AI-based energy edge platform, system, and method

Abstract

An AI-based energy edge platform has a wide range of features, components, and capabilities for managing and improving legacy infrastructure and coordinating with distributed systems to support critical use cases for a variety of enterprises. The platform can incorporate new technologies that enable efficiency, agility, engagement, and profitability for the ecosystem and individual energy edge nodes. Embodiments can forecast, plan, and manage energy demand and usage in more distributed environments. Embodiments can use AI, IoT, and technologies to more efficiently filter, process, and move data over communications networks. Platform embodiments can leverage energy market connectivity, communications, and transaction enablement platforms. Embodiments can employ intelligent provisioning, data aggregation, and analytics.

Description

[Background technology]

[0001] Energy remains a critical component of the global economy and is undergoing an evolution and transformation that will involve changes in energy generation, storage, planning, demand management, consumption, and supply systems and processes. These changes are enabled by the development and convergence of numerous diverse technologies, including more decentralized, modular, mobile, and portable energy generation and storage technologies that will make energy markets more decentralized and localized, and a variety of technologies that will facilitate energy management in more decentralized systems, such as edge, Internet of Things, and networking technologies, advanced computing and artificial intelligence technologies, and transaction-enabling technologies (e.g., blockchain, distributed ledgers, and smart contracts). The convergence of these networking, computing, and intelligence technologies with more decentralized energy technologies is referred to herein as the "energy edge."

[0002] Over the next few decades, the energy market is expected to evolve and transform from a highly centralized model dependent on fossil fuels and controlled grids to a more decentralized and distributed model that includes many more localized generation-storage-consumption systems. This transition will likely continue for many years, with hybrid systems in which traditional grids become more intelligent and distributed systems play a larger role. There is a need for a platform that can interface with distributed systems and make it easier to manage and improve legacy infrastructure. Summary of the Invention

[0003] Provided herein is an AI-based energy edge platform with a wide range of features, components, and capabilities for managing and improving legacy infrastructure and coordinating with distributed systems that support critical use cases for various enterprises. The platform can incorporate new technologies that enable efficiency, agility, engagement, and profitability for the ecosystem and individual energy edge nodes. Embodiments are guided by, and potentially integrated with, methodologies and systems used to forecast, plan, and manage energy demand and utilization in larger distributed environments. Embodiments can use AI and AI enablers such as IoT, which may be deployed in massively dense data environments (reflecting the proliferation of sensors in smart energy systems and IoT), and technologies that more efficiently filter, process, and move data over communication networks. Platform embodiments can leverage energy market connectivity, communications, and transaction enablement platforms. Embodiments can employ intelligent provisioning, data aggregation, and analytics. Among many use cases, the platform can enable improvements in optimizing energy generation, storage, supply, and / or corporate consumption in businesses (e.g., buildings, data centers, factories, etc.), integrating and using new power generation and energy storage technologies and assets (distributed energy resources, or "DERs"), optimizing energy utilization across existing networks, and digitalizing existing infrastructure and support systems.

[0004] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, comprising an adaptive energy data pipeline configured to communicate data across a set of nodes in a network, each node of the set of nodes adapted to operate on an energy dataset related to at least one of energy generation, energy storage, energy delivery, or energy consumption, and at least one node of the set of nodes configured to filter, compress, transform, error correct, and / or route at least a portion of the energy dataset based on at least one of a set of network conditions, data size, data granularity, or data content via one or both of an algorithm or a set of rules.

[0005] In some aspects, the technology described herein relating to the AI-based platform, the adaptive energy data pipeline is further configured to adapt transport of data over a network and / or communication system, the adapting being based on one or more of congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transportation cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, and user-set conditions.

[0006] In some aspects, the technology described herein relating to an AI-based platform further includes an 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-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy use preference.

[0007] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform one or more of: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; or adjusting energy data.

[0008] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by one or more machines, one or more plants, or one or more vehicles in a vehicle fleet.

[0009] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the adaptive energy data pipeline is further configured to perform one or more of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; analyzing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data; routing and / or transmitting the energy-related data; or maintaining security of the energy-related data.

[0010] In some embodiments, the technology described herein relating to an AI-based platform includes the energy dataset being based on one or more public data resources, the public data resources including one or more of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0011] In some aspects, the technology described herein relating to an AI-based platform includes the energy dataset based on one or more enterprise data resources, the enterprise data resources including 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 operating data.

[0012] In some embodiments, the technology described herein relating to an AI-based platform further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset that is based on one or more human tags and / or labels, one or more human interactions with the hardware and / or software system, one or more results, one or more AI-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0013] In some aspects, the technology described herein relating to an AI-based platform includes at least one node of a set of nodes configured to orchestrate the delivery of energy to one or more points of consumption, the delivery of energy including one or more of one or more fixed transmission lines, one or more instances of wireless energy transmission, the delivery of one or more fuels, or the delivery of one or more stored energies.

[0014] In some aspects, the technologies described herein relating to the AI-based platform are further configured such that at least one node of the set of nodes records, in the distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events including one or more of an energy purchase and / or sale event, a service fee for the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0015] In some aspects, the technology described herein relating to an AI-based platform includes at least one node of the set of nodes being located in an off-grid environment, the off-grid environment including one or more of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0016] In some aspects, the techniques described herein relating to an AI-based platform are configured such that the adaptive energy data pipeline is further configured to monitor one or both of an overall energy consumption by at least a portion of the set of nodes, or monitor at least one role of a node in an overall energy consumption by at least a portion of the set of nodes, and, based on the monitoring, perform one or more of managing energy consumption by the set of nodes, forecasting energy consumption by the set of nodes, or provisioning resources associated with energy consumption by the set of nodes.

[0017] In some aspects, the technology described herein relating to an AI-based platform includes a set of nodes in a network including an adaptive energy data pipeline, the set of edge network devices governing at least one of energy consumption, energy storage, energy supply, or energy consumption by a set of operating devices controlled via the edge network devices.

[0018] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that the adaptive energy data pipeline automatically selects a least-cost path for data communicated between a set of nodes, the selection being based on low-priority energy usage associated with the data.

[0019] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that the adaptive energy data pipeline automatically selects a high-quality service route for data communicated between a set of nodes, the selection being based on a high-priority energy usage associated with the data.

[0020] In some embodiments, the technology described herein relating to an AI-based platform includes an adaptive energy data pipeline that includes a set of artificial intelligence capabilities configured to adapt the pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs.

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

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

[0023] In some aspects, the technology described herein relating to an AI-based platform is configured such that the adaptive energy data pipeline performs enterprise contextual adaptation by automatically processing data based on one or more of an enterprise operating context, an enterprise transactional context, or an enterprise financial context.

[0024] In some aspects, the techniques described herein relating to an AI-based platform are further configured for at least one node of the set of nodes to coordinate communication with at least one other node of the set of nodes to accommodate reporting of data related to at least one of energy generation, energy storage, energy delivery, or energy consumption to the at least one other node.

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

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

[0027] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that the adaptive energy data pipeline requests reported data from at least one node of the set of nodes, the reported data being based on a level of granularity, the level of granularity being based on a machine priority associated with the reported data.

[0028] In some aspects, the technologies described herein relating to an AI-based platform are further configured for the adaptive energy data pipeline to prioritize transmission of the reported data through the adaptive energy data pipeline, the prioritization based on oversight responsibilities associated with the reported data.

[0029] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, comprising a set of adaptive and autonomous data processing systems, each of which is configured to collect data related to energy generation, storage, or supply from a set of edge devices responsible for operational control of a set of distributed energy resources, and to autonomously adjust a set of operational parameters for such operational control based on the collected data.

[0030] In some aspects, the AI-based platform technology described herein further comprises each of the adaptive and autonomous data processing systems adapted to adapt the transport of data over the network and / or communication system, the adaptation being based on one or more of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0031] In some aspects, the technology described herein relating to an AI-based platform includes adaptive and autonomous data processing systems each including an adaptive energy digital twin representing one or more of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, market conditions, or energy usage preferences.

[0032] In some embodiments, the technology described herein relating to an AI-based platform includes an adaptive energy digital twin, where each of the adaptive and autonomous data processing systems is configured to perform one or more of: providing visual and / or analytical indicators of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; or adjusting energy data.

[0033] In some embodiments, the technology described herein relating to an AI-based platform includes an adaptive energy digital twin, each of which is an adaptive and autonomous data processing system configured to generate visual and / or analytical indicators of energy consumption by one or more machines, one or more plants, or one or more vehicles in a vehicle fleet.

[0034] In some embodiments, the techniques described herein relating to an AI-based platform are further configured such that each of the adaptive and autonomous data processing systems performs one or more of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; analyzing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data; routing and / or transmitting the energy-related data; or maintaining security of the energy-related data.

[0035] In some embodiments, the technologies described herein relating to an AI-based platform include the energy edge data being based on one or more public data resources, where the public data resources include one or more of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0036] In some aspects, the technology described herein relating to an AI-based platform is based on one or more enterprise data resources, where the enterprise data resources include 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 operating data.

[0037] In some embodiments, the technology described herein relating to an AI-based platform further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset that is based on one or more human tags and / or labels, one or more human interactions with the hardware and / or software system, one or more results, one or more AI-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0038] In some aspects, the AI-based platform technology described herein includes each of the adaptive and autonomous data processing systems further configured to orchestrate the delivery of energy to one or more points of consumption, the delivery of energy including one or more of one or more fixed transmission lines, one or more instances of wireless energy transmission, the delivery of one or more fuels, or the delivery of one or more stored energy.

[0039] In some aspects, the technology described herein relating to an AI-based platform is further configured such that each of the adaptive and autonomous data processing systems records, in the distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events including one or more of an energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0040] In some aspects, the technology described herein relating to an AI-based platform involves at least one of the adaptive and autonomous data processing systems being deployed in an off-grid environment, the off-grid environment including one or more of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0041] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy data pipeline configured to communicate data between the set of nodes in the network.

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

[0043] In some embodiments, the techniques described herein relating to an AI-based platform are further configured such that the adaptive energy data pipeline automatically selects a least-cost path for data communicated among a set of nodes, the selection being based on low-priority energy usage associated with the data.

[0044] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that the adaptive energy data pipeline automatically selects a high-quality service route for data communicated between a set of nodes, the selection being based on a high-priority energy usage associated with the data.

[0045] In some embodiments, the technology described herein relating to an AI-based platform includes an adaptive energy data pipeline that includes a set of artificial intelligence capabilities configured to adapt the pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs.

[0046] In some aspects, the technology described herein relating to an AI-based platform includes an adaptive energy data pipeline that includes a self-organizing data storage configured to store data on a device based on one or more of a pattern of the data, a content of the data, or a context of the data.

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

[0048] In some aspects, the technology described herein relating to an AI-based platform is configured such that the adaptive energy data pipeline performs enterprise context adaptation by automatically processing data based on one or more of an enterprise operating context, an enterprise transactional context, or an enterprise financial context.

[0049] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one of the adaptive and autonomous data processing systems determines a schedule for the set of processes based on at least one priority and / or need associated with the set of distributed energy resources.

[0050] In some aspects, the technology described herein relating to an AI-based platform is further configured such that at least one of the adaptive and autonomous data processing systems coordinates communication with at least one edge device of the set of edge devices based on at least one priority and / or need associated with the set of distributed energy resources, the communication relating to investigating energy generation, storage, or delivery by the distributed energy resources.

[0051] In some aspects, the technology described herein relating to the AI-based platform is further configured such that at least one of the adaptive and autonomous data processing systems issues instructions to at least one edge device of the set of edge devices, the instructions being based on an investigation of energy generation, storage, or delivery by the distributed energy resource, and the instructions causing the at least one edge device to coordinate the energy generation, storage, or delivery by the at least one edge device.

[0052] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a system configured to perform automated and coordinated governance of a set of operationally coupled energy entities within an energy grid and a set of distributed edge energy resources, at least one of which is functionally independent from the energy grid.

[0053] In some aspects, the AI-based platform technology described herein further configures the system to adapt the transport of data over the network and / or communication system based on one or more of congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, cost of transport conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-set conditions.

[0054] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing one or more of an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy use preference.

[0055] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform one or more of: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; highlighting the energy data; or adjusting the energy data.

[0056] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by one or more machines, one or more plants, or one or more vehicles in a vehicle fleet.

[0057] In some embodiments, the technology described herein relating to an AI-based platform is further configured for the system to perform one or more of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; analyzing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data; routing and / or transporting the energy-related data; or maintaining security of the energy-related data.

[0058] In some embodiments, the technology described herein relating to an AI-based platform further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset that is based on one or more human tags and / or labels, one or more human interactions with the hardware and / or software system, one or more results, one or more AI-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0059] In some aspects, the technology described herein relating to the AI-based platform is further configured to orchestrate the delivery of energy to one or more points of consumption, the delivery of energy including one or more of one or more fixed transmission lines, one or more instances of wireless energy transmission, delivery of one or more fuels, or delivery of one or more stored energies.

[0060] In some aspects, the technology described herein relating to the AI-based platform further configures the system to record, in the distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events including one or more of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0061] In some aspects, the technology described herein relating to an AI-based platform includes at least one of the distributed energy edge resources deployed in an off-grid environment, the off-grid environment including one or more of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0062] In some aspects, the technology described herein relating to an AI-based platform is configured such that the system facilitates governance of the mining environment.

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

[0064] In some aspects, the technology described herein relates to an AI-based platform, where the system includes a set of carbon-aware energy edge solutions, the solutions include discovering, configuring, and enforcing a set of policies regarding carbon generation.

[0065] In some aspects, the techniques described herein relating to an AI-based platform require the solution to monitor energy production by the mining environment to track the carbon emissions generated by the mining environment.

[0066] In some aspects, the technology described herein relating to an AI-based platform requires that the solution offsets carbon emissions from an extractive environment against energy production from the extractive environment.

[0067] In some aspects, 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 policy deployment solutions, the solutions being configurable via user interaction with the user interface.

[0068] In some embodiments, the technology described herein relating to an AI-based platform system includes an intelligent agent trained to generate policies related to governance of the mining environment, where the intelligent agent is trained based on a training set of historical data, feedback from outcomes, and human policy-setting interactions.

[0069] In some aspects, the technology described herein relating to an AI-based platform facilitates governance of the mining environment by the system enforcing policies including one or more of: setting a maximum energy usage for an entity for a period of time; setting a maximum energy cost for an entity for a period of time; setting a maximum carbon production for an entity for a period of time; setting a maximum pollution emissions amount for an entity for a period of time; setting a carbon offset requirement; setting a renewable energy credit requirement; setting an energy mix requirement; setting a minimum rate of return based on energy and other marginal costs for a production entity; or setting a minimum storage baseline for an energy storage entity.

[0070] In some aspects, the technology described herein relating to an AI-based platform includes a set of energy governance smart contract solutions configured to enable users of the platform to design, generate, and deploy smart contracts that automatically provide a degree of governance for a range of energy transactions.

[0071] In some aspects, the technology described herein relating to an AI-based platform includes a set of automated energy financial control solutions configured to enable users of the system to design, generate, configure, or deploy policies for controlling financial factors related to one or more of energy generation, storage, delivery, or utilization.

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

[0073] In some aspects, the technology described herein relating to an AI-based platform is further configured for the system to perform monitoring of production rates of energy by the set of energy entities and to coordinate automated and coordinated governance of the set of energy entities based on the monitoring of the production rates.

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

[0075] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including an adaptive energy data pipeline configured to communicate data across a set of nodes in a network, at least a subset of the set of nodes configured to set at least one parameter of data communications associated with the adaptive energy data pipeline by at least one rule or algorithm, the at least one parameter based on a set of indicators of current network conditions to optimize energy used in the data communications.

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

[0077] In some aspects, the AI-based platform technology described herein is further configured such that the adaptive energy data pipeline adapts transport of data over the network and / or communication system, the adaptation being based on one or more of congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, cost of transport conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, and user-set conditions.

[0078] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing one or more of an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy use preference condition.

[0079] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform one or more of: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; highlighting the energy data; or adjusting the energy data.

[0080] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by one or more machines, one or more plants, or one or more vehicles in a vehicle fleet.

[0081] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the adaptive energy data pipeline performs one or more of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; parsing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data; routing and / or transporting the energy-related data; or maintaining security of the energy-related data.

[0082] In some aspects, the technologies described herein relating to AI-based platforms base data on one or more public data resources, including one or more of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0083] In some aspects, the technologies described herein relating to an AI-based platform are based on data from one or more enterprise data resources, where the enterprise data resources include 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 operating data.

[0084] In some embodiments, the technology described herein relating to an AI-based platform further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset being based on one or more human tags and / or labels, one or more human interactions with the 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.

[0085] In some aspects, the technologies described herein relating to the AI-based platform include the adaptive energy data pipeline further configured to orchestrate the delivery of energy to one or more points of consumption, the delivery of energy including one or more of one or more fixed transmission lines, one or more instances of wireless energy transmission, the delivery of one or more fuels, or the delivery of one or more stored energies.

[0086] In some aspects, the technology described herein relating to the AI-based platform is further configured such that the adaptive energy data pipeline records, in the distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events including one or more of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0087] In some aspects, the technology described herein relating to an AI-based platform includes at least a portion of an adaptive energy data pipeline deployed in an off-grid environment, the off-grid environment including one or more of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0088] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that the adaptive energy data pipeline monitors one or both of an overall energy consumption by at least a portion of the set of nodes and a role of at least one node of the set of nodes in the overall energy consumption by at least a portion of the set of nodes, and, based on the monitoring, performs one or more of: managing energy consumption by the set of nodes, predicting energy consumption by the set of nodes, or providing resources related to energy consumption by the set of nodes.

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

[0090] In some embodiments, the techniques described herein relating to an AI-based platform are further configured such that the adaptive energy data pipeline automatically selects a least-cost path for data communicated across the set of nodes, the selection being based on low-priority energy usage associated with the data.

[0091] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that the adaptive energy data pipeline automatically selects a high-quality service route for data communicated across the set of nodes, the selection being based on a high-priority energy usage associated with the data.

[0092] In some embodiments, the technology described herein relating to an AI-based platform includes an adaptive energy data pipeline that includes a set of artificial intelligence capabilities configured to adapt the pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs.

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

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

[0095] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a digital twin system having a digital twin of a mining environment, the digital twin including at least one parameter sensed by a sensor in the mining environment.

[0096] In some embodiments, the technology described herein relating to an AI-based platform includes at least one parameter related to one or more of an unmined portion of the mining environment, mining of materials from the mining environment, a smart container event including a smart container associated with the mining environment, a physiological state of a miner associated with the mining environment, a transaction-related event associated with the mining environment, or compliance of the mining environment with one or more contracts, regulations, and / or legal policies.

[0097] In some aspects, the technology described herein relating to an AI-based platform further comprises a digital twin system that represents one or more of an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy use preference condition.

[0098] In some embodiments, the technology described herein relating to an AI-based platform is further configured for the digital twin system to perform one or more of: providing visual and / or analytical indicators of energy consumption by one or more energy consumers; filtering the energy data; highlighting the energy data; or adjusting the energy data.

[0099] In some embodiments, the technology described herein relating to an AI-based platform, wherein the digital twin system is further configured to generate visual and / or analytical indicators of energy consumption by one or more of the one or more machines, one or more plants, or one or more vehicles in a vehicle fleet.

[0100] In some aspects, the techniques described herein relating to an AI-based platform base parameters on one or more public data resources, including one or more of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0101] In some aspects, the techniques described herein relating to an AI-based platform include the parameters being based on one or more enterprise data resources, where the enterprise data resources include 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 operating data.

[0102] In some aspects, the technology described herein relating to an AI-based platform includes a digital twin system including at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset being based on one or more human tags and / or labels, one or more human interactions with hardware and / or software systems, one or more outcomes, one or more AI-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0103] In some aspects, the AI-based platform technology described herein includes the digital twin system further configured to orchestrate the delivery of energy to one or more points of consumption, the delivery of energy including one or more of one or more fixed transmission lines, one or more instances of wireless energy transmission, the delivery of one or more fuels, or the delivery of one or more stored energy.

