times of day, in different geographic locations, and with varying user demographics (including local demographics at the point of use). For example, an e-bike used primarily in hilly areas may exhibit different energy consumption patterns compared to an e-bike used in a flat urban environment. By aggregating data from various digital twins, platform 102 can identify these patterns and make informed predictions. This allows the digital twin of a specific device (a particular e-bike) to better predict energy demand, resulting in dynamic charging profiles, etc. Some devices may be located in high-demand areas, indicating a need for more frequent charging, while others may allow for lower average energy costs due to, for example, shorter and less frequent use. For instance, an e-bike located in a busy city center might be identified as requiring frequent charging due to high demand; on the other hand, another e-bike located in a less frequently used area might function well even without frequent charging. This also allows for the aggregation of demand profiles across a range of geographic areas to identify needs, such as charging requirements and available energy. For example, in areas with a high concentration of e-bikes (e.g.), platform 102 can suggest staggered charging schedules to balance demand and prevent grid overload. This can lead to the management of e-bike charging activities, including demand balancing for other charging facilities within a region.

[0538] In an embodiment, platform 102 can be configured such that not every physical instance of a device (e.g., a specific model of e-bike) needs to have its own permanent digital twin. Most such devices have a much longer dormant period than their usage time (very low duty cycles), so even the energy requirements supporting the digital twin processing of such devices can be managed according to demand curves. Instances of physical devices (or configured genetic instances) can be activated based on demand forecasts (and energy resources can be allocated). Consider the scenario of a specific model of e-bike. While these e-bikes may be scattered in different locations and readily available, their actual use or "duty cycle" may not be frequent, and these devices may remain dormant for extended periods. Understanding this unique characteristic, platform 102 is configured such that it can activate digital twins for these devices based on predicted demand, rather than maintaining a continuous digital twin for each e-bike. For example, in an urban environment, if platform 102 predicts a surge in demand for e-bikes during, for example, the morning rush hour, it can activate the digital twins of the e-bikes during that period. These digital twins can facilitate energy management, ensuring that e-bikes are fully charged and readily available. After peak hours, these digital twins can be deactivated to conserve processing energy. This demand-driven approach ensures that energy resources used to process digital twins are used optimally.

[0539] In embodiments, platform 102 can provide and / or facilitate the sharing, exchange, and / or aggregation of energy consumption data provided to a digital twin by physical device instances. This energy consumption data can be collected to establish a set of energy demand parameters for predicting energy demand models, etc. For example, platform 102 is designed to facilitate the exchange and aggregation of energy consumption data from various physical device instances and direct it to their respective digital twins. Consider, for instance, a community with multiple smart homes, each equipped with multiple smart devices. While each household may have its unique energy consumption patterns, the collective data from all these households can reveal broader trends. By aggregating this data, platform 102 can identify patterns, such as increased energy consumption or decreased demand during holidays. These insights can then inform predictive models, ensuring energy suppliers are adequately prepared to meet anticipated demand.

[0540] In an embodiment, platform 102 can be configured such that energy consumption data provided to the digital twin can also help predict energy-related demand, such as 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 based on the supply sources available to the digital twin. In other words, physical equipment not only consumes energy but must also be supplied with energy (or must generate energy itself). The digital twin can use energy supply and / or source to indicate the time / region / specific source supporting energy production (waste removal, refurbishment, etc.). For example, if a local energy production facility relies primarily on non-renewable resources, it will generate higher levels of associated waste. By predicting this, the digital twin can ensure that appropriate waste management measures are in place. Furthermore, the digital twin of a local energy production facility can utilize 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 electric bicycles for upcoming events (graduation, birthdays, etc.) (again, electric bicycles as an example) can be predicted along 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 solar energy forecasts are favorable, the power station can rely primarily on solar energy; otherwise, the power station can obtain energy from upstream energy suppliers to meet its needs.

[0541] Energy Simulation System In this 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. This includes simulation environments that simulate the results using various algorithms that can manage the generation of various power generation assets, the consumption of energy-requiring equipment and systems, and energy storage. Data can be used to simulate uncontrollable loads and optimize charging process interactions, as well as other use cases. The simulation environment can provide outputs to, integrate with, or share data with this set of adaptive energy digital twin systems 134. For example, if a city plans to transition to renewable energy, it can use the set of energy simulation systems 136 to simulate various outcomes. Such simulations can predict how solar panels will respond to changing weather conditions, how wind turbines will operate in different seasons, or how energy storage solutions will need to be managed during peak demand periods.

[0542] In this embodiment, as more enterprises adopt hybrid infrastructure, uptime becomes more complex, requiring 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 this embodiment, distributed energy or DER 128 can be integrated into or integrated 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, this set of energy simulation systems 136 ensures seamless and sustainable operation for enterprises, regardless of their infrastructure model.

[0543] Introduction to the main subsystems and modules of an AI-based energy coordination, optimization, and automation system A set of AI-based energy coordination, optimization, and automation systems 114 may include a set of energy generation coordination systems 138, a set of energy consumption coordination systems 140, a set of energy storage coordination systems 142, a set of energy market coordination systems 146, and a set of energy transmission coordination systems 147, etc. For example, the set of energy transmission coordination systems 147 can support the coordination of energy delivery to consumption points, such as via fixed transmission lines, wireless energy transmission, fuel delivery, and the delivery of stored energy (e.g., chemical or nuclear batteries), and may involve autonomously optimizing the mix of energy types from the aforementioned available resources based on various factors, such as consumption location (e.g., based on distance from the grid), purpose, or type (e.g., whether very high peak energy delivery is required, e.g., for power-intensive production processes). Consider a remote industrial unit far from the main grid whose production processes require electricity. The set of energy generation coordination systems 138 can analyze the location and determine that connecting such a unit to the main grid may not be feasible. Instead, the set of energy generation coordination systems 138 can suggest that a combination of wireless energy transmission and chemical battery delivery may be most suitable in this case.

[0544] Configurable data and intelligence modules and services In an embodiment, platform 102 may include a set of configurable data and intelligence 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 microservice architecture) may exchange data with various data resources to provide relevant outputs, such as supporting a set of internal functions or capabilities of platform 102 and / or supporting a set of functions or capabilities of one or more of a set of configured stakeholder energy edge solutions 108. As one example among many, a service may be configured to acquire event data from an IoT device with cameras or sensors monitoring a generator and integrate it with weather data from a public data resource 162 to provide a weather-related timeline of energy generation data from the generator, which may in turn be consumed by a set of configured stakeholder energy edge solutions 108, for example, to assist the generator's daytime energy generation based on daytime weather forecasts. Platform 102 can support a wide range of such data and intelligence modules and services 118, representing, for example, various outputs composed of the fusion or combination of a wide range of energy edge data sources processed by the platform, higher-level analytical outputs generated by expert analysis of data, forecasting and prediction based on data patterns, automation and control outputs, etc.

[0545] In embodiments, platform 102 can be configured to enable energy-consuming devices and / or systems (e.g., a group of energy-consuming devices in a home) to locally arbitrate access to energy sources, such as trunk energy, primary energy storage (e.g., at the device), local energy storage (e.g., a local battery that can supply energy to multiple devices), etc. Furthermore, devices can consume energy for various purposes, such as consumption, storage, balancing sources, acting as proxies for other devices, etc. Additionally, energy-consuming devices can be configured / can be configured to use multiple energy types, such as the grid, solar, geothermal, fossil fuels (internal combustion engines), hydrogen, etc. Furthermore, in an energy-consuming system (a group of devices as described above), energy consumption can span a range of energy sources (e.g., hydrogen for cooking, solar for energy storage, waste recycling, etc.). For example, consider a home equipped with multiple energy-consuming devices, each with its unique energy needs and preferences. Platform 102 can facilitate a dynamic environment where these devices can locally arbitrate access to various energy sources based on their immediate needs and available resources. For example, on a sunny day, the solar panels in a house may generate excess energy. In this case, platform 102 can primarily utilize the energy from the solar panels, reducing energy consumption from the grid.