[0104] In some aspects, the technology described herein relating to the AI-based platform is further configured such that the digital twin system records, in the distributed ledger and / or blockchain, one or more energy-related events, including one or more of an energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0105] In some aspects, the technology described herein relating to an AI-based platform involves deploying a digital twin system in an off-grid environment, the off-grid environment including one or more of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0106] In some embodiments, the technology described herein relating to an AI-based platform, the mining environment is a data mining environment.

[0107] In some embodiments, the technology described herein relating to an AI-based platform, a mining environment is a set of resources for performing computational operations.

[0108] In some embodiments, the technology described herein relating to an AI-based platform includes the platform using mine-level Internet of Things (IoT) sensing of the mining environment, ground-penetrating sensing of unmined portions of the mining environment, mass spectrometry and computer vision-based sensing of mined materials, asset tagging of smart containers, wearable devices to detect the physiological state of miners, secure recording and resolution of transactions and transaction-related events, smart contracts to automatically allocate revenues derived from the mining environment, and automated systems to record, report, and assess compliance with contractual, regulatory, and legal policy requirements.

[0109] In some aspects, the technology described herein relates to an AI-based platform, where the platform includes a set of carbon-aware energy edge solutions, where the solutions include discovering, configuring, and enforcing a set of policies regarding carbon generation.

[0110] In some aspects, the techniques described herein relating to an AI-based platform require the solution to monitor energy production by the mining environment to track the carbon emissions generated by the mining environment.

[0111] In some aspects, the technology described herein relating to an AI-based platform solution calls for offsetting carbon emissions from extractive environments against energy production from the extractive environments.

[0112] In some aspects, the technology described herein relates to an AI-based platform, wherein the platform includes a user interface, wherein the platform includes a set of automated energy policy deployment solutions, wherein the solutions are configurable via user interaction with the user interface.

[0113] In some embodiments, the technology described herein relating to an AI-based platform includes an intelligent agent trained to generate policies related to governance of the mining environment, where the intelligent agent is trained based on a training set of historical data, feedback from outcomes, and human policy-setting interactions.

[0114] In some aspects, the technology described herein relating to an AI-based platform facilitates governance of the extractive environment by the platform enforcing policies including one or more of: setting a maximum energy usage for an entity for a period of time; setting a maximum energy cost for an entity for a period of time; setting a maximum carbon production for an entity for a period of time; setting a maximum pollution emissions amount for an entity for a period of time; setting a carbon offset requirement; setting a renewable energy credit requirement; setting an energy mix requirement; setting a minimum rate of return based on energy and other marginal costs for a production entity; or setting a minimum storage baseline for an energy storage entity.

[0115] In some embodiments, the technology described herein relating to an AI-based platform includes at least one parameter including a sensor measurement associated with at least one piece of equipment involved in industrial operations in a mining environment.

[0116] In some aspects, the technology described herein relating to an AI-based platform includes a scheduler configured to determine a schedule for generating, storing, and / or transporting energy to at least one piece of equipment associated with industrial operations in a mining environment, the schedule being based on at least one parameter detected by a sensor.

[0117] In some embodiments, the technology described herein relating to an AI-based platform includes at least one parameter included in the digital twin that includes at least one characteristic of at least one dataset related to the mining environment.

[0118] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a governance system for a mining operation and a reporting system for communicating at least one parameter sensed by sensors in a mine of the mining operation, the at least one parameter related to compliance of the mining operation with a set of labor standards.

[0119] In some aspects, the technology described herein relating to the AI-based platform is further configured such that the reporting system adapts the transport of data over the network and / or communication system based on one or more of congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-set conditions.

[0120] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing one or more of an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facilities, a stakeholder's transportation system, market conditions, or energy use preferences.

[0121] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform one or more of: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; adjusting energy data; or generating a visual and / or analytical indication of energy consumption by one or more machines, one or more plants, or one or more vehicles in a vehicle fleet.

[0122] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the reporting system performs one or more of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; parsing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data; routing and / or transporting the energy-related data; or maintaining security of the energy-related data.

[0123] In some embodiments, the technology described herein relating to the AI-based platform is further configured such that the reporting system records, in the distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events including one or more of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0124] In some aspects, the techniques described herein relating to an AI-based platform include: at least one of the at least one parameter is based on one or more of one or more public data resources and one or more enterprise data resources; the one or more public data resources including one or more of weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; and the one or more enterprise data resources including 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 operating data.

[0125] In some embodiments, the technology described herein relating to an AI-based platform further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset being based on one or more human tags and / or labels, one or more human interactions with the hardware and / or software system, one or more outcomes, one or more AI-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0126] In some aspects, the technologies described herein relating to the AI-based platform are further configured such that the governance system orchestrates delivery of energy to one or more points of consumption, the delivery of energy including one or more fixed transmission lines, one or more instances of wireless energy transmission, delivery of one or more fuels, or delivery of one or more stored energy.

[0127] In some embodiments, the technology described herein relating to an AI-based platform includes: a set of labor criteria associated with at least one activity performed by workers in a mine; and communicating at least one parameter sensed by a sensor includes communicating an indication of performance of the at least one activity by the workers sensed by the sensor.

[0128] In some embodiments, the technology described herein relating to an AI-based platform includes associating a set of labor criteria with at least one object associated with workers in a mine, and communicating at least one parameter sensed by a sensor includes communicating an indication of detection of the at least one object by the sensor.

[0129] In some embodiments, the technology described herein relating to an AI-based platform includes the set of labor criteria including thresholds for characteristics of the mine, and the reporting system is further configured to communicate a decision based on a comparison of at least one parameter sensed by the sensor to the threshold.

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

[0131] In some embodiments, the technology described herein relating to the AI-based platform further includes an emergency response system configured to perform at least one emergency response action based on a determination that the at least one parameter sensed by the sensor indicates the occurrence of an emergency related to the mine.

[0132] In some embodiments, the technology described herein relating to an AI-based platform further includes a sensor configuration system configured to determine a configuration of the sensor for performing sensing of the at least one parameter, the configuration being based on compliance of the mining operation with a set of labor standards.

[0133] In some embodiments, the technology described herein relating to an AI-based platform comprises a set of labor criteria accessible to a sensor configuration system and specified in natural language, and the sensor configuration system is configured to determine sensor configurations based on natural language analysis of the set of labor criteria.

[0134] In some embodiments, the technologies described herein relating to an AI-based platform further include a sensor correction system configured to perform at least one sensor corrective action based on a determination of a failure of the sensor sensing the at least one parameter, the at least one sensor corrective action including one or more of: initiating a replacement of the sensor; initiating a diagnostic operation including the sensor; initiating a reconfiguration of the sensor to sense the at least one parameter differently; initiating a request to a mine worker to perform manual sensing of the sensor; or initiating a substitution of the sensor in the mine with at least one other sensor in the mine to sense the at least one parameter.

[0135] In some embodiments, the technologies described herein relating to an AI-based platform further include a compliance verification system configured to verify that at least one parameter sensed by the sensor indicates compliance of the mining operation with the set of labor standards, where verifying includes one or more of: verifying a calibration of the sensors in the mine; verifying at least one parameter sensed by at least one sensor 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 the at least one parameter by a mine worker; or requesting verification by a compliance officer that the at least one parameter indicates compliance of the mining operation with the set of labor standards.

[0136] In some embodiments, the technology described herein relating to an AI-based platform further includes a labor communication interface configured to communicate with mine workers based on at least one parameter sensed by the sensor, the communication relating to compliance of the mining operation with a set of labor standards.

[0137] In some embodiments, the technology described herein relating to an AI-based platform further includes a user interface configured to display a map of the mining operation, the map including an indication of compliance of the mining operation with a set of labor standards based on at least one parameter sensed by the sensor.

[0138] In some embodiments, the technology described herein relating to an AI-based platform, wherein the set of labor criteria includes a set of work requirements for workers to perform tasks associated with the mining operation, and the reporting system is further configured to adapt an assignment of workers to tasks based on the set of work requirements.

[0139] In some embodiments, the technology described herein relating to an AI-based platform, wherein at least one parameter includes a schedule of workers to perform tasks associated with the mining operation, and the reporting system is further configured to adapt the schedule based on compliance of the mining operation with a set of labor standards.

[0140] In some embodiments, the technology described herein relating to the AI-based platform is further configured such that the reporting system initiates at least one protocol in response to the at least one parameter sensed by the sensor, the at least one protocol being based on adjusting the at least one parameter sensed by the sensor to maintain or restore compliance of the mining operation with a set of labor standards.

[0141] In some embodiments, the technology described herein relating to the AI-based platform is further configured such that the reporting system maintains a digital record of the training and / or certification status of at least one worker associated with at least one task of the mining operation.

[0142] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, comprising a set of edge devices, each edge device of the set configured to maintain awareness of the carbon generation and / or emissions of at least one entity of a set of energy using entities linked to and / or managed by the set of edge devices.

[0143] In some aspects, the technology described herein relating to an AI-based platform is configured such that at least one edge device of the set simulates carbon generation and / or emissions of at least one entity of the set of energy using entities.

[0144] In some aspects, the technology described herein relating to an AI-based platform is configured such that at least one edge device of a set executes a set of machine learning algorithms trained on a training dataset of carbon generation data to calculate carbon generation and / or emissions metrics for a set of operational entities.

[0145] In some aspects, the technology described herein relating to an AI-based platform is configured such that at least one edge device of a set executes a set of machine learning algorithms trained on a training dataset of carbon generation data to calculate carbon generation and / or emissions metrics for a set of operational entities.

[0146] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that at least one edge device of the set adapts transport of data over the network and / or communication system, the adaptation being based on one or more of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0147] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing one or more of an energy stakeholder, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facilities, a stakeholder's transportation system, market conditions, or energy use preferences.

[0148] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform one or more of: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; adjusting energy data; or generating a visual and / or analytical indication of energy consumption by one or more machines, one or more plants, or one or more vehicles in a vehicle fleet.

[0149] In some embodiments, the techniques described herein relating to an AI-based platform are further configured such that at least one edge device of the set performs one or more of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; analyzing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data; routing and / or transporting the energy-related data; or maintaining security of the energy-related data.

[0150] In some aspects, the technologies described herein relating to an AI-based platform include at least one edge device of the set including at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset being based on one or more human tags and / or labels, one or more human interactions with the hardware and / or software system, one or more results, one or more AI-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0151] In some aspects, the technology described herein relating to an AI-based platform includes a set of at least one edge device configured to orchestrate the delivery of energy to one or more points of consumption, the delivery of energy including one or more of one or more fixed transmission lines, one or more instances of wireless energy transmission, the delivery of one or more fuels, or the delivery of one or more stored energies.

[0152] In some aspects, the technologies described herein relating to the AI-based platform are further configured such that at least one edge device of the set records, in the distributed ledger and / or blockchain, one or more energy-related events, including one or more of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0153] In some aspects, the technology described herein relating to an AI-based platform includes a set of at least one edge device deployed in an off-grid environment, the off-grid environment including one or more of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0154] In some aspects, the AI-based platform technology described herein is configured such that at least one edge device of the set is further configured to measure a change in carbon production and / or emissions over a period of time based on a comparison of current metrics of carbon production and / or emissions to historical metrics of carbon production and / or emissions.

[0155] In some aspects, the technology described herein relating to an AI-based platform is further configured such that at least one edge device of the set determines a target for carbon generation and / or emissions based on a policy for carbon generation and / or emissions.

[0156] In some aspects, the AI-based platform technology described herein is configured such that at least one edge device of the set is further configured to perform a comparison of the carbon generation and / or emissions metric to a carbon generation and / or emissions target and determine compliance of the carbon generation and / or emissions with a carbon generation and / or emissions policy based on the comparison.

[0157] In some aspects, the technology described herein relating to an AI-based platform is further configured such that at least one edge device of the set determines an environmental impact of the carbon generation and / or emissions based on the carbon generation and / or emissions metrics relative to the carbon generation and / or emissions targets.

[0158] In some aspects, the technology described herein relating to an AI-based platform includes a method for generating and / or discharging carbon emissions associated with a set of activities, wherein at least one edge device of the set is further configured to allocate at least a portion of the carbon generation and / or emissions to at least one activity of the set of activities.

[0159] In some aspects, the AI-based platform technology described herein is configured such that at least one edge device of the set is further configured to associate at least one indicator for associating the carbon generation and / or emission metric with the carbon generation and / or emission goal, the indicator including one or more of: a date and / or time period of the carbon generation and / or emission; a location of a source of the carbon generation and / or emission; a direction and / or speed of transport of the carbon generation and / or emission; a location affected by the carbon generation and / or emission; a physical metric of the carbon generation and / or emission; a chemical composition of the carbon generation and / or emission; weather patterns occurring in an area associated with the carbon generation and / or emission; a wildlife population in an area associated with the carbon generation and / or emission; or a human activity affected by the carbon generation and / or emission.

[0160] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that at least one edge device of the set transmits an alert related to carbon generation and / or emissions based on a comparison of the carbon generation and / or emissions metric to an alert threshold related to carbon generation and / or emissions.

[0161] In some aspects, the technology described herein relating to an AI-based platform is further configured such that at least one edge device of the set adjusts an activity related to carbon generation and / or emissions based on the carbon generation and / or emissions metric, the adjustment modifying a future state of the carbon generation and / or emissions.

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

[0163] In some aspects, the techniques described herein relating to an AI-based platform are further configured for at least one edge device of the set of edge devices to maintain awareness by generating at least one local report and / or alert, the at least one local report and / or alert being associated with a pattern of carbon generation and / or emissions associated with at least one entity of the set of energy using entities.

[0164] In some aspects, the technology described herein relating to an AI-based platform is further configured such that at least one edge device of the set of edge devices is further configured to modify operation of one or more pieces of equipment and / or processes associated with at least one entity of the set of energy using entities, wherein modifying the operation is based on at least one measurement of carbon generation and / or emissions associated with the at least one entity of the set of energy using entities.

[0165] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a digital twin updated by a data collection system that dynamically maintains a set of historical, current, and / or forecasted energy demand parameters for a set of fixed entities and a set of mobile entities within a defined domain, where the updates to the digital twin are based on the set of energy demand parameters.

[0166] In some aspects, the techniques described herein relating to an AI-based platform are based on one or more of: a current set of aggregate data where a set of operational entities are controlled via a set of edge networking devices linked to the set of operational entities, and the energy demand parameter is derived from demand from the set of operational entities, and the set of operational entities are controlled via a set of edge networking devices linked to the set of operational entities; a historical set of aggregate data where the set of operational entities are controlled via a set of edge networking devices linked to the set of operational entities; and a simulated set of aggregate data derived from demand from the set of operational entities.

[0167] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that the data collection system adapts transport of data over the network and / or communication system, the adaptation being based on one or more of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0168] In some aspects, the technology described herein relating to an AI-based platform provides a digital twin representing one or more of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facilities, a stakeholder's transportation system, market conditions, or energy use preferences.

[0169] In some embodiments, the AI-based platform technology described herein, the digital twin is further configured to perform one or more of: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; highlighting the energy data; adjusting the energy data; or generating a visual and / or analytical indication of energy consumption by one or more machines, one or more plants, or one or more vehicles in a vehicle fleet.

[0170] In some aspects, the technologies described herein relating to an AI-based platform include wherein at least one of the energy demand parameters is based on one or more of one or more public data resources and one or more enterprise data resources, wherein the one or more public data resources include one or more of weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources, and the one or more enterprise data resources include 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 operating data.

[0171] In some aspects, the technology described herein relating to an AI-based platform includes a digital twin 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, the training dataset being based on one or more human tags and / or labels, one or more human interactions with the 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.

[0172] In some aspects, the technology described herein relating to the AI-based platform is further configured to orchestrate the delivery of energy to one or more points of consumption, the delivery of energy including one or more fixed transmission lines, one or more instances of wireless energy transmission, the delivery of one or more fuels, or the delivery of one or more stored energy.

[0173] In some embodiments, the technology described herein relating to an AI-based platform is further configured for the digital twin to coordinate the delivery of energy to one or more consumption points based on an energy delivery and / or consumption policy.

[0174] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the digital twin determines the carbon generation and / or emissions impact of the delivery of energy to one or more points of consumption.

[0175] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the digital twin adjusts the supply of energy to one or more consumption points based on a probability of a shortage of available energy at the one or more consumption points and a consequence of the shortage of available energy at the one or more consumption points.

[0176] In some embodiments, the AI-based platform technology described herein is further configured such that the digital twin determines the supply of energy to one or more consumption points based on a comparison of the availability of energy in each of two or more energy sources, the comparison including one or more of: a current and / or future amount of energy stored by at least one of the two or more energy sources; a current and / or future resource expenditure associated with the acquisition, storage, and / or supply of energy by at least one of the two or more energy sources; or a current and / or future demand by other energy consumers for energy from at least one of the two or more energy sources.

[0177] In some aspects, the technology described herein relating to the AI-based platform is further configured such that the digital twin records, in the distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events including one or more of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0178] In some aspects, the technology described herein relating to an AI-based platform involves deploying a digital twin in an off-grid environment, the off-grid environment including one or more of an off-grid energy generation system, an off-grid energy storage system, and an off-grid energy mobilization system.

[0179] In some embodiments, the technology described herein relating to an AI-based platform is configured such that the AI-based platform measures the performance of the digital twin based on a prediction delta, where the prediction delta is based on a comparison of a prediction generated by the digital twin based on a set of energy demand parameters to measurements in a data collection system that correspond to the prediction.

[0180] In some aspects, the techniques described herein relating to an AI-based platform are configured such that the AI-based platform updates the digital twin based on the prediction delta, the updating including one or more of: retraining the digital twin based on the prediction delta; adjusting prediction corrections applied to predictions of the digital twin based on the prediction delta; supplementing the digital twin with at least one other trained machine learning model; or replacing the digital twin with an alternative digital twin.

[0181] In some embodiments, the technology described herein relating to an AI-based platform is configured such that the digital twin is further configured to generate a forecast based on at least one of the energy demand parameters and a representation of the impact of the at least one of the energy demand parameters on the forecast.

[0182] In some aspects, the AI-based platform technology described herein is further configured for the digital twin to determine one or more modifications to the set of energy demand parameters to improve future predictions of the digital twin, the one or more modifications including one or more additional historical, current, and / or forecast energy demand parameters associated with the set of fixed entities and the set of mobile entities within the defined domain, or one or more modifications to one or more historical, current, and / or forecast energy demand parameters associated with the set of fixed entities and the set of mobile entities within the defined domain.

[0183] In some aspects, the technology described herein relating to the AI-based platform is further configured such that the digital twin orchestrates the supply of energy to one or more consumption points based on one or more entity parameters received from at least one entity of the set of fixed entities and / or the set of mobile entities within the defined domain, the one or more entity parameters including one or more of a current and / or future energy state of the at least one entity, a current and / or future energy consumption by the at least one entity, or a current and / or future activity performed by the at least one entity related to the energy consumption.

[0184] In some aspects, the technology described herein relating to an AI-based platform is further configured such that the digital twin is configured to send a request to at least one entity of the set of fixed entities and / or the set of mobile entities within the defined domain to adjust one or more entity parameters associated with the at least one entity, wherein the one or more entity parameters include one or more of: a current and / or future energy state of the at least one entity; a current and / or future energy consumption by the at least one entity; or a current and / or future activity performed by the at least one entity associated with the energy consumption.

[0185] In some aspects, the technology described herein relating to the AI-based platform is further configured such that the digital twin is further configured to perform a simulation of at least one process of at least one physical machine associated with one or both of the set of fixed entities or the set of mobile entities, and output, based on the simulation, at least one energy demand parameter resulting from the at least one process.

[0186] In some aspects, the technology described herein relating to an AI-based platform includes a digital twin associated with at least one physical machine associated with one or both 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 process outputs corresponding to updated detections of process outputs performed by the at least one physical machine.

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

[0188] In some aspects, the technology described herein relates to an AI-based platform that enables intelligent orchestration and management of power and energy, including a set of modular, distributed energy systems that are configurable based on local demand requirements.

[0189] In some embodiments, the technologies described herein relating to an AI-based platform predict local demand requirements by a demand forecasting algorithm running on a set of edge networking devices that are linked to a set of energy consuming systems.

[0190] In some embodiments, the technology described herein relating to an AI-based platform configures at least one of a set of modular distributed energy systems to be located by the AI-based platform in proximity to the location and time of demand.

[0191] In some embodiments, the technology described herein relating to an AI-based platform is configured such that at least one of a set of modular distributed energy systems is deployed by the AI-based platform based on the location and type of local demand requirements.

[0192] In some embodiments, the technology described herein relating to an AI-based platform configures at least one of a set of modular distributed energy systems to generate energy at the point of local demand by the AI-based platform.

[0193] In some embodiments, the technology described herein relating to an AI-based platform configures at least one of a set of modular distributed energy systems by the AI-based platform to supply modular power generation systems to locations of demand.

[0194] In some embodiments, the technology described herein relating to an AI-based platform includes at least one of a set of modular distributed energy systems configured by the AI-based platform to route the supply of energy by a set of energy supply facilities to locations of demand.

[0195] In some embodiments, the technology described herein relating to an AI-based platform includes at least one of a set of modular distributed energy systems orchestrated by the AI-based platform to store energy in proximity to the location and time of demand.

[0196] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one of the set of modular distributed energy systems adapts transport of data over the network and / or communication system, the adaptation being based on one or more of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0197] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing one or more of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facilities, a stakeholder's transportation system, market conditions, or energy use preferences.

[0198] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform one or more of: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; adjusting energy data; or generating a visual and / or analytical indication of energy consumption by one or more machines, one or more plants, or one or more vehicles in a vehicle fleet.

[0199] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one of the modular distributed energy systems performs one or more of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; analyzing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data;

[0200] In some aspects, the technologies described herein relating to an AI-based platform base the local demand requirements on one or more of one or more public data resources and one or more enterprise data resources, where the one or more public data resources include one or more of weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources, and the one or more enterprise data resources include 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 operating data.

[0201] In some embodiments, the technology described herein relating to an AI-based platform further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset that is based on one or more human tags and / or labels, one or more human interactions with the hardware and / or software system, one or more results, one or more AI-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0202] In some aspects, the technology described herein relating to an AI-based platform includes at least one of the modular distributed energy systems configured to orchestrate the delivery of energy to one or more points of consumption, the delivery of energy including one or more of one or more fixed transmission lines, one or more instances of wireless energy transmission, the delivery of one or more fuels, or the delivery of one or more stored energies.

[0203] In some embodiments, the technology described herein relating to an AI-based platform configures a first system of a modular distributed energy system to communicate with a second system of the modular distributed energy system to orchestrate the delivery of energy to one or more points of consumption by coordinating the generation, storage, supply, and / or consumption of energy by one or both of the first system or the second system.

[0204] In some aspects, the technology described herein relating to an AI-based platform is configured such that at least one of the modular distributed energy systems is configured to coordinate the delivery of energy to one or more points of consumption based on a carbon generation and / or emissions policy.

[0205] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one of the modular distributed energy systems records, in a distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events including one or more of an energy purchase and / or sale event, a service fee associated with an energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0206] In some aspects, the technology described herein relating to an AI-based platform involves deploying at least one of the modular distributed energy systems in an off-grid environment, the off-grid environment including one or more of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0207] In some embodiments, the technology described herein relating to an AI-based platform is associated with a digital twin of at least one of the modular distributed energy systems configured to model and / or predict one or more characteristics and / or operation of at least one of the modular distributed energy systems.

[0208] In some embodiments, the technology described herein relating to an AI-based platform enables a set of modular distributed energy systems to be configured to change the amount of reserved capacity to accommodate patterns of energy demand related to local demand requirements.

[0209] In some embodiments, the technology described herein relating to an AI-based platform enables a set of modular distributed energy systems to be configured to relocate energy supply and / or access resources based on measurements and / or predictions of local demand requirements.

[0210] In some embodiments, the technology described herein relating to an AI-based platform enables a set of modular distributed energy systems to be configured to alter the schedule of energy production based on measurements and / or forecasts of local demand requirements.

[0211] In some embodiments, the technology described herein relating to an AI-based platform is configurable for a set of modular distributed energy systems to change the allocation of resources associated with the set of modular distributed energy systems, the allocation being based on a subset of local demand requirements.

[0212] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, comprising an artificial intelligence system configured to perform an analysis of a pattern of energy associated with an operational process including a set of resources, the set of resources being at least partially independent from an electric grid, and further configured to output a set of operational parameters for providing for energy generation, storage, and / or consumption to enable the operational process, the set of operational parameters being based on the analysis.

[0213] In some embodiments, the technology described herein relating to an AI-based platform, wherein at least one operating parameter of the set of operating parameters is a generation output level of a distributed energy generation resource.

[0214] In some embodiments, the technology described herein relating to an AI-based platform includes a method for generating a distributed energy storage resource, wherein at least one operational parameter of the set of operational parameters is a target storage level of the distributed energy storage resource.

[0215] In some embodiments, the technology described herein relating to an AI-based platform is configured such that at least one operational parameter of the set of operational parameters is a timing of supply of a distributed energy supply resource.

[0216] In some aspects, the technologies described herein relating to an AI-based platform include the artificial intelligence system further configured to adapt the transport of data over the network and / or communication system, the adaptation being based on one or more of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0217] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing one or more of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facilities, a stakeholder's transportation system, market conditions, or energy use preferences.

[0218] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform one or more of: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; adjusting energy data; or generating a visual and / or analytical indication of energy consumption by one or more machines, one or more plants, or one or more vehicles in a vehicle fleet.

[0219] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the artificial intelligence system performs one or more of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; analyzing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data; routing and / or transporting the energy-related data; or maintaining security of the energy-related data.

[0220] In some aspects, the technologies described herein relating to an AI-based platform include: at least one of the operating parameters is based on one or more of one or more public data resources or one or more enterprise data resources; the one or more public data resources include one or more of weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; and the one or more enterprise data resources include 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 operating data.

[0221] In some embodiments, the technologies described herein relating to AI-based platforms involve an artificial intelligence system being trained based on a training dataset, the training dataset being based on one or more human tags and / or labels, one or more human interactions with hardware and / or software systems, 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.

[0222] In some aspects, the technology described herein relating to an AI-based platform includes an artificial intelligence system configured to orchestrate the delivery of energy to one or more points of consumption, the delivery of energy including one or more of one or more fixed transmission lines, one or more instances of wireless energy transmission, the delivery of one or more fuels, or the delivery of one or more stored energies.

[0223] In some embodiments, the technology described herein relating to an AI-based platform further configures the artificial intelligence system to record, in the distributed ledger and / or blockchain, one or more energy-related events, wherein the one or more energy-related events include one or more of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0224] In some aspects, the technology described herein relating to an AI-based platform involves an artificial intelligence system being deployed in an off-grid environment, the off-grid environment including one or more of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

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

[0226] In some embodiments, the technology described herein relating to an AI-based platform, wherein the artificial intelligence system is further configured to assess compliance of the operating process with one or both of a carbon generation and / or emissions policy or a set of labor standards associated with the operating process.

[0227] In some embodiments, the technology described herein relating to an AI-based platform, wherein the artificial intelligence system is further configured to adjust a set of operational parameters to provide for energy generation, storage, and / or consumption associated with the operational process based on one or both of a carbon generation and / or emissions policy or a set of labor standards associated with the operational process.

[0228] In some aspects, the technology described herein related to an AI-based platform is further configured such that the artificial intelligence system sends a message to at least one edge device of the set of edge devices associated with the operational process, the message including a request to adjust at least one operation of the at least one edge device based on the set of operational parameters.

[0229] In some aspects, the technology described herein relating to an AI-based platform is further configured such that the artificial intelligence system receives, from at least one edge device of the set of edge devices associated with the operational process, an indication of a current and / or predicted energy state of the at least one edge device, and the set of operational parameters is based on the indication of the current and / or predicted energy state of the at least one edge device.

[0230] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the artificial intelligence system determines the set of operational parameters based on an output of a digital twin representing at least one edge device of the set of edge devices associated with the operational process, wherein the output of the digital twin indicates a current and / or predicted energy state of the at least one edge device.

[0231] In some embodiments, the technology described herein relating to an AI-based platform is further configured to orchestrate a set of modular distributed energy systems to generate, store, and / or supply energy, the orchestration being based on a set of operating parameters and local demand requirements.

[0232] In some embodiments, the technology described herein relating to an AI-based platform, wherein the analysis of energy patterns associated with an operational process includes an analysis of the availability of backup power sources that can be used in response to a failure of at least a portion of the electrical grid.

[0233] In some embodiments, the technology described herein relating to an AI-based platform includes analyzing patterns of energy associated with an operational process, wherein the analysis includes analyzing at least one auxiliary function associated with the set of resources, and the set of operational parameters includes at least one operational parameter associated with the at least one auxiliary function.

[0234] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a policy and governance engine configured to deploy a set of rules and / or policies that govern a set of energy generation, storage, and / or consumption workloads, the rules and / or policies being associated with the configuration of a set of edge devices operating in local data communication with a set of energy generation equipment, energy storage equipment, energy delivery equipment, or energy consumption systems.

[0235] In some aspects, the technology described herein relating to an AI-based platform, upon configuration in a policy and governance engine, a policy associated with an energy generation instruction is automatically applied by at least one of the edge devices to control energy generation by at least one energy generation system controlled via the edge devices.

[0236] In some aspects, the technology described herein relating to an AI-based platform, upon configuration in a policy and governance engine, automatically applies policies associated with energy consumption instructions by at least one of the edge devices to control energy consumption by at least one energy consuming system controlled via the edge device.

[0237] In some embodiments, the technology described herein relating to an AI-based platform, upon configuration in a policy and governance engine, automatically applies policies associated with energy supply instructions by at least one of the edge devices to control energy supply by at least one energy supply system controlled via the edge devices.

[0238] In some aspects, the technology described herein relating to an AI-based platform, upon configuration in a policy and governance engine, a policy associated with an energy storage instruction is automatically applied by at least one of the edge devices to control energy storage by at least one energy storage system controlled via the edge devices.

[0239] In some aspects, the technology described herein relating to an AI-based platform is configured such that the policy and governance engine operates on a set of stored policy templates to configure policies.

[0240] In some aspects, the techniques described herein relating to an AI-based platform automatically generate a set of recommended policies for presentation to a policy and governance engine based on a dataset of past policies, a dataset representing the operational state and / or configuration of a set of distributed energy resources, and a set of past results.

[0241] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the policy and governance engine adjusts the rules and / or policies based on at least one contextual factor, the at least one contextual factor comprising at least one of historical energy transaction data, at least one operational factor, at least one market factor, at least one expected market behavior, or at least one expected customer behavior.

[0242] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that the policy and governance engine adapts the transport of data over the network and / or communication system, the adaptation being based on at least one of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0243] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, a market condition, or an energy use preference.

[0244] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform at least one of providing a visual and / or analytical indication of energy consumption by at least one energy consumer, filtering the energy data, highlighting the energy data, or adjusting the energy data.

[0245] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of the at least one machine, the at least one plant, or the at least one vehicle in the vehicle fleet.

[0246] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the policy and governance engine performs at least one of extracting energy-related data, detecting and / or correcting errors in the energy-related data, transforming, converting, normalizing, and / or cleansing the energy-related data, parsing the energy-related data, detecting patterns, content, and / or objects in the energy-related data, compressing the energy-related data, streaming the energy-related data, filtering the energy-related data, loading and / or storing the energy-related data, routing and / or transporting the energy-related data, or maintaining security of the energy-related data.

[0247] In some aspects, the technology described herein relating to an AI-based platform includes at least one of the rules and / or policies based on at least one public data resource, wherein the at least one public data resource includes at least one of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0248] In some aspects, the technologies described herein relating to an AI-based platform include at least one of the rules and / or policies based on at least one enterprise data resource, the at least one enterprise data resource including at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0249] In some embodiments, the technology described herein relating to an AI-based platform further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset being based on at least one human tag and / or label, at least one human interaction with the hardware and / or software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0250] In some aspects, the technology described herein relating to an AI-based platform includes a policy and governance engine configured to orchestrate the delivery of energy to at least one point of consumption, the delivery of energy including at least one of at least one fixed transmission line, at least one instance of wireless energy transmission, the delivery of at least one fuel, or the delivery of at least one stored energy.

[0251] In some aspects, the technology described herein relating to the AI-based platform is further configured such that the policy and governance engine records, in the distributed ledger and / or blockchain, at least one energy-related event, the at least one energy-related event including at least one of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0252] In some aspects, the technology described herein relating to an AI-based platform includes a policy and governance engine located in an off-grid environment, the off-grid environment including at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0253] In some embodiments, the technology described herein relating to the AI-based platform is further configured such that the policy and governance engine generates and / or executes at least one smart contract, each of the at least one smart contract applying rules and / or policies to at least one energy-related transaction.

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

[0255] In some aspects, the technologies described herein relating to an AI-based platform are further configured for the policy and governance engine to deploy, to the set of edge devices, at least one instruction for adapting at least one operating parameter associated with at least one industrial machine and / or industrial process controlled by the set of edge devices.

[0256] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a set of edge devices configured to communicate with at least one energy generation facility, energy storage facility, and / or energy consumption system and automatically execute a set of pre-configured policies that govern the energy generation, energy storage, or energy consumption of the respective energy generation facility, energy storage facility, or energy consumption system.

[0257] In some embodiments, the technology described herein relating to an AI-based platform is a set of contextual policies where automatically executed policies are adjusted based on the current status of a set of energy generating entities within the energy grid.

[0258] In some embodiments, the technology described herein relating to an AI-based platform is a set of contextual policies that automatically execute policies that adjust based on the current state of a set of energy-generating entities in an energy generation environment, including an energy grid and a set of distributed energy resources that operate independently of the energy grid.

[0259] In some embodiments, the technology described herein relating to an AI-based platform is a set of contextual policies where automatically executed policies are adjusted based on the current state of a set of energy storage entities within an energy grid.

[0260] In some aspects, the technology described herein relating to an AI-based platform is a set of contextual policies that adjust 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, where the automatically executed policies are a set of contextual policies that adjust based on the current state of a set of energy delivery entities in the energy grid.

[0261] In some aspects, the technology described herein relating to an AI-based platform is a set of contextual policies that automatically execute policies that adjust 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.

[0262] In some embodiments, the technology described herein relating to an AI-based platform is a set of contextual policies that automatically execute policies that adjust based on the current state of a set of energy consuming entities that consume energy from an energy grid.

[0263] In some aspects, the technology described herein relating to an AI-based platform is a set of contextual policies that automatically execute policies that adjust based on the current state of a set of energy consuming entities that consume energy from an energy grid and from a set of distributed energy resources that operate independently of the energy grid.

[0264] In some aspects, the technology described herein relating to an AI-based platform is further configured such that the set of edge devices adjusts the set of pre-configured policies based on at least one contextual factor, the at least one contextual factor including at least one of historical energy transaction data, at least one operational factor, at least one market factor, at least one expected market behavior, or at least one expected customer behavior.

[0265] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that at least one of the edge devices adapts transport of data over the network and / or communication system, the adaptation being based on at least one of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0266] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, a market condition, or an energy use preference.

[0267] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform at least one of providing a visual and / or analytical indication of energy consumption by at least one energy consumer, filtering the energy data, highlighting the energy data, or adjusting the energy data.

[0268] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of the at least one machine, the at least one plant, or the at least one vehicle in the vehicle fleet.

[0269] In some embodiments, the techniques described herein relating to an AI-based platform are further configured such that at least one of the edge devices performs at least one of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; analyzing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data; routing and / or transporting the energy-related data; or maintaining security of the energy-related data.

[0270] In some aspects, the technology described herein relating to an AI-based platform includes at least one of the pre-configured policies based on at least one public data resource, the at least one public data resource including at least one of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0271] In some aspects, the technologies described herein relating to an AI-based platform include at least one of the pre-configured policies based on at least one enterprise data resource, the at least one enterprise data resource including at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0272] In some aspects, the technologies described herein relating to an AI-based platform include at least one of the edge devices including at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset being based on at least one human tag and / or label, at least one human interaction with the hardware and / or software system, at least one result, at least one AI-generated training data sample, at least one of a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0273] In some aspects, the technologies described herein relating to the AI-based platform further configure at least one of the edge devices to orchestrate delivery of energy to at least one point of consumption, the delivery of energy including at least one of at least one fixed transmission path, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy.

[0274] In some aspects, the technology described herein relating to the AI-based platform is further configured such that at least one of the edge devices records at least one energy-related event in the distributed ledger and / or blockchain, the at least one energy-related event including at least one of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0275] In some aspects, the technology described herein relating to an AI-based platform involves at least one of the edge devices being deployed in an off-grid environment, the off-grid environment including at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0276] In some aspects, the techniques described herein relating to an AI-based platform are further configured for the set of edge devices to determine at least one pattern of energy availability based on communication with at least one energy generation facility, energy storage facility, and / or energy consumption system, and to update execution of the set of pre-defined policies based on the at least one pattern.

[0277] In some embodiments, the technology described herein relating to an AI-based platform includes at least one edge device of a set of edge devices configured to manage operations of an industrial facility, wherein the set of pre-configured policies is based on at least one energy target associated with the industrial facility.

[0278] In some embodiments, the technology described herein relating to an AI-based platform includes at least one energy generation facility, energy storage facility, and / or energy consumption system located in a geographic region, and the set of pre-configured policies is based on at least one energy objective associated with the geographic region.

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

[0280] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a machine learning system trained on a set of energy intelligence data and deployed to an edge device, the machine learning system configured to undergo additional training by the edge device to improve energy management.

[0281] In some embodiments, the technology described herein relating to an AI-based platform, energy management includes managing the generation of energy by a set of distributed energy generation resources.

[0282] In some embodiments, the technology described herein relating to an AI-based platform, energy management includes managing the storage of energy through a set of distributed energy storage resources.

[0283] In some embodiments, the technology described herein relating to an AI-based platform, energy management includes managing the supply of energy by a set of distributed energy supply resources.

[0284] In some embodiments, the technology described herein relating to an AI-based platform, energy management includes managing the consumption of energy by a set of distributed energy-consuming resources.

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

[0286] In some aspects, the AI-based platform technologies described herein further configure the machine learning system to adapt the transport of data over the network and / or communication system, the adaptation being based on at least one of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0287] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, a market condition, or an energy use preference.

[0288] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform at least one of providing a visual and / or analytical indication of energy consumption by at least one energy consumer, filtering the energy data, highlighting the energy data, or adjusting the energy data.

[0289] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of the at least one machine, the at least one plant, or the at least one vehicle in the vehicle fleet.

[0290] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the machine learning system performs at least one of extracting energy-related data, detecting and / or correcting errors in the energy-related data, transforming, converting, normalizing, and / or cleansing the energy-related data, analyzing the energy-related data, detecting patterns, content, and / or objects in the energy-related data, compressing the energy-related data, streaming the energy-related data, filtering the energy-related data, loading and / or storing the energy-related data, routing and / or transporting the energy-related data, or maintaining security of the energy-related data.

[0291] In some aspects, the technology described herein relating to an AI-based platform includes the energy intelligence data being based on at least one public data resource, the at least one public data resource including at least one of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0292] In some aspects, the technology described herein relating to an AI-based platform includes the energy intelligence data being based on at least one enterprise data resource, the at least one enterprise data resource including at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0293] In some embodiments, the technologies described herein relating to an AI-based platform further train the machine learning system based on a training dataset, the training dataset being based on at least one of at least one human tag and / or label, at least one human interaction with the hardware and / or software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0294] In some aspects, the technology described herein relating to the AI-based platform includes the machine learning system further configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy including at least one of at least one fixed transmission line, at least one instance of wireless energy transmission, delivery of at least one fuel, or delivery of at least one stored energy.

[0295] In some aspects, the technology described herein relating to the AI-based platform is further configured such that the machine learning system records at least one energy-related event in the distributed ledger and / or blockchain, the at least one energy-related event including at least one of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0296] In some aspects, the technology described herein relating to an AI-based platform involves edge devices being deployed in an off-grid environment, the off-grid environment including at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0297] In some aspects, the technology described herein relating to an AI-based platform includes an edge device located in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

[0298] In some aspects, the technology described herein relating to an AI-based platform provides information regarding the energy status and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy, to an edge device.