[0546] In this embodiment, platform 102 can capture energy consumption information from / via edge devices and develop datasets representing multiple perspectives on energy consumption. Edge devices, which can communicate with a range of energy-consuming devices and device types (e.g., locally or very close), can collect data about the devices, including, for example, which sources the device can consume, what kind of source the device consumes, the purpose / use of the consumed energy, etc. Further examples may include what appears to be that the device performs any kind of optimization, such as utilizing local storage during periods of high energy costs (including high transmission costs that may be measured based on transmission efficiency, etc.), consuming energy during off-peak periods to supplement storage, and / or utilizing low-cost sources (e.g., solar energy) when readily available. Various analytics, etc., can be generated, captured, and used within 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 utilizing local storage during periods of high energy costs. By aggregating this data from various edge devices, platform 102 can identify patterns, predict future energy needs, and optimize energy consumption across devices.

[0547] Energy Trading Support System Configurable data and intelligence modules and services 118 may include a set of energy trading support systems 144. This set of energy trading support systems 144 may 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 procurement and sales (in spot, forward, and peer-to-peer markets, as well as direct counterparty transactions), related service fees; transaction-related energy events, such as consumption, generation, distribution, and / or storage events, and other typically energy-related transaction-related events, such as carbon generation or reduction events, renewable energy credit events, pollution generation or reduction events, etc. This set of smart contracts can consume any data type and entity of the data types and entities described in this disclosure as a set of inputs, undertake a set of computations (optionally configured in a stream of 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 energy trading support system 144 can be supported or enhanced by artificial intelligence, including autonomously discovering, configuring, and executing trades based on strategies and / or providing automation or semi-automation of trades based on the training and / or supervision of a group of trading experts. Autonomous and / or automated (supervised or semi-supervised) processes can be achieved through robotic process automation, for example, by training a group of intelligent agents to enable a group of trading experts to interact with the trading support system (e.g., a software system for configuring and executing energy trading activities) for trade discovery, configuration, or execution.

[0548] As energy is increasingly produced and consumed in local, decentralized markets, the energy market is likely to follow the models of other peer-to-peer or sharing economy markets, such as carpooling, shared apartments, and secondhand 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 output.

[0549] As more distributed or peer-to-peer energy trading markets develop, platform 102 may include systems, or link to, integrate with, or support other platforms, to facilitate P2P trading, wholesale contracts, renewable energy certificate (REC) tracking, and a wider range of distributed energy supply, payment management, and other trading components. In embodiments, the foregoing may utilize blockchain, distributed ledger, and / or smart contract systems 132. For example, a homeowner with excess solar energy might decide to sell this excess energy. This transaction is securely recorded on the blockchain.

[0550] In this 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 might decide to utilize its collective solar power generation. Platform 102 enables households with solar panels to exchange excess energy with those without, ensuring benefits for the entire community.

[0551] In this embodiment, trading elements can be configured by the energy trading support system 144 to optimize energy generation, storage, or consumption, such as utility company usage time fees. Utilizing IoT-based platforms, energy demand can be shifted from periods of high prices; these platforms can identify the periods with the cheapest energy costs. For example, in areas where utility fees vary based on usage time, 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 operation accordingly, thereby ensuring efficient and cost-effective energy consumption.

[0552] Stakeholder Energy Digital Twin Configurable data and intelligence modules and services 118 may include a set of stakeholder energy digital twins 148, which in embodiments may include a set of digital twins configured to represent a set of energy-related stakeholder entities, including energy generation resources, energy distribution resources, and / or energy distribution resources owned and operated by stakeholders (including those represented 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 future market pricing of energy, stakeholder supply chains, stakeholder products and services, etc.). This set of stakeholder energy digital twins 148 can provide real-time information about status, operational conditions, etc., such as sensor data, event logs, and other information streams provided from IoT and edge devices, particularly related to energy consumption, generation, storage, and / or distribution.

[0553] This stakeholder energy digital twin 148 provides a visualized, real-time view of the impact of energy on all aspects of a business. Digital twins can be role-based, offering visual and analytical metrics tailored to specific user roles, such as financial reporting information for a Chief Financial Officer (CFO); operational parameters for power plant managers; and energy market information for energy traders. For example, a CFO might need a visualization highlighting the financial costs of energy consumption, such as how shifting operations to off-peak hours affects energy costs. In contrast, a power plant manager might be more interested in operational parameters, such as the efficiency of generating resources. On the other hand, energy traders might want to understand the energy market, such as tracking prices. Therefore, by providing insights tailored to individual roles, the stakeholder energy digital twin 148 ensures that different stakeholders have the relevant information they need to make informed decisions.

[0554] Data integration microservices Configurable data and intelligence modules and services 118 may include a set of data integration microservices 150, for example, organized in a service-oriented architecture, allowing various microservices to be chained, parallelized, or grouped in more complex flows to create higher-level, more complex services, each providing a defined set of outputs by processing a defined set of outputs to support a set of configured stakeholder energy edge solutions 108 or to facilitate AI-based coordination, optimization, and / or automation systems 114. Configurable data and intelligence modules and services 118 may be (but are not limited to) configured by 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 data resources 110 and / or platform 102 used for various energy edge coordination.

[0555] Figure 2A and Figure 2B Introduction to the main subsystems of the main ecosystem components Data resources coordinated at the energy edge refer to Figure 2A Data resources 110 for energy edge coordination may include a set of edge and IoT network systems 160, public data resources 162, and / or a set of enterprise data resources 168. In embodiments, these may be supported by or utilize an adaptive energy data pipeline 164, which 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 may include adjusting one or more of these aspects of data processing based on data content (e.g., through packet inspection or other mechanisms for understanding packets), network conditions (e.g., congestion, latency / delay, packet loss, error rate, transport costs, quality of service (QoS), etc.), usage context (e.g., based on users, systems, use cases, applications, etc., including based on their priority), market factors (e.g., price or cost factors), user configuration or other factors, and various combinations thereof. For example, among many other routes, the lowest-cost route can be automatically selected for data related to the management of low-priority energy use, such as heating a swimming pool, while the fastest or highest QoS route can be selected for data supporting priority use or energy, such as supporting critical healthcare infrastructure.

[0556] refer to Figure 2BPlatform 102 and coordination can include, integrate, link to, utilize, 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 embodiments, elements of Advanced Energy Resources and Systems 104, a set of configured stakeholder energy edge solutions 108, and / or energy edge coordination 110 can be coupled with… Figure 1 The corresponding elements shown may be the same, similar, or different. Data resources may include individual databases, distributed databases, and / or federated data resources, etc.

[0557] Edge and IoT network systems It can collect and process a wide range of energy-related data (including through artificial intelligence services and other capabilities), and control commands can be processed by a set of edge and IoT network systems 160, such as network systems integrated into devices, components, or systems; network systems located in IoT devices and systems; network systems located in edge devices and systems; for example, the aforementioned network systems are located in or around energy-related entities, such as network systems used by consumers or businesses, such as network systems involved in energy generation, storage, transmission, or use. These include any of the broad range of software, data, and network systems described herein.

[0558] Public Data Resources In this embodiment, platform 102 may track public data resources 162, such as weather data. Weather conditions can affect energy use, especially when they 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. Public data resources 162 may include satellite data, demographic and psychometric data, population data, census data, market data, website data, e-commerce data, and many other types of data.

[0559] Enterprise Data Resources A set of enterprise data resources 168 can include a wide range of 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, and operational data.