[0299] In some aspects, the technologies described herein relating to an AI-based platform include an edge device that includes and / or manages at least one sensor of a set of sensors, the set of sensors being associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

[0300] In some aspects, the technology described herein relating to an AI-based platform includes an edge device associated with a circumstance and / or environment, the edge device further configured to perform additional training of the machine learning system in response to changes in the circumstance and / or environment.

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

[0302] In some embodiments, the techniques described herein for AI-based platforms provide for additional training based on the set of energy intelligence data on which the machine learning system was initially trained and additional energy intelligence data on which the machine learning system has not yet been trained.

[0303] In some embodiments, the techniques described herein relating to AI-based platforms, the additional training includes adding the machine learning system to an ensemble that includes at least one other artificial intelligence system.

[0304] In some embodiments, the technology described herein relating to an AI-based platform, wherein the set of energy intelligence data is based on at least one energy-related policy and / or rule, and the additional training is based on changes to the at least one energy-related policy and / or rule.

[0305] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, comprising a set of edge devices including an artificial intelligence system configured to process data handled by the edge devices, determine based on the data a combination of energy generation, storage, supply, and / or consumption characteristics for a set of systems in local communication with the edge devices, and output a dataset indicative of the constituent proportions of the combination.

[0306] In some embodiments, the technology described herein for an AI-based platform outputs a data set indicating the percentage of energy generated by the energy grid and the percentage of energy generated by a set of distributed energy resources operating independently from the energy grid.

[0307] In some embodiments, the technology described herein for an AI-based platform outputs a dataset indicating the percentage of energy generated by renewable energy resources and the percentage of energy generated by non-renewable resources.

[0308] In some embodiments, the technology described herein for an AI-based platform outputs a data set showing the rate of energy production by type for each interval in a series of time intervals.

[0309] In some embodiments, the technology described herein for an AI-based platform outputs a dataset showing the carbon production associated with energy production for each type of energy in the energy combination during each interval of a series of time intervals.

[0310] In some embodiments, the technology described herein for an AI-based platform outputs a dataset showing the carbon emissions associated with energy production for each type of energy in the energy combination during each interval of a series of time intervals.

[0311] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that at least one of the edge devices adapts transport of data over the network and / or communication system, the adaptation being based on at least one of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0312] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, a market condition, or an energy use preference.

[0313] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform at least one of providing a visual and / or analytical indication of energy consumption by at least one energy consumer, filtering the energy data, highlighting the energy data, or adjusting the energy data.

[0314] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of the at least one machine, the at least one plant, or the at least one vehicle in the vehicle fleet.

[0315] In some embodiments, the techniques described herein relating to an AI-based platform are further configured such that at least one of the edge devices performs at least one of extracting energy-related data, detecting and / or correcting errors in the energy-related data, transforming, converting, normalizing, and / or cleansing the energy-related data, analyzing the energy-related data, detecting patterns, content, and / or objects in the energy-related data, compressing the energy-related data, streaming the energy-related data, filtering the energy-related data, loading and / or storing the energy-related data, routing and / or transporting the energy-related data, or maintaining security of the energy-related data.

[0316] In some aspects, the technology described herein relating to an AI-based platform comprises the step of: based on the data at least one public data resource, the public data resource including at least one of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0317] In some aspects, the technologies described herein relating to an AI-based platform include data based on at least one enterprise data resource, the enterprise data resource including at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0318] In some aspects, the technologies described herein relating to an AI-based platform include at least one of the edge devices including at least one AI-based model and / or algorithm, and the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset being based on at least one human tag and / or label, at least one human interaction with the hardware and / or software system, at least one result, at least one AI-generated training data sample, at least one of a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0319] In some aspects, the technologies described herein relating to the AI-based platform further configure at least one of the edge devices to orchestrate delivery of energy to at least one point of consumption, the delivery of energy including at least one of at least one fixed transmission path, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy.

[0320] In some aspects, the technology described herein relating to the AI-based platform is further configured such that at least one of the edge devices records at least one energy-related event in the distributed ledger and / or blockchain, the at least one energy-related event including at least one of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0321] In some aspects, the technology described herein relating to an AI-based platform involves at least one of the edge devices being deployed in an off-grid environment, the off-grid environment including at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0322] In some aspects, the technology described herein relating to an AI-based platform includes a set of edge devices, at least some of which are located in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

[0323] In some aspects, the technology described herein relating to an AI-based platform provides information regarding the energy status and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy, where a set of edge devices.

[0324] In some aspects, the technologies described herein relating to an AI-based platform include a set of edge devices that include and / or manage at least one sensor of a set of sensors, the set of sensors being associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

[0325] In some embodiments, the techniques described herein relating to an AI-based platform base a combination of energy generation, storage, supply, and / or consumption characteristics on at least one energy demand requirement associated with a set of edge devices.

[0326] In some embodiments, the techniques described herein relating to an AI-based platform are based on prioritizing the collection, storage, transportation, and / or use of energy associated with each energy source associated with a set of edge devices based on a combination of energy generation, storage, supply, and / or consumption characteristics.

[0327] In some embodiments, the techniques described herein relating to an AI-based platform base a combination of energy generation, storage, supply, and / or consumption characteristics on storage, transportation, and / or usage schedules associated with each energy source associated with a set of edge devices.

[0328] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a data processing system configured to fuse at least one entity of a grid dataset of generation, storage, supply, or consumption of an energy grid entity with at least one entity of a dataset of generation, storage, supply, and / or consumption of an off-grid energy entity.

[0329] In some embodiments, the technology described herein relating to an AI-based platform is configured such that the data processing system automatically time-aligns the energy grid entity data with the off-grid energy entity data.

[0330] In some aspects, the technology described herein relating to an AI-based platform includes a data processing system configured to automatically collect off-grid energy entity sensor data from a set of edge devices through which a set of off-grid energy entities are controlled.

[0331] In some aspects, the techniques described herein relating to an AI-based platform are configured to automatically normalize the energy grid entity data and the off-grid energy entity data so that the data processing system presents the data according to a common set of units.

[0332] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that the data processing system adapts transport of data over the network and / or communication system, the adaptation being based on at least one of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0333] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, a market condition, or an energy use preference.

[0334] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform at least one of providing a visual and / or analytical indication of energy consumption by at least one energy consumer, filtering the energy data, highlighting the energy data, or adjusting the energy data.

[0335] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of the at least one machine, the at least one plant, or the at least one vehicle in the vehicle fleet.

[0336] In some embodiments, the techniques described herein relating to an AI-based platform are further configured such that the data processing system performs at least one of extracting energy-related data, detecting and / or correcting errors in the energy-related data, transforming, converting, normalizing, and / or cleansing the energy-related data, analyzing the energy-related data, detecting patterns, content, and / or objects in the energy-related data, compressing the energy-related data, streaming the energy-related data, filtering the energy-related data, loading and / or storing the energy-related data, routing and / or transporting the energy-related data, or maintaining security of the energy-related data.

[0337] In some embodiments, the technology described herein relating to an AI-based platform further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset being based on at least one human tag and / or label, at least one human interaction with the hardware and / or software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0338] In some aspects, the technology described herein relating to the AI-based platform includes the data processing system further configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy including at least one of at least one fixed transmission path, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy.

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

[0340] In some aspects, the technology described herein relating to an AI-based platform includes at least one entity of an off-grid energy generation, storage, and / or consumption dataset located in an off-grid environment, the off-grid environment including at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0341] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that the data processing system intelligently orchestrates and manages power and / or energy based on a dataset of energy generation, storage, and / or consumption data for the set of infrastructure assets, the dataset generated at least in part by a set of sensors included in and / or managed by the set of edge devices.

[0342] In some aspects, the technology described herein relating to an AI-based platform is further configured such that the data processing system manages at least one of: generation of energy by a set of distributed energy generation resources; storage of energy by a set of distributed energy storage resources; supply of energy by a set of distributed energy supply resources; or consumption of energy by a set of distributed energy consumption resources.

[0343] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that the data processing system intelligently orchestrates and manages power and / or energy for a set of entities, the set of entities including at least one of weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources, market data resources, or e-commerce data resources.

[0344] In some aspects, the technology described herein relating to an AI-based platform further comprises the data processing system being configured to execute at least one algorithm that performs a simulation of energy consumption by at least one of the entities, the simulation being based on a dataset including alternative state or event parameters of at least one of the entities that reflect alternative consumption scenarios, and the algorithm accessing a demand response model that describes how energy demand responds to changes in the price of energy or to changes in the price of an operation or activity in which energy is consumed.

[0345] In some aspects, the technology described herein relating to an AI-based platform includes a policy and governance engine configured in a data processing system to deploy a set of rules and / or policies to at least one edge device in local communication with at least one of the entities, the edge device configured to manage at least one of the entities based on the rules and / or policies.

[0346] In some aspects, the technology described herein relating to an AI-based platform includes a data processing system including an analytics system that represents operational parameters and a current state of at least one of the entities based on a set of sensed parameters, the set of sensed parameters being generated by a set of edge devices in proximity to at least one of the entities, and the analytics system configured to provide recommendations related to at least one of the entities or at least one additional available entity.

[0347] In some embodiments, the technology described herein relating to an AI-based platform includes an artificial intelligence system, wherein a data processing system is trained with a historical data set related to energy generation, storage, and / or utilization of an operating process associated with at least one of the entities, and the data processing system is further configured to analyze energy patterns of the operating process and output a prediction of the energy requirements of the operating process based on current conditions and / or information related to at least one of the entities.

[0348] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that the data processing system fuses at least one entity of the backup and / or auxiliary energy generation, storage, supply, or consumption grid dataset with the energy grid entity generation, storage, supply, or consumption grid dataset and the off-grid energy entity generation, storage, supply, and / or consumption grid dataset.

[0349] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that the data processing system coordinates the development of energy grid resources and / or off-grid energy resources based on a fusion of the energy grid entity's generation, storage, supply, or consumption grid dataset and the off-grid energy entity's generation, storage, supply, and / or consumption dataset.

[0350] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a set of autonomous orchestration systems for improving the delivery of a heterogeneous set of energy types to consumption points based on the location of the consumption points and a set of consumption attributes, the consumption attributes including at least one of peak power requirements at the consumption points, continuity of power demand at the consumption points, and types of energy available at the consumption points.

[0351] In some aspects, the technology described herein relates to an AI-based platform in which a set of autonomous orchestration systems orchestrate the delivery of defined types of energy generation capacity to points of consumption.

[0352] In some embodiments, the technology described herein relating to an AI-based platform allows a set of autonomous orchestration systems to orchestrate the provision of defined types of energy storage capacity to points of consumption.

[0353] In some embodiments, the techniques described herein relating to an AI-based platform determine the type of energy that can be used based, at least in part, on a set of operational suitability parameters.

[0354] In some embodiments, the technology described herein relating to an AI-based platform determines the type of energy that can be used based, at least in part, on a set of governance parameters.

[0355] In some embodiments, the technology described herein relating to an AI-based platform involves a set of governance parameters relating to the utilization of renewable energy resources.

[0356] In some aspects, the technology described herein relating to an AI-based platform is such that the set of governance parameters relate to carbon generation or emissions.

[0357] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that at least one of the set of autonomous edge orchestration systems adapts transport of data over the network and / or communication system, the adaptation being based on at least one of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0358] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, a market condition, or an energy use preference.

[0359] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform at least one of providing a visual and / or analytical indication of energy consumption by at least one energy consumer, filtering the energy data, highlighting the energy data, or adjusting the energy data.

[0360] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of the at least one machine, the at least one plant, or the at least one vehicle in the vehicle fleet.

[0361] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that at least one of the set of autonomous orchestration systems performs at least one of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; analyzing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data; routing and / or transporting the energy-related data; or maintaining security of the energy-related data.

[0362] In some embodiments, the technology described herein relating to an AI-based platform includes at least one of the consumption attributes based on at least one public data resource, the public data resource including at least one of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0363] In some aspects, the technology described herein relating to an AI-based platform includes at least one of the consumption attributes based on at least one enterprise data resource, the enterprise data resource including at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0364] In some embodiments, the technology described herein relating to an AI-based platform further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset being based on at least one human tag and / or label, at least one human interaction with the hardware and / or software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0365] In some aspects, the technology described herein relating to an AI-based platform is further configured such that at least one of the set of autonomous orchestration systems orchestrates delivery of energy to at least one point of consumption, the delivery of energy including at least one of at least one fixed transmission line, at least one instance of wireless energy transmission, delivery of at least one fuel, or delivery of at least one stored energy.

[0366] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that at least one of the set of autonomous orchestration systems records at least one energy-related event in the distributed ledger and / or blockchain, the at least one energy-related event comprising at least one of an energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0367] In some aspects, the technology described herein relating to an AI-based platform involves deploying at least one of a set of autonomous orchestration systems in an off-grid environment, the off-grid environment including at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0368] In some aspects, the technology described herein relating to an AI-based platform is further configured such that the set of autonomous orchestration systems determines the supply of a heterogeneous set of energy types based on a set of rules and / or policies governing a set of energy generation, storage, and / or consumption workloads, the rules and / or policies being associated with a configuration of a set of edge devices operating in local data communication with the set of energy generation equipment, energy storage equipment, energy supply equipment, or energy consumption systems.

[0369] In some aspects, the technology described herein relating to an AI-based platform is further configured such that the set of autonomous orchestration systems determines the supply of the heterogeneous set of energy types based on a simulation of energy consumption by at least one energy consumer, the simulation being based on a dataset including at least one alternative state or event parameter of the at least one energy consumer reflecting alternative consumption scenarios, and the simulation being based on a demand response model that describes how energy demand responds to changes in the price of energy or the price of the operation or activity in which the energy is consumed.

[0370] In some embodiments, the technology described herein relating to an AI-based platform improves the delivery of a heterogeneous set of energy types to points of consumption by a set of autonomous orchestration systems matching each of the heterogeneous set of energy types with at least one consumer associated with the point of consumption.

[0371] In some embodiments, the technology described herein relating to an AI-based platform improves the supply of a heterogeneous set of energy types to a point of consumption by a set of autonomous orchestration systems determining the deployment of additional energy sources of one or more energy types, where the deployment is based on a forecast of energy demand requirements associated with the point of consumption.

[0372] In some embodiments, the technology described herein relating to an AI-based platform improves the supply of a heterogeneous set of energy types to a point of consumption by a set of autonomous orchestration systems comparing characteristics of energy demand associated with the point of consumption with characteristics of each energy type in the heterogeneous set of energy types.

[0373] In some embodiments, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including an intelligent agent trained based on a dataset of experts' interactions with energy delivery systems, wherein the intelligent agent is trained to generate at least one recommendation and / or instruction regarding optimization of at least one energy objective and at least one other objective.

[0374] In some aspects, the techniques described herein relate to AI-based platforms, among other objectives, to operational objectives of an enterprise.

[0375] In some embodiments, the technology described herein for an AI-based platform operates in such a way that an intelligent agent acts based on status data from a set of edge devices over which a set of energy generating resources are controlled.

[0376] In some embodiments, the technology described herein for an AI-based platform operates in such a way that an intelligent agent operates based on status data from a set of edge devices over which a set of energy-consuming resources are controlled.

[0377] In some embodiments, the technology described herein for an AI-based platform operates in such a way that an intelligent agent acts based on status data from a set of edge devices over which a set of energy storage resources are controlled.

[0378] In some embodiments, the technology described herein for an AI-based platform operates in such a way that an intelligent agent acts based on status data from a set of edge devices over which a set of energy supply resources are controlled.

[0379] In some aspects, the AI-based platform technology described herein is further configured for the intelligent agent to adapt the transport of data over the network and / or communication system, the adaptation being based on at least one of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0380] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, a market condition, or an energy use preference.

[0381] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform at least one of providing a visual and / or analytical indication of energy consumption by at least one energy consumer, filtering the energy data, highlighting the energy data, or adjusting the energy data.

[0382] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of the at least one machine, the at least one plant, or the at least one vehicle in the vehicle fleet.

[0383] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the intelligent agent performs at least one of extracting energy-related data, detecting and / or correcting errors in the energy-related data, transforming, converting, normalizing, and / or cleansing the energy-related data, analyzing the energy-related data, detecting patterns, content, and / or objects in the energy-related data, compressing the energy-related data, streaming the energy-related data, filtering the energy-related data, loading and / or storing the energy-related data, routing and / or transporting the energy-related data, or maintaining security of the energy-related data.

[0384] In some aspects, the technologies described herein relating to an AI-based platform include a dataset based on at least one public data resource, the public data resource including at least one of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0385] In some aspects, the technologies described herein relating to an AI-based platform include a dataset based on at least one enterprise data resource, the enterprise data resource including at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0386] In some aspects, the techniques described herein relating to an AI-based platform involve an intelligent agent being trained based on a training dataset, the training dataset being based on at least one human tag and / or label, at least one human interaction with a hardware and / or software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0387] In some aspects, the technology described herein relating to the AI-based platform includes the intelligent agent further configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy including at least one of at least one fixed transmission line, at least one instance of wireless energy transmission, delivery of at least one fuel, or delivery of at least one stored energy.

[0388] In some aspects, the technology described herein relating to the AI-based platform is further configured such that the intelligent agent records at least one energy-related event in the distributed ledger and / or blockchain, the at least one energy-related event including at least one of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0389] In some aspects, the technology described herein relating to an AI-based platform involves an intelligent agent being deployed in an off-grid environment, the off-grid environment including at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0390] In some aspects, the technology described herein relating to an AI-based platform places an intelligent agent in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

[0391] In some embodiments, the technology described herein relating to an AI-based platform provides an intelligent agent with information regarding the energy state and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

[0392] In some aspects, the technology described herein relating to an AI-based platform includes an intelligent agent managing at least one sensor of a set of sensors, the set of sensors being associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

[0393] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the intelligent agent manages at least one processing task associated with the at least one device, and the at least one recommendation and / or instruction includes adjusting the at least one processing task based on at least one energy objective and / or at least one other objective.

[0394] In some embodiments, the AI-based platform technology described herein is further configured such that the intelligent agent migrates between the at least two devices and, while resident on each of the at least two devices, applies at least one recommendation and / or instruction to the device on which the intelligent agent resides.

[0395] In some embodiments, the technology described herein relating to an AI-based platform further configures the intelligent agent to exchange information with at least one other intelligent agent, the information being based on at least one recommendation and / or instruction, or one or both of the at least one energy objective and / or at least one other objective.

[0396] In some embodiments, the technology described herein relating to an AI-based platform, wherein the recommendations and / or instructions are associated with at least one device, and the intelligent agent is further configured to exchange collected and / or determined data associated with the at least one device with at least one other intelligent agent.

[0397] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including an artificial intelligence system trained on a set of energy generation, energy storage, energy supply, and / or energy consumption outcomes, wherein the artificial intelligence system is configured to analyze a dataset of current energy generation, current energy storage, current energy supply, and / or current energy consumption information and provide recommendations including at least one operational parameter that meets both mobile energy needs or fixed location energy needs in a predetermined domain.

[0398] In some embodiments, the technology described herein relating to an AI-based platform, the predetermined domain comprises a predetermined geographic location and a predetermined time period.

[0399] In some embodiments, the technology described herein relating to an AI-based platform, wherein at least one operating parameter indicates production instructions for a set of energy-generating resources.

[0400] In some embodiments, the technology described herein relating to an AI-based platform, wherein at least one operating parameter indicates storage instructions for a set of energy storage resources.

[0401] In some embodiments, the technology described herein relating to an AI-based platform, wherein at least one operating parameter indicates a supply instruction for a set of energy supply resources.

[0402] In some embodiments, the technology described herein relating to an AI-based platform includes at least one operational parameter indicating consumption instructions for a set of entities that consume energy.

[0403] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that the artificial intelligence system adapts the transport of data over the network and / or communication system, the adaptation being based on at least one of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0404] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, a market condition, or an energy use preference.

[0405] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform at least one of providing a visual and / or analytical indication of energy consumption by at least one energy consumer, filtering the energy data, highlighting the energy data, or adjusting the energy data.

[0406] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of the at least one machine, the at least one plant, or the at least one vehicle in the vehicle fleet.

[0407] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the artificial intelligence system performs at least one of extracting energy-related data, detecting and / or correcting errors in the energy-related data, transforming, converting, normalizing, and / or cleansing the energy-related data, analyzing the energy-related data, detecting patterns, content, and / or objects in the energy-related data, compressing the energy-related data, streaming the energy-related data, filtering the energy-related data, loading and / or storing the energy-related data, routing and / or transporting the energy-related data, or maintaining security of the energy-related data.

[0408] In some aspects, the technologies described herein relating to an AI-based platform include the dataset being based on at least one public data resource, the at least one public data resource including at least one of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0409] In some aspects, the technologies described herein relating to an AI-based platform include a dataset based on at least one enterprise data resource, the at least one enterprise data resource including at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0410] In some aspects, the technologies described herein relating to an AI-based platform include an artificial intelligence system trained based on a training dataset, the training dataset being based on at least one human tag and / or label, at least one human interaction with a hardware and / or software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0411] In some aspects, the technology described herein relating to an AI-based platform includes the artificial intelligence system further configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy including at least one of at least one fixed transmission line, at least one instance of wireless energy transmission, delivery of at least one fuel, or delivery of at least one stored energy.

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

[0413] In some aspects, the technology described herein relating to an AI-based platform involves an artificial intelligence system being deployed in an off-grid environment, the off-grid environment including at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0414] In some embodiments, the technology described herein relating to an AI-based platform places an artificial intelligence system in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

[0415] In some embodiments, the technology described herein relating to an AI-based platform involves an artificial intelligence system providing information regarding the energy state and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

[0416] In some aspects, the technologies described herein relating to an AI-based platform include an artificial intelligence system controlling at least one sensor of a set of sensors, the set of sensors being associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

[0417] In some embodiments, the technology described herein relating to an AI-based platform, a predetermined domain includes at least one boundary, and the dataset is restricted based on at least one boundary associated with the predetermined domain.

[0418] In some embodiments, the technology described herein relating to an AI-based platform bases recommendations on at least one constraint associated with at least one operational parameter, and the artificial intelligence system is trained to analyze the dataset based on the at least one constraint.

[0419] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including an artificial intelligence system configured to analyze a dataset of monitored local conditions and generate a recommended configuration of at least one distributed system of a set of distributed systems, each distributed system of the set of distributed systems configurable to both produce energy and consume energy, the configuration causing the at least one distributed system to produce and / or consume energy based on the monitored local conditions.

[0420] In some aspects, the technology described herein relating to an AI-based platform comprises an artificial intelligence system configuring a plurality of distributed systems of a set of distributed systems such that a set of aggregate performance requirements are met across the plurality of distributed systems.

[0421] In some embodiments, the technology described herein relating to an AI-based platform, the aggregated performance requirements are a set of economic performance requirements.

[0422] In some embodiments, the technology described herein relating to an AI-based platform, the aggregated performance requirements are a set of regulatory performance requirements.

[0423] In some aspects, the techniques described herein for AI-based platforms are where the aggregate performance requirement is with respect to carbon production or emissions.

[0424] In some embodiments, the technology described herein relating to an AI-based platform is such that the aggregated performance requirements are a set of consumption requirements.

[0425] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that the artificial intelligence system adapts the transport of data over the network and / or communication system, the adaptation being based on at least one of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0426] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, a market condition, or an energy use preference.

[0427] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform at least one of providing a visual and / or analytical indication of energy consumption by at least one energy consumer, filtering the energy data, highlighting the energy data, or adjusting the energy data.

[0428] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of the at least one machine, the at least one plant, or the at least one vehicle in the vehicle fleet.

[0429] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the artificial intelligence system performs at least one of extracting energy-related data, detecting and / or correcting errors in the energy-related data, transforming, converting, normalizing, and / or cleansing the energy-related data, analyzing the energy-related data, detecting patterns, content, and / or objects in the energy-related data, compressing the energy-related data, streaming the energy-related data, filtering the energy-related data, loading and / or storing the energy-related data, routing and / or transporting the energy-related data, or maintaining security of the energy-related data.

[0430] In some aspects, the technologies described herein relating to an AI-based platform include a dataset based on at least one public data resource, the public data resource including at least one of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0431] In some aspects, the technologies described herein relating to an AI-based platform include a dataset based on at least one enterprise data resource, the enterprise data resource including at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0432] In some aspects, the technology described herein relating to an AI-based platform includes the artificial intelligence system further configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy including at least one of at least one fixed transmission line, at least one instance of wireless energy transmission, delivery of at least one fuel, or delivery of at least one stored energy.

[0433] In some aspects, the technology described herein relating to an AI-based platform, wherein the artificial intelligence system is further configured to record at least one energy-related event in the distributed ledger and / or blockchain, the at least one energy-related event including at least one of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0434] In some aspects, the technology described herein relating to an AI-based platform involves an artificial intelligence system being deployed in an off-grid environment, the off-grid environment including at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0435] In some aspects, the technologies described herein relating to an AI-based platform include an artificial intelligence system trained based on a training dataset, the training dataset being based on at least one human tag and / or label, at least one human interaction with a hardware and / or software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0436] In some embodiments, the technology described herein relating to an AI-based platform places an artificial intelligence system in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

[0437] In some embodiments, the technology described herein relating to an AI-based platform involves an artificial intelligence system providing information regarding the energy status and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

[0438] In some aspects, the technologies described herein relating to an AI-based platform include an artificial intelligence system controlling at least one sensor of a set of sensors, the set of sensors being associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

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

[0440] In some embodiments, the techniques described herein relating to an AI-based platform base the recommended configuration on at least one of the current and / or predicted location of at least one distributed system in the set of distributed systems, or the current and / or predicted location of at least one energy resource associated with the set of distributed systems.

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

[0442] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, comprising a set of adaptive and autonomous data processing systems for energy data collection and transmission from a set of edge networking devices over which a set of distributed energy entities are controlled, the data processing systems being trained based on a training dataset to recognize a set of events and / or signals indicative of an energy pattern of at least one of the set of distributed energy entities.

[0443] In some embodiments, the technology described herein relating to an AI-based platform includes a set of distributed energy entities including at least one energy generation resource.

[0444] In some embodiments, the technology described herein relating to an AI-based platform includes a set of distributed energy entities including at least one energy consuming entity.

[0445] In some embodiments, the technology described herein relating to an AI-based platform includes a set of distributed energy entities including at least one energy storage resource.

[0446] In some embodiments, the technology described herein relating to an AI-based platform includes a set of distributed energy entities including at least one energy supply resource.

[0447] In some embodiments, the technology described herein relating to an AI-based platform includes a training data set that includes historical energy generation data for a set of entities similar to the entities controlled via edge networking devices.

[0448] In some embodiments, the technology described herein relating to an AI-based platform includes a training dataset that includes historical energy consumption data for a set of entities similar to the entities controlled via edge networking devices.

[0449] In some embodiments, the technology described herein relating to an AI-based platform includes a training dataset that includes historical energy delivery data for a set of entities similar to the entities controlled via edge networking devices.

[0450] In some embodiments, the technology described herein relating to an AI-based platform includes a training data set that includes historical energy storage data for a set of entities similar to the entities controlled via edge networking devices.

[0451] In some aspects, the AI-based platform technology described herein further comprises at least one of the adaptive and autonomous data processing systems adapted to adapt the transport of data over the network and / or communication system, the adaptation being based on at least one of a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality of service (QoS) condition, a usage condition, a market factor condition, or a user-set condition.

[0452] In some aspects, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin representing at least one of an energy stakeholder entity, an energy distribution resource, a stakeholder's information technology, a networking infrastructure entity, an energy-dependent stakeholder's production facility, a stakeholder's transportation system, a market condition, or an energy use preference.

[0453] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to perform at least one of providing a visual and / or analytical indication of energy consumption by at least one energy consumer, filtering the energy data, highlighting the energy data, or adjusting the energy data.

[0454] In some embodiments, the technology described herein relating to an AI-based platform further includes an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of the at least one machine, the at least one plant, or the at least one vehicle in the vehicle fleet.

[0455] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one of the adaptive and autonomous data processing systems performs at least one of: extracting energy-related data; detecting and / or correcting errors in the energy-related data; transforming, converting, normalizing, and / or cleansing the energy-related data; analyzing the energy-related data; detecting patterns, content, and / or objects in the energy-related data; compressing the energy-related data; streaming the energy-related data; filtering the energy-related data; loading and / or storing the energy-related data;

[0456] In some aspects, the technology described herein relating to an AI-based platform includes the energy edge set being based on at least one public data resource, the public data resource including at least one of a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an e-commerce data resource.

[0457] In some aspects, the technology described herein relating to an AI-based platform includes the energy edge set based on at least one enterprise data resource, the at least one enterprise data resource including at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0458] In some embodiments, the technology described herein relating to an AI-based platform further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset being based on at least one human tag and / or label, at least one human interaction with the hardware and / or software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0459] In some aspects, the technologies described herein relating to the AI-based platform further include at least one of the adaptive and autonomous data processing systems being configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy including at least one of at least one fixed transmission path, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy.

[0460] In some embodiments, the technologies described herein relating to the AI-based platform further include at least one of the adaptive and autonomous data processing systems being further configured to record at least one energy-related event in the distributed ledger and / or blockchain, the at least one energy-related event including at least one of an energy purchase and / or sale event, a service fee associated with the energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission generation event, a carbon emission reduction event, a renewable energy credit event, a pollution generation event, or a pollution reduction event.

[0461] In some aspects, the technology described herein relating to an AI-based platform includes at least one of the adaptive and autonomous data processing systems deployed in an off-grid environment, the off-grid environment including at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0462] In some embodiments, the techniques described herein relating to an AI-based platform are further configured such that the set of adaptive and autonomous data processing systems performs additional training of the data processing systems based on the initial set of energy intelligence data on which the data processing systems were initially trained and additional energy intelligence data on which the data processing systems have not yet been trained.

[0463] In some aspects, the techniques described herein relating to an AI-based platform are further configured such that the set of adaptive and autonomous data processing systems instruct at least one edge networking device of the set of edge networking devices to adjust an operating parameter associated with the set of distributed energy entities based on recognition of an event and / or signal from the set of events and / or signals.

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

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

[0466] In some embodiments, the technology described herein for an AI-based platform vectorizes data collected from at least one internal edge device or at least one external edge device.

[0467] In some embodiments, the technology described herein for an AI-based platform stores data collected from at least one internal edge device or at least one external edge device in a distributed database.

[0468] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the data integration module determines an energy pattern based on a local energy pattern associated with the data collected from the at least one internal edge device and the at least one external edge device.

[0469] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a digital dynamic twin configured to model at least one of historical energy demand, current historical energy demand, or forecast energy demand, and an AI-based digital twin updater that updates the dynamic digital twin based on a set of energy parameters.

[0470] In some embodiments, the technology described herein relating to an AI-based platform includes an AI-based digital twin updater that performs dynamic digital twin updates to determine a forecast of energy demand for a future period, the update being based on a forecast of energy demand for the future period by another AI model.

[0471] In some embodiments, the technology described herein relating to an AI-based platform associates a dynamic digital twin with a device type, and an AI-based digital twin updater analyzes data associated with energy consumption by devices of the device type to update the dynamic digital twin to model energy consumption by devices of the device type.

[0472] In some embodiments, the technology described herein relating to an AI-based platform is further configured to model energy demand by at least one entity, the modeling being based on data indicative of energy consumption by the at least one entity.

[0473] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including an energy access arbitrator that arbitrates access to at least one energy source by at least one energy consuming device of the set of energy consuming devices among a set of energy consuming devices.

[0474] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, including a set of edge devices in local communication with at least one energy consuming device to identify at least one characteristic of energy consumption by the at least one energy consuming device, wherein at least one edge device of the set of edge devices identifies the at least one characteristic of energy consumption by the at least one energy consuming device based on a plurality of perspectives related to energy consumption by the at least one energy consuming device.

[0475] In some embodiments, the technology described herein relating to the AI-based platform further includes an edge device monitoring system that monitors energy consumption by at least one downstream device of the at least one energy consuming device and enforces an energy policy on the at least one downstream device based on the energy consumption.

[0476] In some embodiments, the technology described herein for an AI-based platform bases the energy policy on the generation mechanism by which energy is generated relative to energy consumption.

[0477] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the edge device monitoring system determines carbon emissions associated with energy consumption by the at least one downstream device.

[0478] In some aspects, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, comprising a set of artificial general intelligence (AGI) agents, each AGI agent assigned to manage a set of energy generation, storage, and / or consumption workloads by a set of entities.

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

[0480] In some aspects, the techniques described herein relating to an AI-based platform include at least one AGI agent of a set of AGI agents monitoring a decision by at least one other AGI agent of the set of AGI agents and adjusting at least one parameter associated with the AI-based platform based on the decision by the at least one other AGI agent.

[0481] In some aspects, the techniques described herein relating to an AI-based platform include at least one AGI agent of a set of AGI agents monitoring energy-related data related to at least one of: at least one interaction between at least one human and at least one component of the AI-based platform; at least one pattern of wildlife usage; 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 cyber-attack associated with at least one energy resource; at least one land remediation operation; at least one AI entity; or at least one robotic entity.

[0482] In some aspects, the technology described herein relating to an AI-based platform includes at least one AGI agent of the set of AGI agents performing adjustments on data associated with at least one of a data collection process, a data storage process, a data reporting process, or a data transmission process, where the adjustments are made based on at least one of an anonymity request by an individual associated with the data or a privacy request by an individual associated with the data.

[0483] In some embodiments, the technology described herein for an AI-based platform includes at least one AGI agent of a set of AGI agents monitoring the behavior of at least one energy resource in a networked element and updating a policy associated with the at least one energy resource based on the behavior.

[0484] In some embodiments, the technology described herein for an AI-based platform includes at least one AGI agent of a set of AGI agents updating an energy allocation to promote energy availability to at least one energy resource in response to movement. [Brief explanation of the drawings]

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

[0486] [Figure 1] FIG. 1 is a schematic diagram illustrating an introduction to the platform and key elements, according to some embodiments.

[0487] [Figure 2A] 2A and 2B are schematic diagrams illustrating an introduction to the major subsystems of the major ecosystem, according to some embodiments. [Figure 2B]2A and 2B are schematic diagrams illustrating an introduction to the major subsystems of the major ecosystem, according to some embodiments.

[0488] [Figure 3] FIG. 3 is a schematic diagram illustrating more details of a distributed energy generation system according to some embodiments.

[0489] [Figure 4] FIG. 4 is a schematic diagram illustrating details of data resources, according to some embodiments.

[0490] [Figure 5] FIG. 5 is a schematic diagram showing more details of configured Energy Edge participants, according to some embodiments.

[0491] [Figure 6] FIG. 6 is a schematic diagram showing more details of an intelligence enabling system according to some embodiments.

[0492] [Figure 7] FIG. 7 is a schematic diagram illustrating more details of AI-based energy orchestration, according to some embodiments.

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

[0494] [Figure 9] FIG. 9 is a schematic diagram illustrating the dual-process learning functionality of a dual-process artificial neural network, according to some embodiments.

[0495] [Figure 10]10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 11] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 12] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 13]10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 14] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 15] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 16]10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 17] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 18] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 19]10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 20] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 21] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 22]10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 23] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 24] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 25]10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 26] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 27] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 28]10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 29] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 30] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 31]10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 32] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 33] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 34]10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 35] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 36] 10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure. [Figure 37]10-37 are schematic diagrams of embodiments of neural net systems that may be connected to, integrated into, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organization, machine learning, artificial intelligence, and neural net systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, in accordance with embodiments of the present disclosure.

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

[0497] [Figure 39] FIG. 39 is a diagram illustrating quantum computing service request processing according to some embodiments of the present disclosure.

[0498] [Figure 40] FIG. 40 is a perspective view of the thalamic services according to the present disclosure and how they are coordinated within modules.

[0499] [Figure 41] FIG. 41 is another perspective view showing thalamic services according to the present disclosure and how they coordinate within modules. DETAILED DESCRIPTION OF THE INVENTION

[0500] Figure 1: Introduction to the platform and key elements In embodiments, provided herein is an AI-based energy edge platform, sometimes referred to herein for convenience simply as platform 102, that includes a set of intelligent, sometimes autonomous or semi-autonomous, systems, subsystems, applications, processes, methods, modules, services, layers, devices, components, machines, products, subsystems, interfaces, connections, and other elements that work together to enable intelligent, sometimes autonomous or semi-autonomous, connections and other elements that work together to enable the orchestration and management of power and energy in various ecosystems and environments, including distributed entities (sometimes referred to herein as “distributed energy resources” or “DERs”) and other energy resources and systems that generate, store, consume, and / or transport energy, and including IoT, edge, and other devices and systems that can be used to process data related to DERs and other energy resources and to inform, analyze, control, optimize, predict, and otherwise assist in the orchestration of distributed energy resources and other energy resources.

[0501] By way of example, distributed energy resources (“DERs”) include (but are not limited to): wind turbines (including wind turbine farms), photovoltaic (PV), flexible and / or floating solar energy systems (including solar energy farms), fuel cells (including natural gas-fired fuel cells and biomass-fired fuel cells), coal mines, oil wells, natural gas wells, modular nuclear reactors, nuclear batteries, modular hydroelectric power systems, microturbines and turbine arrays, reciprocating engines, combustion turbines, cogeneration plants, biofuels, and the like. Energy storage systems include: gas generators, municipal solid waste incinerators, battery storage energy (including chemical batteries), capacitive energy storage, geothermal energy systems, molten salt energy storage, electrical thermal energy storage (ETES), gravity storage, compressed fluid energy storage, pumped hydro 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., fuel delivery vehicles, fuel delivery pipelines, wired power transmission systems, wireless power transmission systems, etc.

[0502] In an embodiment, the platform 102 enables a set of configured stakeholder energy edge solutions 108 having a wide range of functions, applications, capabilities, and uses that can be achieved, without limitation, by using or orchestrating a set of advanced energy resources and systems 104, including DERs and the like. The configured set of stakeholder energy edge solutions 108 may aggregate domain-specific stakeholder data, such as proprietary datasets generated in connection with enterprise operations, analytics, and / or strategy, real-time data from stakeholder assets (e.g., collected by IoT and edge devices located in proximity to stakeholder assets and operations), real-time data from stakeholder-specific energy resources and systems 104 (e.g., available energy generation, storage, or distribution systems that may be deployed at stakeholder locations to augment or replace the electric grid), etc., into solutions that meet the stakeholder's energy needs and capabilities, including baseline, period, and peak energy needs for conducting operations such as large-scale data processing, transportation, production of goods and materials, resource extraction and processing, heating and cooling, and many others.

[0503] In embodiments, the platform 102 (and / or its elements) and / or the set of configured stakeholder energy edge solutions 108 can obtain data from, provide data to, and / or exchange data with a set of data resources for the energy edge orchestration 110. The platform 102 obtains information from the set of data resources for the energy edge orchestration 110. These data resources may include data sets ranging from real-time energy consumption metrics to predictive analytics on future energy demand. Using these resources, the platform 102 can make timely and informed decisions. The platform 102 also obtains information from the set of data resources for the energy edge orchestration 110. Such data may include feedback on energy optimization strategies, insights gained 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 are continually updated, facilitating more accurate and dynamic energy management. Additionally, a set of configured stakeholder energy edge solutions 108, tailored to meet the unique needs of various stakeholders, can provide data to, and gain insights from, the platform 102. As an example, a stakeholder solution designed for solar energy farms can provide real-time data on solar panel efficiency, which the platform 102 can use to optimize energy distribution. Such data exchange between the platform 102, the set of configured stakeholder energy edge solutions 108, and the set of data resources for energy edge orchestration 110 ensures that optimizations are based on the most updated available data.