[0560] Subsystems and modules of advanced energy resources and systems In this embodiment, the advanced energy resource and system 104 may include a distributed energy resource, or “DER” 128. A more decentralized energy resource means that more individuals, network groups, and energy communities will be able to generate and share their own energy and coordinate systems to achieve ultimate efficiency. DER 128 may be small- or medium-sized generation and / or storage units that operate locally and can be connected to a larger power grid at the distribution level. For example, DER 128 may be connected to a local power grid or isolated from the grid in a standalone application.

[0561] Energy infrastructure transformation The advanced energy resources and systems 104 coordinated by platform 102 may include a set of transformed energy infrastructure systems 120. The energy edge will involve the increasing digitization of generation, transmission, substation, and distribution assets, which in turn will impact the operation, maintenance, and expansion of traditional power grid infrastructure. In embodiments, the set of transformed energy infrastructure systems 120 may be integrated with or linked to platform 102. The transition to improved infrastructure may include moving from SCADA systems and other existing control, automation, and monitoring systems to an IoT platform with advanced capabilities.

[0562] In this embodiment, new assets added to or coordinated with the grid (e.g., DER 128) can be compatible with existing infrastructure to maintain voltage, frequency, and phase synchronization. For example, consider a city integrating renewable energy sources such as wind turbines and solar panels (DER 128) into its existing grid. These new assets need to be integrated with the older infrastructure to ensure consistent power delivery. This compatibility ensures that residents do not experience fluctuations in voltage, frequency, or phase synchronization even as the city transitions to greener energy, thus ensuring a stable power supply.

[0563] In this embodiment, any improvements to traditional grid assets, new grid-connected equipment, and support systems may comply with regulatory standards from NERC, FERC, NIST, and other relevant agencies; positively impact grid reliability; reduce grid vulnerability to cyberattacks and other security threats; enhance the grid's ability to accommodate extensive bidirectional energy flows (i.e., DER proliferation); and provide interoperability with technologies that improve grid efficiency (i.e., by providing and facilitating demand response, reducing grid congestion, etc.).

[0564] The digitization of traditional power grid assets can involve assets used for power generation, transmission, storage, and distribution, including power plants, substations, and transmission lines.

[0565] In embodiments, to maintain and improve existing energy infrastructure, platform 102 may include various capabilities, including fully integrated predictive maintenance across utility-owned assets (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 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 can provide predictive maintenance, alerting the utility before potential problems become severe.

[0566] In this embodiment, grid maintenance can be provided. Through proactive maintenance, utilities can accurately detect defects and reduce unplanned outages, thereby 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 to repair it promptly. This proactive approach not only reduces unplanned outages but also lowers maintenance costs, resulting in a more efficient and cost-effective grid.

[0567] Digital resources In this embodiment, platform 102 can 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. Furthermore, the ongoing digital transformation includes the proliferation of intelligent devices and systems with data processing and communication capabilities, the increasing ubiquitous sensors in edge, IoT, and other devices, and the generation of massive, dense data streams. All of this provides opportunities for enhanced intelligence, automation, optimization, and flexibility as information constantly flows between the physical and digital worlds. Such devices and systems require significant amounts of energy. For example, data centers consume vast amounts of energy, and edge and IoT devices can be deployed in off-grid environments requiring alternative forms of energy generation, storage, or mobility. In this embodiment, a set of digital resources can be integrated, accessed, or used to optimize energy consumption for computing, storage, and other resources in data centers and at the edge. In this embodiment, as more and more devices embed sensors and controllers, information can flow continuously between the physical and digital worlds as machines “talk” to each other. Products can be tracked from source to customer or during use, enabling rapid responses to internal and external changes. Personnel responsible for managing or regulating such systems can obtain detailed data from these devices to optimize operations throughout the process. This trend transforms big data into intelligent data, which can significantly improve cost and process efficiency.

[0568] In this embodiment, advancements in digital technology enable previously impossible levels of monitoring and operational performance. Leveraging sensors and other smart assets, service providers can collect extensive data across multiple parameters, enabling real-time monitoring 24 / 7.

[0569] In embodiments, the DER 128 will be integrated into computing networks and infrastructure devices and systems to enhance existing power grids and reduce costs and improve reliability. For example, platform 102 can significantly enhance existing power grids by integrating the DER 128 (e.g., localized solar power plants or wind turbines) into urban infrastructure. For instance, during peak demand periods, platform 102 can enable a city's energy management system to utilize local energy sources instead of relying solely on traditional power plants, which can reduce pressure on the main grid and save significant costs.

[0570] Mobile energy resources In this embodiment, the DER can be integrated into mobile energy resources 124, such as electric vehicles (EVs) and their charging networks / infrastructure, thereby enhancing the existing power grid and contributing to cost reduction and improved reliability. Given the rise of EVs (all types), charging infrastructure and vehicle charging planning need to be optimized to match supply and demand. Furthermore, the increasing electricity demand and the development of EV infrastructure will necessitate optimization using other relevant technologies such as edge computing and IoT. EV charging can be integrated into decentralized infrastructure and can even be used as a DER 128 by adding to the grid (e.g., via bidirectional charging stations) or by supplying power to another system locally. Vehicle power electronics and batteries can benefit the grid by providing system and grid services. Excess energy can be stored in the vehicle as needed and discharged when required. This flexible option not only avoids costly load spikes during periods of high energy demand but also increases the share of renewable energy usage.

[0571] In this embodiment, to universally integrate electric vehicles and charging infrastructure into the power distribution network, coordination with various other normalized communication protocols is required. Platform 102 may 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. Here, platform 102 can act as a central hub, integrating various protocols to ensure smooth, efficient, and coordinated communication between the vehicle, the charging station, and the power grid when the EV is parked at a charging station.

[0572] Configuration of stakeholder energy edge solutions A set of configured stakeholder energy edge solutions 108 may include a set of mobility 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 utilize various advanced energy resources and systems 104 and / or various configurable data and intelligence modules and services 118 to achieve benefits for specific stakeholders (e.g., private enterprises, NGOs, independent service organizations, government organizations, etc.). All such solutions can leverage edge intelligence, for example, using data collected from onboard or integrated sensors, IoT systems, and edge devices located near entities that generate, store, transmit, and / or use energy to feed models, expert systems, analytics systems, data services, intelligent agents, robotic process automation systems, and other artificial intelligence systems to facilitate solutions tailored to the specific needs of stakeholders. For example, in the case of an urban area, the set of mobility demand solutions 152 could be used to predict peak travel times and adjust public transportation schedules accordingly. Similarly, in the case of a large corporate campus, the set of enterprise optimization solutions 154 could be used to manage its energy consumption, ensuring that office buildings are adequately powered during working hours while conserving energy during off-peak hours.

[0573] Enterprise optimization solutions In this embodiment, DER 128 will be integrated with or into enterprise and shared resources to enhance the existing power grid and reduce costs and improve reliability. Increased digitalization will facilitate integration activities and promote new approaches to optimizing energy in buildings / operations and in campuses and enterprises. For example, by integrating DER 128, a campus can supplement its electricity demand with renewable energy. Digitalization of energy management can help campuses monitor and adjust their energy consumption in real time. In this embodiment, this can enable the efficient management of buildings by leveraging big data and plug load analytics, thereby increasing the bottom line for for-profit businesses. For example, campuses can efficiently manage their buildings to ensure energy is used where it is needed, thereby optimizing operating costs.

[0574] In embodiments, IoT sensors and building automation control systems can be configured to help optimize floor space, identify unused equipment, automate energy consumption, improve security, and reduce the building's environmental impact. For example, in a multi-story office building equipped with IoT sensors and building automation control systems, these systems can monitor energy consumption on each floor, ensuring that lighting and HVAC systems are optimized for the number of occupants. In one example, unused meeting rooms can automatically turn off lights and adjust temperature, reducing energy waste.