[0504] The platform 102 includes a set of intelligence enablement systems 112, a set of AI-based energy orchestration, optimization, and automation systems 114, and a set of configurable data and intelligence modules and services 118, with which it can integrate, exchange data, and / or link. The set of intelligence enablement systems 112 serves as the cognitive backbone of the platform 102. Utilizing advanced algorithms and computational tools, the set of intelligence enablement systems 112 provides the platform 102 with the intelligence necessary to analyze vast data sets, recognize patterns, and make informed decisions. The set of AI-based energy orchestration, optimization, and automation systems 114 ensures that the platform 102 achieves efficiency and adaptability. By orchestrating energy sources, optimizing energy flows, and automating processes, the set of AI-based energy orchestration, optimization, and automation systems 114 transforms the platform 102 into a dynamic entity that responds to real-time changes and is proactive in its strategy. A configurable set of data and intelligence modules and services 118 provides modularity and customization flexibility to the platform 102. Depending on the specific use case, stakeholders can configure these modules to meet their unique requirements.

[0505] The set of intelligence enabling systems 112 may include a set of intelligent data layers 130 that manage and process information, a set of distributed ledger and smart contract systems 132 that ensure secure and transparent transaction and data management, a set of adaptive energy digital twin systems 134 that create virtual replicas of physical energy assets for better monitoring and optimization, and / or a set of energy simulation systems 136 that model potential energy scenarios to support decision-making. These integrated systems work collectively within the intelligence enabling system 112 to provide a comprehensive solution for advanced energy management.

[0506] The set of AI-based energy orchestration, optimization, and automation systems 114 can include a set of energy generation orchestration systems 138 that manage and coordinate energy production sources, a set of energy consumption orchestration systems 140 that oversee and optimize energy usage, a set of energy market orchestration systems 146 that facilitate energy trading and transactions, a set of energy delivery orchestration systems 147 that ensure efficient and reliable energy distribution, and a set of energy storage orchestration systems 142 that manage the storage of energy. Combining these systems provides a holistic approach to orchestrating the entire energy lifecycle.

[0507] The set of configurable data and intelligence modules and services 118 may include a set of energy transaction realization systems 144 that facilitate and streamline energy-related transactions, a set of stakeholder energy digital twins 148 that provide stakeholder-specific virtual representations of energy assets for better monitoring and management, and a set of data integration microservices 150 that may enable or contribute to the realization of the set of configured stakeholder energy edge solutions 108 that ensure an integrated approach to energy management.

[0508] Platform 102 may include, integrate with, link to, exchange data with, be governed by, take input from, and / or provide output to one or more artificial intelligence (AI) systems, including 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 others described throughout this disclosure and in the documents incorporated by reference herein. Unless the context indicates otherwise, reference to AI or to one or more examples of AI should be understood to encompass these various alternative methods and systems. For example, without limitation, AI systems described to enable any of the wide variety of functions, capabilities, and solutions described herein (optimization, autonomous operation, prediction, control, orchestration, etc.) should be understood to be implementable by operating on a model or rule set, by training on a training dataset of human tags, labels, etc., by training on a training dataset of human interactions (e.g., as follows): g., training on a training dataset of human interaction (e.g., human interaction with a software interface or hardware system), training on an outcome training dataset, training on an AI-generated training dataset (e.g., a complete training dataset is generated by an AI from a seed training dataset), supervised learning, semi-supervised learning, deep learning, etc. For any given function or capability described herein, various types of neural networks may be used, including any of the types described herein or in documents incorporated by reference, and in embodiments, a hybrid set of neural networks may be selected within the set such that a more advantageous neural network type is implemented to perform each element of a multi-function or multi-capability system or method.As one example among many, deep learning, or black box, systems can use gated recurrent neural networks for functions like language translation in intelligent agents, where there is no need to understand the underlying mechanisms of AI behavior as long as the results are perceived favorably by the user; more transparent models or systems and simpler neural networks can be used in systems of automated governance, where a deeper understanding of how inputs are transformed into outputs may be necessary to comply with regulations or policies. AI-based energy orchestration, optimization and automation system

[0509] In embodiments, platform 102 can employ demand forecasting, including automated forecasting using artificial intelligence or by ingesting data streams of forecast information from third parties. Demand forecasting can help inform site selection and intelligently planned network expansion, among other things. In embodiments, machine learning algorithms can generate multiple forecasts for factors such as weather, prices, solar generation, energy demand, and other factors to analyze how energy assets can best capture or generate value at different times and / or locations.

[0510] In embodiments, the AI-based energy orchestration, optimization, and automation system 114 can enable optimization of energy patterns by analyzing a building's or other operational energy usage and seeking to reshape the patterns for optimization (e.g., by modeling demand response to various stimuli). By analyzing energy consumption trends, the AI-based energy orchestration, optimization, and automation system 114 can identify areas of waste or inefficiency. As an example, it can evaluate how a building's energy consumption varies at different times of the day or during different seasons. Using this knowledge, the automation system 114 can reshape these patterns to achieve optimal energy use. This could be applied in a commercial office building, where the AI-based energy orchestration, optimization, and automation system 114 might notice a spike in energy consumption in the early afternoon due to simultaneous use of lighting, heating, and cooling systems. By modeling how a building responds to certain stimuli, such as optimizing heating, ventilation, and air conditioning (HVAC) systems based on real-time occupancy data, an AI-based energy orchestration, optimization, and automation system 114 can suggest measures to distribute energy consumption more evenly throughout the day, thereby reducing peak demand and associated costs.

[0511] The AI-based energy orchestration, optimization, and automation system 114 may be enabled by a set of intelligence enabling systems 112 that provide functions and capabilities to support a variety of applications and use cases.

[0512] In an embodiment, platform 102 may be configured to integrate data from at least one internal edge device located within the environment (e.g., sensors in a building, vehicle, machinery, utility) and at least one external edge device located outside the environment (e.g., sensors in a weather monitoring station broadcasting real-time data, vehicles, etc.). Platform 102 may collect real-time energy intelligence data and provide it to an intelligence circuit that learns based on the data and results and automatically takes action to optimize energy management. For example, an edge device connected to a DER may be combined with an edge device from a local weather monitoring station. Local weather data (e.g., cloud cover, temperature, wind, precipitation, etc.) may be correlated with energy output from the DER, and a machine learning model may be trained to utilize variables from a second edge device to predict actions related to the environment of the first edge device. As a further example, radar signals output by a weather station edge device may be used to act on the ramping up or down of energy from the DER.

[0513] In embodiments, data output from one or more edge devices may be vectorized and / or stored in a distributed database. Energy data acquisition from devices may be further optimized through the use of vector-based updates of data, where only changes that affect the model of consumption information are communicated. Vectors can be developed based on the analysis of data from the aforementioned consuming devices. Vectors for complex energy consumption systems may be multidimensional vectors representing consumption types, purposes, devices, etc., to form a highly efficient way of communicating complex energy usage environments. As an example, consider a smart grid system in which thousands of home appliances, HVAC systems, and lighting solutions continuously transmit energy consumption data. The system analyzes this data and creates vectors based on consumption patterns. These vectors may include various parameters, such as consumption types, consumption purposes, and specific devices consuming energy, especially for complex energy consumption systems.

[0514] 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 also be based on broader data, including weather forecast data, energy consumption in areas currently affected by weather forecasts to prepare areas expected to experience weather, etc. Pattern analysis may include not only raw usage but also information about the consumer (e.g., energy-consuming devices being operated) that may influence learning. As an example, a consumer's daily work schedule may include turning off all household appliances during work hours and increasing energy consumption in the evening, which may be a local pattern that may be recognized and adapted to by the system.

[0515] Demographics and other human-based activities may play a role in energy pattern analysis. As an example, local demographics that suggest consumers trade in older vehicles for newer ones more frequently than other areas may suggest that local energy demand for electric vehicle charging may increase sooner in such areas. If demographics and / or consumer behavior suggest that consumers in a region tend to trade in for used vehicles, maintaining legacy energy procurement in such areas may be indicated as preferable. Intelligence Realization System Subsystems and Modules Intelligent Data Layer

[0516] The set of intelligence enabling systems 112 may include a set of intelligent data layers 130, e.g., a set of services (including microservices), APIs, interfaces, modules, applications, programs, etc., that consume any of the data entities and types described throughout this disclosure and may undertake a wide range of processing functions, such as extraction, cleansing, normalization, calculation, transformation, loading, batching, streaming, filtering, routing, parsing, conversion, pattern recognition, content recognition, object recognition, etc. Through a set of interfaces, users of the platform 102 can configure the set of intelligent data layers 130 or their outputs to meet internal platform needs and / or enable further configuration, such as a set of configured stakeholder energy edge solutions 108. The set of intelligent data layers 130, and more generally the set of intelligence enablement systems 112, and / or the configurable data and intelligence modules and services 118, may access data from a variety of sources throughout the platform 102 and, in embodiments, may operate from a set of shared data resources, which may be included in a set of centralized and / or distributed databases, or may consist of a set of distributed or decentralized data sources, such as IoT or edge devices that generate energy-related event logs or streams.The set of intelligent data layers 130 may be configured for a wide range of energy-related tasks, such as forecasting / predicting energy consumption, generation, storage, or distribution parameters (e.g., at the individual device, sub-device level, at the individual device, subsystem, system, machine, or fleet level); optimizing energy generation, storage, distribution, or consumption (even at various levels of optimization); automatically discovering, configuring, and / or executing energy transactions (including micro-trades and / or larger transactions in spot and futures markets, as well as transactions in peer-to-peer group or single counterparty transactions); monitoring and tracking energy consumption, generation, distribution, and / or storage parameters and attributes (e.g., baseline levels, volatility, cyclical patterns, episodic events, peak levels, etc.); monitoring and tracking energy-related parameters and attributes (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 preferred user behavior), and many others.

[0517] In an embodiment, the platform 102 may be configured to analyze the monitored energy dataset and generate configuration recommendations for the distributed system to generate and consume energy. The platform 102 may be configured to analyze streams from one or more local power consuming entities and generate recommendations. For example, a manufacturing plant may have a significantly different set of needs than a hospital campus. In this manner, the AI-based platform can perform an analysis of each of multiple energy consumption scenarios and associated devices and demands to recommend DER types for supplying and regulating energy that correspond to the needs and demands of the local power consuming entities. A hospital may have ERs with specific sets of demands, such as operating room open hours or contingent demand based on emergency situations. Examples of monitored energy datasets include one or more of grid-based energy resources and mobile energy resources. Grid-based energy resources may include, for example, fossil fuel-based energy production facilities (such as coal, oil, and natural gas), renewable energy-based production facilities (such as solar power plants, wind power plants, geothermal power plants, tidal power plants, and hydroelectric power plants). Mobile energy resources may include, for example, mobile battery installations, mobile fossil fuel-based generators, mobile renewable energy generators, mobile transformers and power conditioning systems, drone-based power supply / storage systems, vehicle-based power supply / storage systems, and the like. Distributed Ledger and Smart Contract Systems

[0518] The set of intelligence-enabling 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 of the smart contracts may operate on data stored on the set of distributed ledgers or blockchains to record energy-related trading events such as purchases and sales of energy (in spot, forward, and peer-to-peer markets, as well as direct counterparty transactions), associated service charges, trading-related energy events such as consumption, generation, distribution, and / or storage events, and other trading-related events often associated with energy, such as carbon production or reduction events, renewable energy credit events, pollution production or reduction events, etc. The set of smart contracts handled by the smart contract system 132 may consume as a set of inputs any of the data types and entities described throughout this disclosure, undertake a set of computations (optionally configured in flows that incorporate inputs from disparate systems in multi-step transactions), and provide a set of outputs that enable transactions to be completed, reported (optionally recorded on a set of distributed ledgers), etc. The set of energy trading realization systems 144 may be enabled or augmented by artificial intelligence, including autonomously discovering, structuring, and executing trades according to a strategy and / or providing automated or semi-automated trading based on training and / or oversight by a set of trading experts.

[0519] In embodiments, the smart contract system 132 can be used by a set of energy trading enablement systems 144 (described elsewhere in this disclosure) to configure trading solutions. Each smart contract in the smart contract system 132 is intricately designed to process data stored within these distributed ledgers or blockchains. Smart contract functionality extends to documenting various energy-related trading events, including, but not limited to, recording peer-to-peer energy transactions and direct transactions between parties. Additionally, smart contracts capture data related to energy events associated with transactions, including information about energy consumption, generation, distribution, and storage, as well as service fees and other transaction-related data. For example, in a city energy grid that integrates renewable energy sources such as solar and wind, the smart contract system 132 can autonomously execute agreements to purchase solar energy during peak solar hours and wind energy during windy hours. At the same time, it also records each transaction, the associated service fees, and even the carbon offsets achieved through the use of renewable energy sources. Adaptive Energy Digital Twin System

[0520] Any entity, analysis result, artificial intelligence output, state, operating state, or other characteristic noted throughout this disclosure may, in embodiments, be presented in a digital twin, such as a set of broadly applicable adaptive energy digital twin systems 134 and / or a set of stakeholder energy digital twins 148 configured to the needs of a particular stakeholder or stakeholder solution. A set of adaptive energy digital twin systems 134 can provide, for example, visual or analytical indicators of energy consumption by a set of machines, a group of factories, a fleet of vehicles, etc., subsets of the same (e.g., to compare energy parameters by each of a set of similar machines to identify out-of-range operation), and many other aspects. Digital twins may be adaptive, such as filtering, highlighting, or otherwise adjusting the data presented based on real-time conditions, such as changing energy costs, changing driving behavior, etc.

[0521] In embodiments, the platform 102 may be configured to create, manage, and / or otherwise provide dynamic digital twins of past, current, and predicted distributed energy demand for both mobile and stationary entities within a base domain. For example, relatively large enterprise or organizational settings, such as industrial environments, factory environments, distribution centers, hospital environments, university / college environments, office building environments, and mining operations, may be modeled via digital twins. In a specific example, for a manufacturing facility with numerous machines, assembly lines, and automation systems, the platform 102 can create a digital twin of this environment, capturing every detail of its energy consumption patterns. Such a digital twin can provide real-time information about the facility's energy demands, from each machine's historical energy usage data to current consumption rates and even predictions of future energy demand based on a forecasted production schedule. Large-scale environments can be modeled to enable significant cost shifts based on energy adjustments across the entire environment. For example, in large environments with high energy consumption, even small adjustments can have significant financial impacts. Having a dynamic digital twin allows stakeholders to simulate various energy adjustments and analyze their impacts. For example, an office building can make a big difference in energy costs by adjusting the operation of its HVAC systems based on real-time occupancy data or optimizing lighting based on the availability of natural light.

[0522] In embodiments, platform 102 can be configured to model government entities, such as states, counties, cities, towns, development areas, and communities, through one or more digital twins. As an example, for a city with thousands or hundreds of thousands of residents, businesses, a public transportation system, and numerous amenities, platform 102 can create a digital twin of such a city to capture every aspect of its energy consumption. This digital representation can include everything from lighting in public parks to air conditioning systems in government buildings and the energy demands of public transportation. In doing so, platform 102 provides city managers with a holistic view of the city's energy footprint, facilitating informed energy management decisions. Platform 102 can also model larger entities, such as states and counties, capturing the diverse energy demands of various regions, from urban centers to rural areas. Platform 102, on the other hand, can also represent smaller entities, such as towns. For example, in a new town being developed for industrial use, platform 102 can model the expected energy demand based on the planned industries to ensure that the energy infrastructure is properly prepared to meet the demand. In another example, a prefecture planning a transition to renewable energy could use a digital twin to simulate the impact of integrating solar farms or wind turbines, providing insight into potential energy savings, grid stability, and the environmental benefits of such a transition.

[0523] In embodiments, the platform 102 may include an AI-based system for updating the digital twin based on a set of energy parameters, which may include adapting energy consumption data from the digital twin's physical devices based on the set of energy parameters, such as adjusting the cost incurred for consumed energy based on the dynamic energy market from which the device sources energy. As an example, consider a device that procures energy from a dynamic energy market where the cost of energy fluctuates based on demand, supply, and other market factors. If the device consumes energy when costs are high, the AI-based system can adjust the digital twin to reflect this, accurately reflecting the financial impact of virtual and real-world energy consumption. The AI-based system may also incorporate the energy procurement preferences of the device's user(s) (optionally expressed in the device's digital twin) when updating the device. For example, if a user expresses a preference for green energy through the device's digital twin, the AI ​​system ensures that this preference is incorporated into updates to the energy consumption data. In the case of a shared device (e.g., an e-bike), energy consumed during the user's sharing of the device (while the e-bike is checked out to the user's account) and / or energy associated with the user's sharing of the device may be allocated to specific energy sources based on the user profile. For example, when a user checks out an e-bike under their user account, energy consumed during use may be sourced from a specifically preferred energy source, as detailed in the user profile associated with the user account. Additionally or alternatively, the owner of the device and / or digital twin may specify a budget of consumed energy to allocate to each of multiple energy sources. As an example, there may be a scenario in which the device owner has a specific budget of consumed energy across multiple energy sources. In such a case, the AI ​​system ensures that the digital twin accurately reflects this budget.For example, an owner can specify that 50% of the energy consumed by a device should come from wind energy and the remaining 50% should come from hydroelectric energy. The AI ​​system can ensure that this allocation is accurately reflected when updating the digital twin. In this way, platform 102 with its AI-based system provides a digital twin that is dynamic, responsive, and tailored to individual preferences and real-world scenarios, rather than just a static representation.

[0524] In embodiments, the AI-based system for updating the digital twin based on the set of energy parameters may include adapting energy production and / or distribution control for a future period (e.g., during a future high-demand event) based on the set of energy parameters. This may include relying on AI-based predictions of energy demand for a future period to adjust how the energy supply system operates, such as energy parameters that determine how much energy to store versus generate and supply. As an example, in a scenario where high demand is predicted, perhaps due to a festival, the AI-based system can predict this demand surge by analyzing the energy parameters and adapt energy production and / or distribution control accordingly. In another example, based on historical data and current trends, the AI-based system can predict an increase in energy demand during the summer. In addition to AI-based energy demand predictions, the AI-based system can evaluate macro trends / activities based on the energy parameters. As an example, an AI-based system updating an energy consumption system may detect pricing patterns suggesting that energy costs may rise sharply (e.g., due to a major weather event, etc.), and the set of energy parameters may guide the AI-based system to adapt energy consumption and / or storage guidance for at least selected consumers (e.g., a public system (e.g., a tax-based system) to avoid unnecessary burdens on taxpayers). As an example, if the AI-based system detects a pattern suggesting that energy costs may rise due to an upcoming major weather event, preemptive measures may be taken. By analyzing a set of energy parameters, the AI-based system may guide certain consumers to adapt their energy consumption or storage patterns or guide public systems to reduce consumption or increase storage. In this way, the platform 102 with the AI-based system ensures that energy management is proactive and efficient.

[0525] In embodiments, platform 102 may be configured to provide and / or facilitate digital twins of a common device type (e.g., e-bikes of the same model). The digital twins can exchange consumption data across a range of usage instances to better understand how this common device type consumes energy in different environments, different time periods, different geographies, and user demographics (including those local to the point of use). For example, an e-bike used primarily in hilly terrain may exhibit different energy consumption patterns compared to one used in a flat, urban environment. By aggregating this data from various digital twins, platform 102 can identify these patterns and make informed predictions. This allows the digital twin of a particular device (e.g., a particular e-bike) to better predict energy demand, particularly leading to dynamic charging profiles. Some devices may be located in high-demand areas suggesting the need for more frequent charging, while other devices may be permitted to maintain a lower average energy charge, for example, due to shorter usage times and less frequent use. For example, an e-bike located in a busy urban area may be recognized as requiring frequent charging due to high demand. Meanwhile, another e-bike may be located in a low-use area and operate optimally without frequent charging. Demand profiles for various geographic areas can also be aggregated to identify demand, such as charging needs and available energy. As an example, in an area with a high concentration of e-bikes (for example), the platform 102 can suggest staggering charging schedules to balance demand and prevent grid overload. This can lead to managing e-bike charging activity, including balancing demand with other chargeable devices in the area.