[0575] In this embodiment, platform 102 can manage the total energy consumption of systems and devices connected to the power grid or a set of DER 128 systems. Some systems are almost always operational, while other devices and machines may only be connected occasionally. By maintaining an understanding of the building's total daily power consumption and the role of individual devices in the total energy use of a particular system, platform 102 can optionally predict, provide, manage, and control total consumption using AI or algorithms. For example, platform 102 can use AI and algorithms to monitor and adjust energy consumption based on the specific needs of each building, thereby optimizing energy use.

[0576] In this embodiment, platform 102 can track and utilize understanding of occupant behavior. Occupant activity levels, behavioral patterns, and comfort preferences can be considered as factors in 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 the IoT-based analytics platform can consider when generating recommended solutions. For example, in winter, if the platform notices that residents tend to stay home at night, it can adjust heating accordingly. Over time, the system learns from these patterns to ensure energy is used efficiently.

[0577] In embodiments, platform 102 can support or integrate with systems or platforms for autonomous operation. For example, industrial sites, such as oil drilling platforms and power plants, require extensive monitoring of efficiency and safety because leaks of liquids, vapors, or oil can be catastrophic, costly, and wasteful. Artificial intelligence and machine learning can provide autonomy to power plants, such as those 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 analytics (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, platform 102 can monitor the health of machines in real time, predict potential weaknesses, and recommend timely maintenance and repairs through AI and machine learning.

[0578] In this 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 deliver oil to refineries and ultimately to gas stations. Edge AI can calculate the optimal flow rate of oil to ensure production reliability and protect the long-term health of pipelines. In this embodiment, companies can inspect pipelines for defects that could lead to dangerous failures and automatically alert pipeline operators.

[0579] Energy supply and governance solutions Energy supply and governance solutions 156 can include solutions for governing mining operations. Cobalt, nickel, and other metals are essential components of the batteries needed for the green EV revolution. The funding required to support the growing market will put economic pressure on mining operations, many of which take place in regions like the DRC where corruption, child labor, and violence have a long history. The company is exploring for cobalt in regions like Greenland, partly because the region offers reliable labor law enforcement, tax compliance, and so on. This commitment can be made more reliably there and in other jurisdictions through a suite of mining governance solutions 542. This suite of mining governance solutions 542 may include mine-grade IoT sensing of the mining environment, ground penetration sensing of unmined portions, mass spectrometry and computer vision-based sensing of mined materials, asset tagging of smart containers (e.g., detecting and recording open and close events to ensure that the material placed in the container is the same material delivered at the destination), wearable devices for detecting miners' physiological states, secure (e.g., blockchain and DLT-based) recording and resolution of transactions and transaction-related events, smart contracts for automatically distributing revenue (e.g., to tax authorities, workers), and automated systems for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements. From basic sensors to compliance reports, all of the above can be selectively represented in a digital twin representing each mine owner or an enterprise-operated entity.

[0580] The energy supply and governance solution 156 may also include a suite of carbon-sensing energy solutions, wherein data on the current state of carbon generation or emissions is collected via edge and IoT devices, and operational entities are managed by automatically generating a set of recommendations and / or control instructions to meet strategies, such as managing control over operational entities that generate (or capture) carbon by keeping operations within limits offset by available carbon offset credits.

[0581] More details about various energy supply and governance solutions are provided below.156

[0582] Localized production solutions In this embodiment, a set of localized production solutions 158 can be integrated with, linked to, or managed by platform 102 to meet localized production needs, particularly for goods with very high transportation costs (e.g., food) or services where energy distribution costs have a significant adverse impact on product or service profitability (e.g., requiring intensive computing in locations where the power grid is absent, lacks capacity, is unreliable, or is too expensive). Platform 102 can manage the energy consumption of this set of localized production solutions 158, optimizing usage based on available resources, especially in locations where traditional power grids may be absent or unreliable.

[0583] In this embodiment, the power management system can converge with other systems (e.g., building management system, operations management system, production system, service system, data center, etc.) to allow for enterprise-wide energy management. Platform 102 ensures optimal energy use across the enterprise by integrating power management with building management systems, operations management systems, production systems, service systems, data centers, etc. For example, during off-hours, when the building management system reduces lighting, the data center can offload its heavy computing workload, balancing the overall energy load.

[0584] Figure 3 More detailed information about distributed energy generation systems refer to Figure 3 The distributed energy generation system 302 may include wind turbines, solar photovoltaic (PV) systems, flexible and / or floating solar systems, fuel cells, modular nuclear reactors, nuclear batteries, modular hydroelectric systems, microturbines and turbine arrays, reciprocating engines, gas turbines, and thermal power plants, etc. The distributed energy storage system 304 may include battery energy storage (including chemical batteries, etc.), molten salt energy storage, electrothermal 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 may be managed by platform 102. In embodiments, the distributed energy storage system 304 may be portable, allowing energy units to be delivered to points of use, including points of use not connected to the conventional power grid or where the conventional power grid does not fully meet demand (e.g., points of use requiring larger peak power, more reliable continuous power, or other capabilities). Management may include integrating, coordinating, and maximizing the return on investment (ROI) of distributed energy (DER) while providing reliability and flexibility for energy demand.

[0585] In embodiments, DER 128 may use various distributed energy delivery methods and systems 308 with a variety of energy delivery capabilities, including transmission lines (e.g., conventional power grids and building infrastructure), wireless energy delivery (including resonant transmission via coupling, high-Q resonators, near-field energy delivery, and other methods), fluid, battery, fuel cell, small nuclear system, and the like.

[0586] Mobile energy resources 124 include various resources for generating, storing, or transmitting energy at various scales; therefore, mobile energy resources 124 may include subcategories of DER 128 with mobility attributes, such as those integrated into vehicles 310 (e.g., electric vehicles, hybrid electric vehicles, hydrogen fuel cell vehicles, etc., and in embodiments including a group of autonomous vehicles, the autonomous vehicles may be unmanned vehicles (UAVs), drones, etc.); those integrated into or used by mobile electronic devices 312 or other mobile systems; and those portable resources 314 (including those removable and replaceable from vehicles or other systems). As mobile energy resources 124 and supporting infrastructure (e.g., charging stations) scale in terms of capacity and availability, coordination of mobile energy resources 124 and other DER 128 (optionally with available grid resources) becomes increasingly important.

[0587] Resources involved in the generation, storage, and transmission of energy are increasingly being digitized. These digitized resources 122 may include intelligent resources 318 (e.g., smart devices (e.g., thermostats), smart home devices (e.g., speakers), smart buildings, smart wearables, and many other devices utilizing processors, network connectivity, smart agents, and other onboard intelligent features), wherein the intelligent features of intelligent resources 318 can be used for energy coordination, optimization, autonomy, control, and / or for providing data for artificial intelligence and analytics related to the foregoing. Digital resources 122 may also include IoT digitized resources and edge digitized resources 320, wherein sensors or other data collectors (e.g., data collectors monitoring event logs, network packets, network service patterns, networked device location patterns, or other available data) provide additional energy-related intelligence, such as intelligence related to the energy generation, storage, transmission, or consumption of traditional infrastructure systems and equipment, ranging from large-scale generators and transformers to consumer or commercial equipment, appliances, and other systems near a set of IoT or edge devices that can monitor these. Therefore, IoT and edge devices can provide digital information about the energy status and flow of such devices and systems, regardless of whether these devices and systems have onboard intelligence features; for example, IoT devices can also deploy current sensors on the power lines to appliances to detect utilization patterns, or edge-networked devices can detect whether another device or system connected to that device is in use (and in what state) by monitoring network traffic from that device. Digital resources 122 can also include cloud-aggregated resources 322 regarding energy generation, storage, transmission, or use, such as aggregated data across fleets of similar resources owned or operated by the enterprise and used in conjunction with defined workflows or activities. Cloud-aggregated 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.