[0526] In embodiments, platform 102 can be configured so that every physical instance of a device (e.g., a particular model of e-bike) does not need to have its own permanent digital twin. Because most of these types of devices are dormant for significantly longer periods than they are in use (i.e., have a very sparse duty cycle), even the energy demand for processing to support the digital twins of these types of devices can be managed based on demand profiles. Instances of physical devices (or configured genetic instances) can be activated (energy resources can be allocated) based on predicted demand. Consider the scenario of a particular model of e-bike: these e-bikes may be scattered across various locations and always available, but their actual frequency of use, or “duty cycle,” may be infrequent, and the devices may be dormant for long periods of time. Recognizing this unique characteristic, platform 102 can be configured to activate the digital twins of these devices based on predicted demand, instead of continuously maintaining a digital twin for each e-bike. As an example, in an urban environment, if platform 102 predicts a surge in e-bike demand, say during the morning rush hour, it can activate the e-bike’s digital twin during that time. These digital twins can facilitate energy management, ensuring that e-bikes are charged and ready for use. Once rush hour is over, these digital twins can be deactivated to save processing energy. This demand-driven approach ensures optimal utilization of energy resources for processing the digital twins.

[0527] In embodiments, platform 102 may provide for and / or facilitate the sharing, exchange, and / or aggregation of energy consumption data provided to a digital twin by physical device instances, which can be harvested to establish a set of energy demand parameters for a predictive energy demand model, etc. For example, platform 102 is designed to facilitate the exchange and aggregation of energy consumption data from various physical device instances and stream it to their respective digital twins. As an example, consider a neighborhood with multiple smart homes, each equipped with multiple smart devices. While each home may have its own unique energy consumption patterns, the aggregate data from all these homes can reveal broader trends. By aggregating this data, platform 102 can identify patterns, such as increased energy consumption during the holiday season or decreased demand during holiday periods. Such insights can be fed into predictive models, ensuring energy providers are adequately prepared to meet the predicted demand.

[0528] In embodiments, the platform 102 may be configured so that energy consumption data provided to the digital twin also facilitates forecasting of energy-related demand, such as maintenance of energy supply infrastructure. For example, the need to address waste from energy production can be better predicted based on the digital twin's available supply sources, not just consumption. In other words, physical devices not only consume energy but must also be supplied with energy (or generate their own). Energy supply and / or procurement can be used by the digital twin to indicate time / region / specific sources of energy production for support (waste removal, renovation, etc.). As an example, if a local energy production facility relies primarily on non-renewable energy sources, the associated waste generation will be high. The digital twin can predict this and ensure appropriate waste management measures are implemented. Furthermore, the digital twin of a local energy production facility can leverage demand forecasts from the energy consumption digital twin to address not only production but also up-the-chain procurement. For example, if the forecast demand for e-bike usage (using e-bikes again as an example) for upcoming events (graduation, freshman day, etc.) could be forecast along with, for example, the availability of solar energy, a local energy depot could source up-the-chain energy only when and / or as needed. For example, if the solar energy forecast is good, the depot could rely primarily on solar energy; otherwise, the depot could source energy from up-the-chain energy suppliers to meet demand. Energy Simulation System

[0529] In an embodiment, the set of energy simulation systems 136 is provided to develop and evaluate detailed simulations of energy generation, demand response, and charging management, including simulation environments that simulate the results of using various algorithms that may govern power generation across various generation assets, consumption by energy-demanding devices and systems, and energy storage. Data can be used to simulate the interaction of uncontrollable loads with optimized charging processes, among other use cases. The simulation environments can provide output to, integrate with, or share data with the set of adaptive energy digital twin systems 134. As an example, if a city is planning a transition to renewable energy sources, the city can use the set of energy simulation systems 136 to simulate various outcomes. The simulations can predict how solar panels will react to changing weather, how wind turbines will operate during different seasons, or how energy storage solutions need to be managed during peak demand periods.

[0530] In embodiments, as more enterprises adopt hybrid infrastructures, uptime becomes more complex, requiring backup and failover strategies that span cloud, colocation, on-premises facilities, and edge infrastructure. This may include AI-based algorithms to automatically manage the energy of devices and systems within such devices. For example, artificial intelligence may enable autonomous data center cooling or industrial controls. In embodiments, distributed energy resources (DERs) 128 may be integrated with or be integrated with, for example, AI-driven computing infrastructure, smart power distribution units (PDUs), uninterruptible power supply (UPS) systems, energy-aware airflow management systems, and HVAC systems. By simulating energy scenarios, a set of energy simulation systems 136 ensures that enterprises operate seamlessly and sustainably, regardless of infrastructure model. Introduction to the main subsystems and modules of an AI-based energy orchestration, optimization, and automation system

[0531] The set of AI-based energy orchestration, optimization, and automation systems 114 may include, among others, a set of energy generation orchestration systems 138, a set of energy consumption orchestration systems 140, a set of energy storage orchestration systems 142, a set of energy market orchestration systems 146, and a set of energy delivery orchestration systems 147. For example, the set of energy delivery orchestration systems 147 may enable the orchestration of energy delivery to points of consumption via fixed grids, wireless energy transmission, fuel delivery, stored energy (e.g., chemical or nuclear batteries), etc., and may include autonomously optimizing the combination of energy types among the aforementioned available resources based on various factors, such as location (e.g., based on distance from the grid), purpose or type of consumption (e.g., whether there is a need for very high peak energy supply, such as a power-intensive production process), etc. Consider a remote industrial unit far from the main grid that requires power for its production process. The set of energy generation orchestration 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 orchestration systems 138 can suggest that a combination of wireless energy transmission and a chemical battery supply may be most suitable in this case. Configurable Data and Intelligence Modules and Services

[0532] In an embodiment, the platform 102 may include a configurable set of data and intelligence modules and services 118. These may include a set of energy tradable systems 144, a set of stakeholder energy digital twins 148, a set of data integration microservices 150, etc. Each module or service (optionally configured in a microservices architecture) may exchange data with various data resources to provide related outputs, such as to support a set of internal functions or capabilities of the platform 102 and / or to support one or more functions or capabilities of a set of configured stakeholder energy edge solutions 108. As one example among many, a service may be configured to obtain event data from IoT devices with cameras or sensors monitoring power generators and integrate it with weather data from public data resources 162 to provide a weather-related timeline of the power generators' energy generation data, which may in turn be consumed by the set of configured stakeholder energy edge solutions 108 to help forecast day-ahead energy generation by the power generators based on day-ahead weather forecasts. Such a wide range of configured data and intelligence modules and services 118 may be enabled by the platform 102 and represent, for example, various outputs consisting of the fusion or combination of the wide range of energy edge data sources handled by the platform, higher level analytical outputs resulting from expert analysis of the data, predictions and forecasts based on patterns in the data, automation and control outputs, and many other outputs.

[0533] In embodiments, platform 102 may be configured to enable energy consuming devices and / or systems (e.g., a set of energy consuming devices in a home) to locally arbitrate for access to energy sources, such as mains energy, first-level stored energy (e.g., at the device), and local stored energy (e.g., a local battery that can supply energy to multiple devices). Devices may also consume energy for various purposes, such as consumption, storage, balancing supply, and acting on behalf of other devices. Energy consuming devices may also be configurable / configurable to use multiple energy types, such as the power grid, solar, geothermal, fossil fuels (combustion engines), and hydrogen. Within an energy consuming system (a collection of devices such as those described above), energy consumption may span various energy sources (e.g., hydrogen for cooking, solar for energy storage, waste-to-energy recovery, etc.). As an example, consider a home with multiple energy consuming devices, each with its own unique energy needs and preferences. Platform 102 can facilitate a dynamic environment in which these devices can locally arbitrate access to various energy sources based on immediate needs and available resources. For example, on a sunny day, the solar panels on a home may be generating excess energy, in which case the platform 102 may utilize the energy primarily from the solar panels and reduce energy consumption from the grid.

[0534] In embodiments, platform 102 can capture energy consumption information from / via edge devices and develop datasets representing multiple perspectives on consumed energy. Edge devices, which may communicate (e.g., locally or in close proximity) with various energy-consuming devices and device types, can collect data about the devices, including, for example, what sources the devices may consume, what sources the devices have consumed, the purpose / use of the consumed energy, etc. Further examples could include whether devices appear to be performing some optimization, such as utilizing local storage during periods of high energy costs (including high transmission costs, which may be measured based on delivery efficiency, etc.), consuming energy to replenish storage during off-peak hours, and / or utilizing lower-cost sources (e.g., solar) when readily available. Extensive analytics may be generated, captured, and used in energy management systems, etc. As an example, consider a smart plug connected to a refrigerator that provides insights into energy consumption patterns, revealing details such as a preference for using local storage during times 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. Energy trading support system

[0535] The configurable data and intelligence modules and services 118 may include a set of energy trade-enabling systems 144. A set of energy trade-enabling systems 144 may include smart contracts that may operate on data stored in a set of distributed ledgers or blockchains that record energy-related trading events, such as energy purchases and sales (spot, forward, and peer-to-peer markets, as well as direct counterparty transactions) and associated service fees, trading-related energy events such as consumption, generation, distribution, and / or storage events, and other trading-related events often associated with energy, such as carbon production or reduction events, renewable energy credit events, pollution production or reduction events, etc. A set of smart contracts can consume as a set of inputs any of the data types and entities described throughout this disclosure, perform a set of calculations (optionally configured with flows incorporating inputs from disparate systems in multi-step transactions), and provide a set of outputs that enable trade completion, reporting (optionally recorded on a set of distributed ledgers), etc. The set of energy trade realization systems 144 can be enabled or augmented by artificial intelligence, including autonomously discovering, structuring, and executing trades according to a strategy and / or providing automated or semi-automated trading based on training and / or oversight by a set of trading experts. Autonomy and / or automation (supervised or semi-supervised) can be enabled by robotic process automation, such as by training a collection of intelligent agents to interact with a group of trading experts and trade realization systems (e.g., software systems used to configure and execute energy trading activities) to discover, structure, or execute trades.

[0536] As energy becomes produced and consumed in local, decentralized markets, energy markets are likely to follow the pattern of other peer-to-peer and shared economy markets, such as ride-sharing, apartment-sharing, and second-hand goods markets. Technology makes it possible to bypass top-down, centralized energy supply, and allows operators to create platforms that allow them to manage and monetize excess capacity through leasing and trading of assets and output.

[0537] As more decentralized or peer-to-peer tradable energy markets develop, platform 102 may include, link, integrate, or enable systems for P2P trading, wholesale contracts, renewable energy certificate (REC) tracking, and other platforms facilitating broader distributed energy supply, payment management, and other transaction elements. In embodiments, the above may use a blockchain, distributed ledger, and / or smart contract system 132. As an example, a homeowner with excess solar energy may decide to sell this excess energy. This transaction is securely recorded on the blockchain.

[0538] Specifically, increased transparency, choice, and flexibility will enable consumers to actively participate in energy markets by generating, storing, and selling electricity, rather than just consuming it. As an example, let's say a community decides to go solar. Platform 102 will enable households with solar panels to trade their excess energy with those that do not, benefiting the entire community.

[0539] In an embodiment, the transaction component may be configured by a set of energy transaction-enabling systems 144 to optimize energy generation, storage, or consumption, such as utility time-of-use rates. An IoT-based platform that can identify the times of day when energy costs are lowest shifts energy demand away from high-price times. As an example, in areas where utility costs vary by time of use, the platform 102 can shift energy demand to times when energy is cheaper. As an example, smart home devices linked to the platform 102 can identify the times of day when energy costs are lowest and adjust their operation accordingly, ensuring efficient and cost-effective energy consumption. Stakeholder Energy Digital Twin

[0540] The configurable data and intelligence modules and services 118 may, in embodiments, include a set of digital twins 148 configured to represent a set of energy-related stakeholder entities, including stakeholder-owned and stakeholder-operated energy generation resources, energy distribution resources, and / or energy distribution resources (including representing them by type, such as representing renewable energy systems, carbon generation systems, and others), stakeholder energy digital twins 148, stakeholder information technology and network infrastructure entities (e.g., edge and IoT devices and systems, networking systems, data centers, cloud data systems, on-premise information technology systems, etc.), energy-intensive stakeholder production facilities such as machinery and systems used in manufacturing, stakeholder transportation systems, market conditions (e.g., regarding current and future market prices of energy, stakeholder supply chains, stakeholder products and services, etc.), etc. The set of stakeholder energy digital twins 148 may provide real-time information regarding status, operating conditions, etc., particularly related to energy consumption, generation, storage, and / or distribution, such as provided sensor data, event logs, and other information streams from IoT and edge devices.

[0541] A set of stakeholder energy digital twins 148 can provide a visual, real-time view of energy impacts across all aspects of an enterprise. The digital twins may be role-based, providing visual analytical metrics appropriate to the user's role, such as financial reporting information for a chief financial officer (CFO), operational parameter information for a power plant manager, and energy market information for an energy trader. As an example, a CFO may need a visual representation that highlights the financial costs of energy consumption, such as how shifting operations to off-peak hours affects energy costs. In contrast, a power plant manager may be interested in operational parameters such as the efficiency of energy-generating resources. Meanwhile, an energy trader may seek insights into the energy market, such as tracking prices. By providing insights tailored to individual roles in this way, a set of stakeholder energy digital twins 148 ensures that various stakeholders have the relevant information they need to make informed decisions. Data Integration Microservices

[0542] The configurable data and intelligence modules and services 118 may include a set of data integration microservices 150, such as organized in a service-oriented architecture such that various microservices can be grouped in series, in parallel, or in more complex flows to create higher-level, more complex services that each provide a defined set of outputs by processing a defined set of outputs, such as to enable a configured set of stakeholder energy edge solutions 108 or to facilitate an AI-based orchestration, optimization, and / or automation system 114. The configurable data and intelligence modules and services 118 may be composed of, without limitation, various functions and capabilities of a set of intelligent data layers 130 that operate on various data resources for the energy edge orchestration 110 and / or the platform's 102 internal event logs, outputs, data streams, etc. Figure 2A-2B: Introduction to the main subsystems of the main components of an ecosystem Data Resources for Energy Edge Orchestration

[0543] Referring to FIG. 2A , data resources for energy edge orchestration 110 may include a set of edge and IoT networking systems 160, public data resources 162, and / or a set of enterprise data resources 168, which, in embodiments, may use or be enabled by an adaptive energy data pipeline 164 that automatically handles the data processing, filtering, compression, storage, routing, transport, error correction, security, extraction, transformation, loading, normalization, cleansing, and / or other data processing capabilities involved in transporting data over a network or communication system. This may include, but is not limited to, based on data content (e.g., by packet inspection or other mechanisms for understanding the same), based on network conditions (e.g., congestion, delay / latency, packet loss, error rates, cost of transport, Quality of Service (QoS), etc.), based on the context of use (e.g., based on the user, system, use case, application, etc. (including based on similar prioritization), based on market factors (e.g., price or cost factors), based on user configuration, or other factors, as well as various combinations of the same. For example, among many other factors, the least cost route may be automatically selected for data related to managing low priority energy uses such as heating a swimming pool, while the fastest or highest QoS route may be selected for data supporting high priority uses or energy such as supporting critical medical infrastructure.

[0544] 2B, the platform 102 and orchestration may include, integrate, link, integrate, use, create, or otherwise process a wide range of data resources for the advanced energy resources and systems 104, the set of configured stakeholder energy edge solutions 108, and / or the energy edge orchestration 110. In embodiments, the elements of the advanced energy resources and systems 104, the set of configured stakeholder energy edge solutions 108, and / or the energy edge orchestration 110 may be the same as, similar to, or different from the corresponding elements shown in FIG. 1. The data resources may include separate databases, distributed databases, and / or federated data resources, among many others. Edge and IOT Networking Systems

[0545] A wide range of energy-related data may be collected and processed (including by artificial intelligence services and other functions) by the set of edge and IoT networking systems 160, and control instructions may be processed, such as those integrated into devices, components, or systems, those located on IoT devices and systems, those located on edge devices and systems, or when the foregoing are located in or around energy-related entities such as those used by consumers or businesses, e.g., those involved in the generation, storage, delivery, or use of energy. These include any of the wide range of software, data, and network systems described herein. Public Data Resources

[0546] In an embodiment, the platform 102 may track public data resources 162, such as weather data. Weather conditions can affect energy usage, particularly related to HVAC systems. By collecting, compiling, and analyzing weather data in conjunction with other building information, building managers can be proactive about HVAC energy consumption. Public data resources 162 may include satellite data, demographic and psychographic data, population data, census data, market data, website data, e-commerce data, and many other types. Enterprise Data Resources

[0547] The 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, sales data, and many others. Subsystems and modules of advanced energy resource systems

[0548] In an embodiment, advanced energy resources and systems 104 can include distributed energy resources (DERs) 128. More distributed energy resources mean more individuals, networked groups, and energy communities will be able to generate and share their own energy and coordinate systems to achieve ultimate effectiveness. DERs 128 can be small- or medium-sized units of power generation and / or storage that may be connected to a larger power grid at a local, distribution level. For example, DERs 128 can either be connected to a local power grid or isolated from the grid for standalone use. Transforming energy infrastructure

[0549] The advanced energy resources and systems 104 orchestrated by platform 102 can include a set of transformed energy infrastructure systems 120. The energy edge involves increasing digitization of generation, transmission, substation, and distribution assets, which in turn shapes the operation, maintenance, and expansion of legacy grid infrastructure. In embodiments, the set of transformed energy infrastructure systems 120 can be integrated with or linked to platform 102. The transition to an improved infrastructure can include transitioning from SCADA systems and other existing control, automation, and monitoring systems to an IoT platform with advanced capabilities.

[0550] In embodiments, new assets (e.g., DERs 128) that are added to or coordinated with the grid may be compatible with the existing infrastructure to maintain voltage, frequency, and phase synchronization. As an example, consider a city that is incorporating renewable energy sources, such as wind turbines and solar panels (DERs 128), into its existing power grid. These new assets must integrate with the older infrastructure to ensure a stable power supply. This compatibility ensures that residents do not experience fluctuations in voltage, frequency, or phase synchronization, ensuring a stable power supply, even as the city transitions to greener energy sources.

[0551] Specifically, improvements to legacy grid assets, new grid-connected equipment, and support systems may: comply with regulatory standards from NERC, FERC, NIST, and other relevant authorities; positively impact grid reliability; make the grid less susceptible to cyberattacks and other security threats; increase the grid's ability to accommodate broader bidirectional flows of energy (e.g., DER penetration); and provide interoperability with technologies that improve grid efficiency (e.g., provide or facilitate demand response, reduce grid congestion).

[0552] Digitalization of legacy grid assets can relate to assets used for generating, transmitting, storing, and distributing electricity, such as power plants, substations, and transmission lines.

[0553] In an embodiment, 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), smart (AI / ML-based) outage detection and response, and / or smart (AI / ML-based) load forecasting (including optional integration of DERs 128 with the existing grid). As an example, consider a scenario in which an electric utility has a network of generation and distribution assets, some of which are decades old. To ensure the longevity and efficiency of these assets, platform 102 may provide predictive maintenance and alert the utility to potential issues before they become critical.

[0554] In embodiments, the platform 102 can provide maintenance for the power grid. Proactive maintenance allows utilities to accurately detect defects and reduce unplanned outages to better serve their customers. AI systems deployed with IoT and / or edge computing can help monitor energy assets and reduce maintenance costs. For example, if a power line shows signs of wear or tear, the platform 102 can alert the power company for timely repairs. This proactive approach not only reduces unplanned outages but also reduces maintenance costs, resulting in a more efficient and cost-effective power grid. Digitized Resources

[0555] In embodiments, platform 102 can take advantage of the digital transformation of a wide range of digitized resources. Machines are becoming smarter, and software intelligence is being embedded into every aspect of business, helping to drive new levels of operational efficiency and innovation. Digital transformation is also underway, with the increasing presence of smart devices and systems capable of processing and communicating data, and near-ubiquitous sensors at the edge, IoT, and other devices generating large, dense data streams. All of this presents opportunities for increased intelligence, automation, optimization, and agility as information continuously flows between the physical and digital worlds. These devices and systems require large amounts of energy. For example, data centers consume large amounts of energy, and edge and IoT devices may be deployed in off-grid environments that require alternative forms of energy generation, storage, or movement. In embodiments, a set of digitized resources may be integrated, accessed, or used for energy optimization for compute, storage, and other resources in data centers and at the edge, among other locations. In an embodiment, as more devices incorporate sensors and controls, machines will "talk" to each other, enabling a continuous flow of information between the physical and digital worlds. Products can be tracked from source to customer and even while in use, allowing for rapid response to internal and external changes. Those responsible for managing and regulating such systems can obtain detailed data from these machines to optimize the operation of the entire process. This trend transforms big data into smart data, enabling significant cost and process efficiencies.