[0588] In embodiments, digital resource 122 can be used for a wide range of applications involving or benefiting from real-time information about the attributes, status, or flow of energy generation, storage, transmission, or consumption, including the implementation of digital twins, such as a set of adaptive energy digital twin systems 134 and / or a set of stakeholder energy digital twins 148, and stakeholder energy edge solutions 108 for a set of configurations. For example, a digital twin of an urban public transportation system can predict energy demand based on commuting patterns, thereby adjusting the operation of electric buses accordingly. Similarly, digital twins can be applied to various fields, such as manufacturing units that monitor mechanical energy consumption. The integration of platform 102 with these digital twins ensures that energy is always used optimally to meet the real-time needs of the respective systems.

[0589] In recent decades, energy production, storage, and consumption, particularly those involving green or renewable energy, have been the subject of intensive research and development, resulting in higher peak power generation capacity, increased storage capacity, reduced size and weight, and improved intelligence and autonomy. Advanced energy resources and systems 104 can include a wide range of advanced energy infrastructure systems and devices resulting from combinations of features and capabilities. In embodiments, flexible hybrid energy systems 324 can be provided, adapted to meet varying energy consumption requirements. For example, more than one energy source (e.g., solar or wind) can be provided to meet the baseline energy consumption requirements for off-grid operation, while nuclear batteries can be provided to meet much higher peak power requirements, for example, for temporary resource-intensive activities, such as operating drilling rigs in mines or periodically operating large factory machinery. A wide 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 embodiments, advanced energy resources and systems 104 may include advanced energy generation systems that draw electricity from fluid flows, such as a portable turbine array 328, which can be transported to a point of consumption near wind or water flow to replace or enhance grid resources. Advanced energy resources and systems 104 may also include modular nuclear systems 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, including vehicles, localized production systems (as described elsewhere herein), smart buildings, etc. Modular nuclear systems 330 may include SMRs and other reactor types. Advanced energy resources and systems 104 may include advanced storage systems 332, including advanced batteries and fuel cells, including batteries with onboard intelligence for autonomous management, batteries with network connectivity for remote management, batteries with alternative chemistry (including green chemistry, e.g., nickel-zinc), batteries made of alternative materials or structures (e.g., diamond batteries), batteries containing power generation capabilities (e.g., nuclear batteries), and advanced fuel cells (e.g., cathode layer fuel cells, alkaline fuel cells, bulk electrolyte fuel cells, solid oxide fuel cells, and many others).

[0590] Figure 4 More detailed information about data resources refer to Figure 4Data resources 110 used for energy edge coordination may include a wide range of public datasets as well as private or proprietary datasets from enterprises or individuals. This may include datasets generated by or transmitted through the edge and IoT network system 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 about network traffic, latency, congestion, quality of service (QoS), packet loss, error rate, etc.); and event data 408 (e.g., data from event logs of edge and IoT devices, event logs from an enterprise's operational assets, event logs of wearable devices, event data detected by inspecting traffic on application programming interfaces, and event data generated by devices and systems). Event streams, 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 flow, non-cyclic flow, iterative flow, and / or cyclic flow, etc.); state data 410 (e.g., data indicating the historical, current, or predicted / expected state of an entity (e.g., machine, system, device, user, object, individua...

Claims

1. An AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: Graph neural networks, including: Each represents a group of nodes representing at least one Distributed Energy Resource (DER); and A set of edges of the set of nodes are interconnected, wherein each edge represents at least one energy-related feature between at least two nodes of the set of nodes.

2. The AI-based platform according to claim 1, wherein, At least one node in the group of nodes includes information about the at least one distributed energy resource, and the information includes at least one of the following: The power generation capacity of the at least one distributed energy resource; The power storage capacity of the at least one distributed energy resource; The power transmission capacity of the at least one distributed energy resource; The power exchange capacity of the at least one distributed energy resource; The power conversion capability of the at least one distributed energy resource; The power transmission capacity of the at least one distributed energy resource; or The power consumption capacity of the at least one distributed energy resource.

3. The AI-based platform according to claim 1, wherein, At least one node in the set of nodes indicates information about at least one energy-related pattern associated with at least one distributed energy resource, and the at least one energy-related pattern includes at least one of the following: Energy demand patterns; Energy supply models; Energy storage capacity modes; Energy market pricing models; Energy availability mode; or Energy emission patterns.

4. The AI-based platform according to claim 1, wherein, At least one edge in the graph neural network representing energy-related features between at least two nodes indicates at least one energy-related relationship between the at least two nodes, and the at least one energy-related relationship includes at least one of the following: The electrical connection relationship between at least two nodes; The power transmission capability relationship between at least two nodes; The power transmission relationship between at least two nodes; The power exchange relationship between at least two nodes; The power conversion relationship between at least two nodes; The power generation dependency relationship between at least two nodes; The power supply dependency between at least two nodes; The power transmission dependency between at least two nodes; The power storage dependency between at least two nodes; or The power consumption dependency between at least two nodes.

5. The AI-based platform according to claim 1, wherein, The graph neural network includes at least one directed edge, and the direction of the directed edge represents the directional dependency between at least two nodes in the set of nodes.

6. The AI-based platform according to claim 1, wherein, In a graph neural network, at least one edge representing energy-related features between at least two nodes indicates at least one energy-related event, and said at least one energy-related event includes at least one of the following: Energy generation events associated with the at least two nodes; Energy storage events associated with the at least two nodes; Energy transfer events associated with the at least two nodes; Energy supply events associated with the at least two nodes; Energy demand events associated with the at least two nodes; Energy surplus events associated with the at least two nodes; Energy shortage events associated with the at least two nodes; Energy consumption events associated with the at least two nodes; Energy emission events associated with the at least two nodes; or Energy leakage events associated with the at least two nodes.

7. The AI-based platform of claim 1 further includes at least one artificial intelligence model configured to generate the graph neural network based on data associated with at least one distributed energy resource.

8. The AI-based platform according to claim 7, wherein, The data associated with the at least one distributed energy resource includes at least one of the following: Energy generation data associated with the at least one distributed energy resource; Energy storage data associated with the at least one distributed energy resource; Energy transmission data associated with the at least one distributed energy resource; Energy supply data associated with the at least one distributed energy resource; Energy demand data associated with the at least one distributed energy resource; Energy surplus data associated with the at least one distributed energy resource; Energy shortage data associated with the at least one distributed energy resource; Energy consumption data associated with the at least one distributed energy resource; Energy emission data associated with the at least one distributed energy resource; or Energy leakage data associated with the at least one distributed energy resource.

9. The AI-based platform according to claim 7, wherein, The data associated with the at least one distributed energy resource includes at least one of the following: Historical data indicating at least one historical characteristic associated with the at least one distributed energy resource; Current data indicating at least one current characteristic associated with the at least one distributed energy resource; or Predictive data indicating at least one predictive feature associated with the at least one distributed energy resource.

10. The AI-based platform according to claim 1, wherein, At least one distributed energy resource represented by at least one node of the graph neural network represents at least one of the following: Wind turbines; Solar photovoltaic (PV); Flexible solar energy systems; Floating solar energy system; Solar power plant; Fuel cells; Coal mine; oil well; Natural gas wells; Modular nuclear reactor; nuclear battery; Modular hydropower generation system; Miniature turbines; Turbine array; Reciprocating engine; Gas turbine; Cogeneration plant; Biomass generator; Urban solid waste incinerator; Battery storage system; Capacitor energy storage system; Geothermal energy system; Molten salt energy storage system; Electric thermal energy storage (ETES) system; Gravity-based storage systems; Compressed fluid energy storage; Pumped hydroelectric energy storage (PHES) system; Liquid air energy storage (LAES) system; Coal storage facilities; Oil storage tanks; Natural gas storage tanks; Liquefied natural gas (LNG) storage tanks; flywheel; Gravity battery; Fuel transport vehicles; Fuel transport pipelines; Wired power transmission system; or Wireless power transmission system.

11. An AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: A supply graph neural network, each representing a group of nodes of at least one distributed energy resource (DER), is configured to generate, store, convert, and / or transmit energy; and A demand graph neural network, each representing a set of nodes of at least one distributed energy resource (DER), is configured to consume energy.

12. The AI-based platform according to claim 11, wherein, At least one node in the set of nodes included in the supply graph neural network indicates information about the at least one distributed energy resource, and the information includes at least one of the following: The power generation capacity of the at least one distributed energy resource; The power storage capacity of the at least one distributed energy resource; The power transmission capacity of the at least one distributed energy resource; The power exchange capacity of the at least one distributed energy resource; The power conversion capability of the at least one distributed energy resource; The power transmission capacity of the at least one distributed energy resource; or The power consumption capacity of the at least one distributed energy resource.

13. The AI-based platform according to claim 11, wherein, At least one node in the set of nodes included in the supply graph neural network indicates information about at least one energy-related pattern associated with at least one distributed energy resource, and the at least one energy-related pattern includes at least one of the following: Energy demand patterns; Energy supply models; Energy storage capacity modes; Energy market pricing models; Energy availability mode; or Energy emission patterns.

14. The AI-based platform according to claim 11, wherein, The supply graph neural network includes at least one edge, the at least one edge representing at least one energy correlation between at least two nodes of the supply graph neural network, and the at least one energy correlation includes at least one of the following: The electrical connection relationship between at least two nodes; The power transmission capability relationship between at least two nodes; The power transmission relationship between at least two nodes; The power exchange relationship between at least two nodes; The power conversion relationship between at least two nodes; The power generation dependency relationship between at least two nodes; The power supply dependency between at least two nodes; The power transmission dependency between at least two nodes; The power storage dependency between at least two nodes; or The power consumption dependency between at least two nodes.

15. The AI-based platform according to claim 11, wherein, The supply graph neural network includes at least one edge, which represents the energy correlation between at least two nodes of the supply graph neural network. The supply graph neural network includes at least one directed edge, and The direction of the directed edge represents the directional dependency between at least two nodes in a set of nodes of the supplying graph neural network.

16. The AI-based platform according to claim 11, wherein, The supply graph neural network includes at least one energy-related event between at least two nodes of the supply graph neural network; and The at least one energy-related event includes at least one of the following: Energy generation events associated with the at least two nodes; Energy storage events associated with the at least two nodes; Energy transfer events associated with the at least two nodes; Energy supply events associated with the at least two nodes; Energy demand events associated with the at least two nodes; Energy surplus events associated with the at least two nodes; Energy shortage events associated with the at least two nodes; Energy consumption events associated with the at least two nodes; Energy emission events associated with the at least two nodes; or Energy leakage events associated with the at least two nodes.

17. The AI-based platform of claim 11 further includes at least one artificial intelligence model configured to generate at least one of the supply graph neural network or the demand graph neural network based on data associated with at least one distributed energy resource.

18. The AI-based platform according to claim 17, wherein, The data associated with the at least one distributed energy resource includes at least one of the following: Energy generation data associated with the at least one distributed energy resource; Energy storage data associated with the at least one distributed energy resource; Energy transmission data associated with the at least one distributed energy resource; Energy supply data associated with the at least one distributed energy resource; Energy demand data associated with the at least one distributed energy resource; Energy surplus data associated with the at least one distributed energy resource; Energy shortage data associated with the at least one distributed energy resource; Energy consumption data associated with the at least one distributed energy resource; Energy emission data associated with the at least one distributed energy resource; or Energy leakage data associated with the at least one distributed energy resource.

19. The AI-based platform according to claim 17, wherein, The data associated with the at least one distributed energy resource includes at least one of the following: Historical data indicating at least one historical characteristic associated with the at least one distributed energy resource; Current data indicating at least one current characteristic associated with the at least one distributed energy resource; or Predictive data indicating at least one predictive feature associated with the at least one distributed energy resource.

20. The AI-based platform according to claim 11, wherein, At least one distributed energy resource, represented by at least one node of the supply graph neural network, represents at least one of the following: Wind turbines; Solar photovoltaic (PV); Flexible solar energy systems; Floating solar energy system; Solar power plant; Fuel cells; Coal mine; oil well; Natural gas wells; Modular nuclear reactor; nuclear battery; Modular hydropower generation system; Miniature turbines; Turbine array; Reciprocating engine; Gas turbine; Cogeneration plant; Biomass generator; Urban solid waste incinerator; Battery storage system; Capacitor energy storage system; Geothermal energy system; Molten salt energy storage system; Electric thermal energy storage (ETES) system; Gravity-based storage systems; Compressed fluid energy storage; Pumped hydroelectric energy storage (PHES) system; Liquid air energy storage (LAES) system; Coal storage facilities; Oil storage tanks; Natural gas storage tanks; Liquefied natural gas (LNG) storage tanks; flywheel; Gravity battery; Fuel transport vehicles; Fuel transport pipelines; Wired power transmission system; or Wireless power transmission system.

21. The AI-based platform of claim 11 further includes an AI-based energy coordination model, the AI-based energy coordination model being configured to reserve at least one energy supply capacity of at least one node of the supply graph neural network for at least one energy demand of at least one node of the demand graph neural network.

22. The AI-based platform of claim 11 further includes an AI-based energy coordination model configured to coordinate and manage electricity and energy by meshing the supply graph neural network and the demand graph neural network.

23. The AI-based platform according to claim 22, wherein, The AI-based energy coordination model meshes the supply and demand graph neural networks by matching each node of the demand graph neural network with at least one node of the supply graph neural network.

24. The AI-based platform according to claim 22, wherein, The AI-based energy coordination model uses an energy-related event graph neural network to mesh the supply graph neural network and the demand graph neural network, and the energy-related event graph neural network includes: Each node represents a set of nodes representing at least one energy-related event involving at least one node of the supply graph neural network and / or at least one node of the demand graph neural network; and A set of edges associated with at least two nodes of the energy-related event graph neural network.

25. The AI-based platform according to claim 24, wherein, The AI-based energy coordination model is configured to generate the energy-related event graph neural network based on at least one interaction between at least one node of the supply graph neural network and / or at least one node of the demand graph neural network.

26. An AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: Representing at least one digital twin of at least one distributed energy resource (DER); and Graph neural networks, including: A set of nodes associated with at least one digital twin; and A set of edges of the set of nodes are interconnected respectively.

27. The AI-based platform according to claim 26, wherein, At least one node in the group of nodes includes information about the at least one distributed energy resource, and the information includes at least one of the following: The power generation capacity of the at least one distributed energy resource; The power storage capacity of the at least one distributed energy resource; The power transmission capacity of the at least one distributed energy resource; The power exchange capacity of the at least one distributed energy resource; The power conversion capability of the at least one distributed energy resource; The power transmission capacity of the at least one distributed energy resource; or The power consumption capacity of the at least one distributed energy resource.

28. The AI-based platform according to claim 26, wherein, At least one node in the set of nodes indicates information about at least one energy-related pattern associated with at least one distributed energy resource, and the at least one energy-related pattern includes at least one of the following: Energy demand patterns; Energy supply models; Energy storage capacity modes; Energy market pricing models; Energy availability mode; or Energy emission patterns.

29. The AI-based platform according to claim 26, wherein, At least one edge in the graph neural network representing energy-related features between at least two nodes indicates at least one energy-related relationship between the at least two nodes, and the at least one energy-related relationship includes at least one of the following: The electrical connection relationship between at least two nodes; The power transmission capability relationship between at least two nodes; The power transmission relationship between at least two nodes; The power exchange relationship between at least two nodes; The power conversion relationship between at least two nodes; The power generation dependency relationship between at least two nodes; The power supply dependency between at least two nodes; The power transmission dependency between at least two nodes; The power storage dependency between at least two nodes; or The power consumption dependency between at least two nodes.

30. The AI-based platform according to claim 26, wherein, The graph neural network includes at least one directed edge, and the direction of the directed edge represents the directional dependency between at least two nodes in the set of nodes.

31. The AI-based platform according to claim 26, wherein, In a graph neural network, at least one edge representing energy-related features between at least two nodes indicates at least one energy-related event, and said at least one energy-related event includes at least one of the following: Energy generation events associated with the at least two nodes; Energy storage events associated with the at least two nodes; Energy transfer events associated with the at least two nodes; Energy supply events associated with the at least two nodes; Energy demand events associated with the at least two nodes; Energy surplus events associated with the at least two nodes; Energy shortage events associated with the at least two nodes; Energy consumption events associated with the at least two nodes; Energy emission events associated with the at least two nodes; or Energy leakage events associated with the at least two nodes.

32. The AI-based platform of claim 26 further includes at least one artificial intelligence model configured to generate the graph neural network based on data indicated by the at least one digital twin.

33. The AI-based platform according to claim 32, wherein, The data indicated by the at least one digital double value includes at least one of the following: Energy generation data associated with the at least one distributed energy resource; Energy storage data associated with the at least one distributed energy resource; Energy transmission data associated with the at least one distributed energy resource; Energy supply data associated with the at least one distributed energy resource; Energy demand data associated with the at least one distributed energy resource; Energy surplus data associated with the at least one distributed energy resource; Energy shortage data associated with the at least one distributed energy resource; Energy consumption data associated with the at least one distributed energy resource; Energy emission data associated with the at least one distributed energy resource; or Energy leakage data associated with the at least one distributed energy resource.

34. The AI-based platform according to claim 32, wherein, The data associated with the at least one distributed energy resource includes at least one of the following: Historical data indicating at least one historical characteristic associated with the at least one distributed energy resource; Current data indicating at least one current characteristic associated with the at least one distributed energy resource; or Predictive data indicating at least one predictive feature associated with the at least one distributed energy resource.

35. The AI-based platform according to claim 26, wherein, At least one digital twin associated with at least one node of the graph neural network represents at least one of the following: Wind turbines; Solar photovoltaic (PV); Flexible solar energy systems; Floating solar energy system; Solar power plant; Fuel cells; Coal mine; oil well; Natural gas wells; Modular nuclear reactor; nuclear battery; Modular hydropower generation system; Miniature turbines; Turbine array; Reciprocating engine; Gas turbine; Cogeneration plant; Biomass generator; Urban solid waste incinerator; Battery storage system; Capacitor energy storage system; Geothermal energy system; Molten salt energy storage system; Electric thermal energy storage (ETES) system; Gravity-based storage systems; Compressed fluid energy storage; Pumped hydroelectric energy storage (PHES) system; Liquid air energy storage (LAES) system; Coal storage facilities; Oil storage tanks; Natural gas storage tanks; Liquefied natural gas (LNG) storage tanks; flywheel; Gravity battery; Fuel transport vehicles; Fuel transport pipelines; Wired power transmission system; or Wireless power transmission system.

36. The AI-based platform according to claim 26, wherein, The AI-based platform is also configured to perform at least one simulation of energy-related events based on the graph neural network, and the at least one simulation is based on at least one digital twin simulation of at least one distributed energy resource.

37. An AI-based platform for enabling intelligent coordination and management of electricity and energy, comprising: Graph neural networks, including: Each represents a group of nodes representing at least one Distributed Energy Resource (DER); and A set of edges that interconnect the group of nodes; and An attention model that indicates at least one attention relationship between at least two nodes of the set of nodes in the graph neural network.

38. The AI-based platform according to claim 37, wherein, The attention model also includes a dependency model that indicates at least one energy-related dependency between at least two nodes of the set of nodes in the graph neural network, and the dependency model is based on a set of edges that interconnect the set of nodes.

39. The AI-based platform of claim 38 further includes an AI-based energy coordination model, the AI-based energy coordination model being configured to coordinate and manage electricity and energy among the at least one distributed energy resource based on the dependency model.

40. The AI-based platform according to claim 37, wherein, The attention model also includes an energy flow model that indicates the energy flow between at least two nodes in the set of nodes of the graph neural network, and the energy flow model is based on the set of edges that interconnect the set of nodes.

41. The AI-based platform of claim 40 further includes an AI-based energy coordination model, the AI-based energy coordination model being configured to coordinate and manage electricity and energy among the at least one distributed energy resource based on the energy flow model.

42. The AI-based platform according to claim 37, wherein, At least one node in the group of nodes includes information about the at least one distributed energy resource, and the information includes at least one of the following: The power generation capacity of the at least one distributed energy resource; The power storage capacity of the at least one distributed energy resource; The power transmission capacity of the at least one distributed energy resource; The power exchange capacity of the at least one distributed energy resource; The power conversion capability of the at least one distributed energy resource; The power transmission capacity of the at least one distributed energy resource; or The power consumption capacity of the at least one distributed energy resource.

43. The AI-based platform according to claim 37, wherein, At least one node in the set of nodes indicates information about at least one energy-related pattern associated with at least one distributed energy resource, and the at least one energy-related pattern includes at least one of the following: Energy demand patterns; Energy supply models; Energy storage capacity modes; Energy market pricing models; Energy availability mode; or Energy emission patterns.

44. The AI-based platform according to claim 37, wherein, At least one edge in the graph neural network representing energy-related features between at least two nodes indicates at least one energy-related relationship between the at least two nodes, and the at least one energy-related relationship includes at least one of the following: The electrical connection relationship between at least two nodes; The power transmission capability relationship between at least two nodes; The power transmission relationship between at least two nodes; The power exchange relationship between at least two nodes; The power conversion relationship between at least two nodes; The power generation dependency relationship between at least two nodes; The power supply dependency between at least two nodes; The power transmission dependency between at least two nodes; The power storage dependency between at least two nodes; or The power consumption dependency between at least two nodes.

45. The AI-based platform according to claim 37, wherein, The graph neural network includes at least one directed edge, and the direction of the directed edge represents the directional dependency between at least two nodes in the set of nodes.

46. ​​The AI-based platform according to claim 37, wherein, In a graph neural network, at least one edge representing energy-related features between at least two nodes indicates at least one energy-related event, and said at least one energy-related event includes at least one of the following: Energy generation events associated with the at least two nodes; Energy storage events associated with the at least two nodes; Energy transfer events associated with the at least two nodes; Energy supply events associated with the at least two nodes; Energy demand events associated with the at least two nodes; Energy surplus events associated with the at least two nodes; Energy shortage events associated with the at least two nodes; Energy consumption events associated with the at least two nodes; Energy emission events associated with the at least two nodes; or Energy leakage events associated with the at least two nodes.

47. The AI-based platform of claim 37 further includes at least one artificial intelligence model configured to generate the graph neural network based on data associated with at least one distributed energy resource.

48. The AI-based platform according to claim 47, wherein, The data associated with the at least one distributed energy resource includes at least one of the following: Energy generation data associated with the at least one distributed energy resource; Energy storage data associated with the at least one distributed energy resource; Energy transmission data associated with the at least one distributed energy resource; Energy supply data associated with the at least one distributed energy resource; Energy demand data associated with the at least one distributed energy resource; Energy surplus data associated with the at least one distributed energy resource; Energy shortage data associated with the at least one distributed energy resource; Energy consumption data associated with the at least one distributed energy resource; Energy emission data associated with the at least one distributed energy resource; or Energy leakage data associated with the at least one distributed energy resource.

49. The AI-based platform according to claim 47, wherein, The data associated with the at least one distributed energy resource includes at least one of the following: Historical data indicating at least one historical characteristic associated with the at least one distributed energy resource; Current data indicating at least one current characteristic associated with the at least one distributed energy resource; or Predictive data indicating at least one predictive feature associated with the at least one distributed energy resource.

50. The AI-based platform according to claim 37, wherein, At least one distributed energy resource represented by at least one node of the graph neural network represents at least one of the following: Wind turbines; Solar photovoltaic (PV); Flexible solar energy systems; Floating solar energy system; Solar power plant; Fuel cells; Coal mine; oil well; Natural gas wells; Modular nuclear reactor; nuclear battery; Modular hydropower generation system; Miniature turbines; Turbine array; Reciprocating engine; Gas turbine; Cogeneration plant; Biomass generator; Urban solid waste incinerator; Battery storage system; Capacitor energy storage system; Geothermal energy system; Molten salt energy storage system; Electric thermal energy storage (ETES) system; Gravity-based storage systems; Compressed fluid energy storage; Pumped hydroelectric energy storage (PHES) system; Liquid air energy storage (LAES) system; Coal storage facilities; Oil storage tanks; Natural gas storage tanks; Liquefied natural gas (LNG) storage tanks; flywheel; Gravity battery; Fuel transport vehicles; Fuel transport pipelines; Wired power transmission system; or Wireless power transmission system.

51. An AI-based platform for realizing intelligent coordination and management of electricity and energy, comprising: Graph neural networks, including: Each represents a group of nodes representing at least one Distributed Energy Resource (DER); and A set of edges that interconnect the set of nodes respectively; and A large language model configured to generate at least one description including the set of nodes and the set of edges in a graph neural network.

52. The AI-based platform according to claim 51, wherein, The large language model includes at least one converter model.

53. The AI-based platform according to claim 51, wherein, At least one description generated by the large language model includes at least one of the following: A description of at least one energy-related capability of at least one distributed energy resource associated with at least one node of the set of nodes; A description of the energy-related capabilities of at least a portion of the said set of nodes; A description of the energy-related costs of at least one distributed energy resource associated with at least one node in the set of nodes; A description of the energy-related costs associated with at least a portion of the said set of nodes; Description of the energy-related deficiencies of the at least one distributed energy resource; Description of energy-related deficiencies based on at least a portion of the said set of nodes; A description of energy-related events associated with at least one node of the set of nodes; or Description of aggregated energy-related events based on at least a portion of the set of nodes.

54. The AI-based platform according to claim 51, wherein, At least one description generated by the large language model is based on at least one cue, and the at least one cue is associated with at least one of the following: At least one distributed energy resource associated with the graph neural network; At least one distributed energy resource capability associated with the graph neural network; At least one distributed energy resource feature associated with the graph neural network; or At least one energy-related event associated with the graphical neural network.

55. The AI-based platform of claim 51 further includes an AI-based energy coordination model, the AI-based energy coordination model being configured to coordinate and manage electricity and energy in the at least one distributed energy resource based on the at least one description generated by the large language model.

56. The AI-based platform of claim 51 further includes an AI-based energy presentation model, said AI-based energy presentation model presenting the at least one description of at least one distributed energy resource based on the at least one description generated by the large language model.

57. The AI-based platform according to claim 51, wherein, At least one node in the group of nodes includes information about the at least one distributed energy resource, and the information includes at least one of the following: The power generation capacity of the at least one distributed energy resource; The power storage capacity of the at least one distributed energy resource; The power transmission capacity of the at least one distributed energy resource; The power exchange capacity of the at least one distributed energy resource; The power conversion capability of the at least one distributed energy resource; The power transmission capacity of the at least one distributed energy resource; or The power consumption capacity of the at least one distributed energy resource.

58. The AI-based platform according to claim 51, wherein, At least one node in the set of nodes indicates information about at least one energy-related pattern associated with at least one distributed energy resource, and the at least one energy-related pattern includes at least one of the following: Energy demand patterns; Energy supply models; Energy storage capacity modes; Energy market pricing models; Energy availability mode; or Energy emission patterns.

59. The AI-based platform according to claim 51, wherein, At least one edge in the graph neural network representing energy-related features between at least two nodes indicates at least one energy-related relationship between the at least two nodes, and the at least one energy-related relationship includes at least one of the following: The electrical connection relationship between at least two nodes; The power transmission capability relationship between at least two nodes; The power transmission relationship between at least two nodes; The power exchange relationship between at least two nodes; The power conversion relationship between at least two nodes; The power generation dependency relationship between at least two nodes; The power supply dependency between at least two nodes; The power transmission dependency between at least two nodes; The power storage dependency between at least two nodes; or The power consumption dependency between at least two nodes.

60. The AI-based platform according to claim 51, wherein, The graph neural network includes at least one directed edge, and the direction of the directed edge represents the directional dependency between at least two nodes in the set of nodes.

61. The AI-based platform according to claim 51, wherein, In a graph neural network, at least one edge representing energy-related features between at least two nodes indicates at least one energy-related event, and said at least one energy-related event includes at least one of the following: Energy generation events associated with the at least two nodes; Energy storage events associated with the at least two nodes; Energy transfer events associated with the at least two nodes; Energy supply events associated with the at least two nodes; Energy demand events associated with the at least two nodes; Energy surplus events associated with the at least two nodes; Energy shortage events associated with the at least two nodes; Energy consumption events associated with the at least two nodes; Energy emission events associated with the at least two nodes; or Energy leakage events associated with the at least two nodes.

62. The AI-based platform of claim 51 further includes at least one artificial intelligence model configured to generate the graph neural network based on data associated with at least one distributed energy resource.

63. The AI-based platform according to claim 62, wherein, The data associated with the at least one distributed energy resource includes at least one of the following: Energy generation data associated with the at least one distributed energy resource; Energy storage data associated with the at least one distributed energy resource; Energy transmission data associated with the at least one distributed energy resource; Energy supply data associated with the at least one distributed energy resource; Energy demand data associated with the at least one distributed energy resource; Energy surplus data associated with the at least one distributed energy resource; Energy shortage data associated with the at least one distributed energy resource; Energy consumption data associated with the at least one distributed energy resource; Energy emission data associated with the at least one distributed energy resource; or Energy leakage data associated with the at least one distributed energy resource.

64. The AI-based platform according to claim 62, wherein, The data associated with the at least one distributed energy resource includes at least one of the following: Historical data indicating at least one historical characteristic associated with the at least one distributed energy resource; Current data indicating at least one current characteristic associated with the at least one distributed energy resource; or Predictive data indicating at least one predictive feature associated with the at least one distributed energy resource.

65. The AI-based platform according to claim 51, wherein, At least one distributed energy resource represented by at least one node of the graph neural network represents at least one of the following: Wind turbines; Solar photovoltaic (PV); Flexible solar energy systems; Floating solar energy system; Solar power plant; Fuel cells; Coal mine; oil well; Natural gas wells; Modular nuclear reactor; nuclear battery; Modular hydropower generation system; Miniature turbines; Turbine array; Reciprocating engine; Gas turbine; Cogeneration plant; Biomass generator; Urban solid waste incinerator; Battery storage system; Capacitor energy storage system; Geothermal energy system; Molten salt energy storage system; Thermal energy storage (ETES) system; gravity-based storage system; Compressed fluid energy storage; Pumped hydro-electric energy storage (PHES) systems; Liquid air energy storage (LAES) systems; Coal storage facilities; Oil storage tanks; Natural gas storage tanks; Liquefied natural gas (LNG) storage tanks; flywheels; Gravity battery; Fuel transport vehicles; Fuel transport pipelines; Wired power transmission system; or Wireless power transmission system.

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