[0556] In an embodiment, advances in digital technology have enabled a level of monitoring and operational performance that was previously impossible. Thanks to sensors and other smart assets, service providers can collect a wide range of data across multiple parameters and monitor in real time, 24 hours a day.

[0557] In embodiments, DERs 128 are integrated into computing networks and infrastructure devices and systems to augment the existing power grid, reduce costs, and improve reliability. For example, by integrating DERs 128, such as localized solar power plants and wind turbines, into city infrastructure, platform 102 can significantly augment the existing power grid. For example, instead of relying solely on traditional power plants during peak demand periods, platform 102 enables a city's energy management system to utilize localized energy sources. Mobile Energy Resources

[0558] In embodiments, DERs may be integrated with mobile energy resources 124, such as electric vehicles (EVs) and their charging networks / infrastructure, thereby augmenting the existing power grid and helping to reduce costs and improve reliability. Given the rise of EVs (of all types), charging infrastructure and vehicle charging plans need to be optimized to match supply and demand. Furthermore, the growing demand for electricity and the development of EV infrastructure require optimization using related technologies, such as edge technology and IoT. Charging for electric vehicles may be integrated into distributed infrastructure, such as adding to the grid, such as bidirectional charging stations, or even used as DERs 128 by locally powering another system. Vehicle power electronics systems and batteries can benefit the power grid by providing system and grid services. Excess energy can be stored in the vehicle as needed and discharged when needed. This flexibility option not only avoids expensive load peaks during short-term high energy demand periods, but also increases the use of renewable energy.

[0559] In embodiments, universal integration of electric vehicles and charging infrastructure into the electric grid requires cooperation with various other standardized communication protocols. Platform 102 may include, integrate, and / or link a set of communication protocols that enable management, provisioning, governance, control, etc. of energy edge devices and systems that use such protocols. Here, platform 102 may act as a central hub that integrates the various protocols, ensuring that when an EV docks at a charging station, communication between the vehicle, the station, and the grid is smooth, efficient, and coordinated. Energy Edge Solutions from Consistent Stakeholders

[0560] The set of configured stakeholder energy edge solutions 108 may include, among others, 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 local production solutions 158, which use various advanced energy resources and systems 104 and / or various configurable data and intelligence modules and services 118 to enable benefits to specific stakeholders, such as private companies, non-governmental organizations, independent service organizations, and government organizations. All such solutions may leverage edge intelligence, such as using data collected from on-board or integrated sensors, IoT systems, and edge devices located in proximity to entities that generate, store, supply, 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 drive solutions to specific stakeholder needs. As an example, a city may utilize the set of mobility demand solutions 152 to predict peak travel times and adjust public transportation schedules accordingly. Similarly, for a large corporate campus, a set of enterprise optimization solutions 154 may be used to manage energy consumption to ensure that office buildings are adequately powered during working hours while conserving energy during non-working hours. Enterprise Optimization Solutions

[0561] In embodiments, DERs 128 are integrated with enterprise and shared resources to augment the existing power grid, helping to reduce costs and improve reliability. Increasing levels of digitalization help facilitate new ways to integrate activities and optimize energy across buildings / businesses, campuses, and enterprises. For example, integrating 128 DERs allows a campus to supplement its power needs with renewable energy. Digitizing energy management allows a campus to monitor and adjust energy consumption in real time. In embodiments, this may enable a commercial enterprise to increase its operational revenue by leveraging big data and plug load analysis to efficiently manage buildings. For example, a campus can efficiently manage buildings, ensure energy is used where it is needed, and optimize operational costs.

[0562] In embodiments, IoT sensors and building automation control systems can be configured to help optimize floor space, identify unused equipment, automate efficient energy consumption, improve safety, and reduce a building's environmental impact. As an 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 and ensure that lighting and HVAC systems are optimized according to the number of occupants. For example, unused conference rooms can automatically turn off lights and adjust temperatures to reduce energy waste.

[0563] In embodiments, the platform 102 can manage the total energy consumption of systems and devices connected to an electrical network or set of DERs 128. Some systems may operate nearly constantly, while other devices and machines may only be connected occasionally. By understanding both the total daily electrical consumption of a building and the role that individual devices play in the overall energy use of a particular system, the platform 102 can, optionally through AI or algorithms, predict, provide, manage, and control the total consumption. For example, through AI and algorithms, the platform 102 can monitor and adjust energy consumption based on the specific needs of each building to optimize energy use.

[0564] In embodiments, the platform 102 can track and leverage an understanding of resident behavior. Resident activity levels, behavioral patterns, and comfort preferences can be considerations for energy efficiency measures. This may include tracking various cyclical or seasonal factors. Over time, a building's energy generation, storage, and / or consumption may follow predictable patterns, which the IoT-based analytics platform can take into account when generating proposed solutions. For example, if the platform notices that residents tend to stay home until the evening during the winter, it can adjust heating accordingly. Over time, the system learns from these patterns, ensuring energy is used efficiently.

[0565] In embodiments, platform 102 enables or can integrate with systems or platforms for autonomous operation. For example, industrial sites such as oil rigs and power plants require extensive monitoring for efficiency and safety, as liquid, steam, or oil leaks can lead to catastrophe, costs, and waste. AI and machine learning may provide autonomous capabilities to power plants, such as those provided by edge devices, IoT devices, and on-site cameras and sensors. Models may be deployed at the power plant edge or DERs 128 to use real-time inference and pattern detection to identify faults such as electrical leakage, shaking, and stress. Operators can use computer vision, deep learning, and intelligent video analytics (IVA) to monitor heavy equipment, detect potential hazards, and provide real-time alerts to protect worker health and safety, prevent accidents, and assign repair technicians for maintenance. As an example, in a factory with multiple machines, platform 102 can use AI and machine learning to monitor machine health in real time, predict potential weaknesses, and suggest timely maintenance or repairs.

[0566] In embodiments, the platform 102 enables or can integrate with systems or platforms for pipeline optimization. For example, oil and gas companies may depend on finding optimal routes for transporting oil to refineries and ultimately fuel stations. Edge AI can calculate optimal oil flows to ensure production reliability and protect long-term pipeline health. In embodiments, companies can inspect pipelines for defects that could lead to dangerous failures and automatically alert pipeline operators. Energy Supply and Governance Solutions

[0567] Energy Supply and Governance Solutions156 may include solutions for the governance of mining operations. Cobalt, nickel, and other metals are fundamental components of the batteries needed for the green electric vehicle revolution. The quantities needed to support an expanding market create economic pressures on mining operations, many of which occur in regions like the Democratic Republic of the Congo, which have a long history of corruption, child labor, and violence. Companies are developing areas like Greenland for cobalt mining, in part because they can rely on reliable labor law enforcement, tax compliance, and more. These commitments can be made with greater confidence, locally and in other jurisdictions, through the Mining Governance Solutions Suite542. The suite of mining governance solutions542 includes mine-level IoT sensing of the mine environment, ground-penetrating sensing of unmined portions, mass spectrometry and computer vision-based sensing of mined materials, asset tagging of smart containers (e.g., detecting and recording opening and closing events to ensure material placed in a container is the same material delivered at the end point), wearable devices to detect the physiological state of miners, secure (e.g., blockchain and DLT-based) recording and resolution of transactions and transaction-related events, smart contracts for automatic revenue allocation (to tax authorities, labor, etc.), and automated systems for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements. All of the above, from base sensors to compliance reports, can optionally be represented in a digital twin representing each mine's owner or operated by the company.

[0568] The energy supply and governance solutions 156 may also include a set of carbon-aware energy solutions in which control of carbon-producing (or capturing) operators is managed through data collection via edge and IoT devices regarding current carbon generation or emission conditions, and through the automated generation of a set of recommendations and / or control instructions to control operators to meet policy, such as maintaining operations within a range offset by available carbon offset credits.

[0569] Details of the various energy supply and governance solutions156 are below. Local Production Solutions

[0570] In an embodiment, a set of localized production solutions 158 can be integrated with, linked to, or managed by platform 102 to meet localized production demands, particularly for goods that are very costly to transport (e.g., food) or services where the cost of energy distribution significantly negatively impacts the margins of the product or service (e.g., requiring intensive computing in locations where the electrical grid is nonexistent, lacks capacity, is unreliable, or is too costly). Platform 102 can manage the energy consumption of the set of localized production solutions 158 and optimize their usage based on available resources, particularly in locations where a traditional electrical grid may not exist or may be unreliable.

[0571] In embodiments, the power management system may converge with other systems, such as building management systems, operations management systems, production systems, service systems, data centers, etc., to enable enterprise-wide energy management. By converging power management with building management systems, operations management systems, production systems, service systems, data centers, etc., the platform 102 can ensure optimal energy use across the enterprise. For example, during off-hours, the building management system can r...

Claims

1. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: an adaptive energy data pipeline configured to communicate data between a set of nodes in a network; each node of the set of nodes is adapted to operate on an energy data set relating to at least one of energy generation, energy storage, energy supply, or energy consumption; and wherein at least one node of the set of nodes is configured to filter, compress, transform, error correct, and / or route at least a portion of the energy data set based on at least one of a set of network conditions, data size, data granularity, or data content using one or both of an algorithm and a set of rules.

2. The adaptive energy data pipeline is further configured to adapt transport of data over a network and / or communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market factors conditions, and User-defined conditions, 2. The AI-based platform of claim 1, wherein the AI-based platform is based on one or more of the following:

3. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 10. The AI-based platform of claim 1, further comprising an adaptive energy digital twin representing one or more of:

4. providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; Highlighting energy data, or adjusting energy data; 10. The AI-based platform of claim 1, further comprising an adaptive energy digital twin configured to perform one or more of:

5. one or more machines; one or more factories; or one or more vehicles in a vehicle fleet; 10. The AI-based platform of claim 1, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by one or more of:

6. The adaptive energy data pipeline further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 10. The AI-based platform of claim 1, configured to execute one or more of:

7. The energy data set is based on one or more public data resources, the public data resources including: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, 10. The AI-based platform of claim 1, comprising one or more of:

8. The energy data set is based on one or more enterprise data resources, the enterprise data resources comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 10. The AI-based platform of claim 1, comprising one or more of:

9. and 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, the training dataset comprising: one or more human tags and / or labels; one or more human interactions with a hardware and / or software system; one or more results, one or more AI-generated training data samples; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 2. The AI-based platform of claim 1, wherein the AI-based platform is based on one or more of:

10. At least one node of the set of nodes is configured to orchestrate delivery of energy to one or more consumption points, the delivery of energy comprising: one or more fixed transmission lines; one or more instances of wireless energy transmission; one or more deliveries of fuel; or one or more deliveries of the stored energy; 10. The AI-based platform of claim 1, comprising one or more of:

11. At least one node of the set of nodes is further configured to record, in a distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 10. The AI-based platform of claim 1, comprising one or more of:

12. At least one node of the set of nodes is located in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 10. The AI-based platform of claim 1, comprising one or more of:

13. The adaptive energy data pipeline further comprises: an overall energy consumption by at least a portion of the set of nodes; a role of at least one node of the set of nodes in the overall energy consumption by at least a portion of the set of nodes; and Based on said monitoring, managing energy consumption by the set of nodes; predicting energy consumption by said set of nodes; or [0033] provisioning resources related to energy consumption by the set of nodes; 2. The AI-based platform of claim 1, configured to:

14. 2. The AI-based platform of claim 1, wherein the set of nodes in the network that includes the adaptive energy data pipeline includes a set of edge networking devices that control at least one of energy consumption, energy storage, energy supply, or energy consumption by a set of operating devices controlled via the edge networking devices.

15. 10. The AI-based platform of claim 1, wherein the adaptive energy data pipeline is further configured to automatically select a least-cost path for data communicated between the set of nodes, the selection being based on low-priority energy usage associated with the data.

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

17. 10. The AI-based platform of claim 1, wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities configured to adapt the pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs.

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

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

20. 10. The AI-based platform of claim 1, wherein the adaptive energy data pipeline is configured to perform enterprise context adaptation by automatically processing data based on one or more of an enterprise operating context, an enterprise transactional context, or an enterprise financial context.

21. 10. The AI-based platform of claim 1, wherein at least one node of the set of nodes is further configured to coordinate communication with at least one other node of the set of nodes to accommodate reporting of data related to at least one of energy generation, energy storage, energy supply, or energy consumption to the at least one other node.

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

23. 2. The AI-based platform of claim 1, wherein the set of nodes comprises a heterogeneous set including at least one energy producer and at least one energy consumer, and the adaptive energy data pipeline is further configured to direct one or both of the at least one energy producer and the at least one energy consumer to communicate with at least one other node in the set of nodes over at least one communication path.

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

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

26. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising: a set of adaptive and autonomous data processing systems, each of said adaptive and autonomous data processing systems configured to collect data related to energy generation, storage, or supply from a set of edge devices responsible for operational control of a set of distributed energy resources, and configured to autonomously adjust a set of operational parameters for such operational control based on the collected data.

27. Each of the adaptive and autonomous data processing systems is further configured to adapt transport of data over a network and / or communication system, said adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 27. The AI-based platform of claim 26, wherein the AI-based platform is based on one or more of:

28. Each of said adaptive and autonomous data processing systems comprises: Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 27. The AI-based platform of claim 26, comprising an adaptive energy digital twin representing one or more of:

29. Each of said adaptive and autonomous data processing systems comprises: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; Highlighting energy data, or adjusting energy data; 27. The AI-based platform of claim 26, further comprising an adaptive energy digital twin configured to perform one or more of:

30. Each of said adaptive and autonomous data processing systems comprises: one or more machines; one or more factories; or one or more vehicles in a vehicle fleet; 27. The AI-based platform of claim 26, comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by one or more of:

31. Each of the adaptive and autonomous data processing systems further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 27. The AI-based platform of claim 26, configured to execute one or more of:

32. The energy edge data is based on one or more public data resources, the public data resources including: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, 27. The AI-based platform of claim 26, comprising one or more of:

33. The energy edge data is based on one or more enterprise data resources, the enterprise data resources comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 27. The AI-based platform of claim 26, comprising one or more of:

34. and 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, the training dataset comprising: one or more human tags and / or labels; one or more human interactions with a hardware and / or software system; one or more results, one or more AI-generated training data samples; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 27. The AI-based platform of claim 26, wherein the AI-based platform is based on one or more of:

35. Each of the adaptive and autonomous data processing systems is further configured to orchestrate delivery of energy to one or more points of consumption, the delivery of energy comprising: one or more fixed transmission lines; one or more instances of wireless energy transmission; one or more deliveries of fuel; or one or more deliveries of the stored energy; 27. The AI-based platform of claim 26, comprising one or more of:

36. Each of the adaptive and autonomous data processing systems 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: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 27. The AI-based platform of claim 26, comprising one or more of:

37. At least one of the adaptive and autonomous data processing systems is located in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 27. The AI-based platform of claim 26, comprising one or more of:

38. 27. The AI-based platform of claim 26, wherein the platform further comprises an adaptive energy data pipeline configured to communicate data between a set of nodes in a network.

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

40. 39. The AI-based platform of claim 38, wherein the adaptive energy data pipeline is further configured to automatically select a least-cost path for data communicated between the set of nodes, the selection being based on low-priority energy usage associated with the data.

41. 39. The AI-based platform of claim 38, wherein the adaptive energy data pipeline is further configured to automatically select a high quality of service route for data communicated between the set of nodes, the selection being based on high priority energy usage associated with the data.

42. 39. The AI-based platform of claim 38, wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities configured to adapt the pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs.

43. 39. The AI-based platform of claim 38, wherein the adaptive energy data pipeline includes a self-organizing data storage configured to store data on a device based on one or more of a pattern of the data, a content of the data, or a context of the data.

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

45. 40. The AI-based platform of claim 38, wherein the adaptive energy data pipeline is configured to perform enterprise context adaptation by automatically processing data based on one or more of an enterprise operating context, an enterprise transactional context, or an enterprise financial context.

46. 27. The AI-based platform of claim 26, wherein at least one of the adaptive and autonomous data processing systems is further configured to determine a schedule for a set of processes based on at least one priority and / or need associated with a set of distributed energy resources.

47. 27. The AI-based platform of claim 26, wherein at least one of the adaptive and autonomous data processing systems is further configured to coordinate communication with at least one edge device of the set of edge devices based on at least one priority and / or need associated with a set of distributed energy resources, the communication related to investigating energy generation, storage, or delivery by the distributed energy resources.

48. 27. The AI-based platform of claim 26, wherein at least one of the adaptive and autonomous data processing systems is further configured to issue instructions to at least one edge device of the set of edge devices, the instructions being based on an examination of energy generation, storage, or delivery by the distributed energy resource, and the instructions causing the at least one edge device to coordinate energy generation, storage, or delivery by the at least one edge device.

49. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising: a system configured to perform automated and coordinated governance of a set of functionally coupled energy entities within an energy grid and a set of distributed edge energy resources, wherein at least one of the distributed edge energy resources is functionally independent from the energy grid.

50. The system is further configured to adapt transport of data over a network and / or communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market factors conditions, and User-defined conditions, 50. The AI-based platform of claim 49, wherein the AI-based platform is based on one or more of:

51. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 50. The AI-based platform of claim 49, further comprising an adaptive energy digital twin representing one or more of:

52. providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; Highlighting energy data, or adjusting energy data; 50. The AI-based platform of claim 49, further comprising an adaptive energy digital twin configured to perform one or more of:

53. one or more machines; one or more factories; or one or more vehicles in a vehicle fleet; 50. The AI-based platform of claim 49, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by one or more of:

54. The system further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 50. The AI-based platform of claim 49, configured to execute one or more of:

55. and 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, the training dataset comprising: one or more human tags and / or labels; one or more human interactions with a hardware and / or software system; one or more results, one or more AI-generated training data samples; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 50. The AI-based platform of claim 49, wherein the AI-based platform is based on one or more of:

56. The system is further configured to orchestrate delivery of energy to one or more points of consumption, the delivery of energy comprising: one or more fixed transmission lines; one or more instances of wireless energy transmission; one or more deliveries of fuel; or one or more deliveries of the stored energy; 50. The AI-based platform of claim 49, comprising one or more of:

57. The 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 50. The AI-based platform of claim 49, comprising one or more of:

58. At least one of the distributed energy edge resources is located in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 50. The AI-based platform of claim 49, comprising one or more of:

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

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

61. 60. The AI-based platform of claim 59, wherein the system includes a set of carbon-aware energy edge solutions, the solutions including discovering, configuring, and enforcing a set of policies regarding carbon generation.

62. 62. The AI-based platform of claim 61, wherein the solution involves monitoring energy production by a mining environment to track carbon emissions generated by the mining environment.

63. 62. The AI-based platform of claim 61, wherein the solution calls for offsetting carbon emissions from an extractive environment against energy production from the extractive environment.

64. 60. The AI-based platform of claim 59, wherein the platform includes a user interface, and the system includes a set of automated energy policy deployment solutions, the solutions being configurable via user interaction with the user interface.

65. 60. The AI-based platform of claim 59, wherein the system includes an intelligent agent trained to generate policies related to governance of the mining environment, the intelligent agent being trained based on a training set of historical data, feedback from outcomes, and human policy setting interactions.

66. The system comprises: Setting a maximum energy usage for an entity over a period of time; Setting the maximum energy cost of an entity for a period of time; Setting a maximum carbon production amount for an entity over a period of time; setting the maximum pollution emissions of an entity for a period of time; setting carbon offset requirements; establishing renewable energy credit requirements; setting energy mix requirements; Setting a floor for profit margins based on the energy costs and other marginal costs of the producing entity; or establishing a minimum storage baseline for energy storage entities; 60. The AI-based platform of claim 59, further facilitating governance of the mining environment by enforcing policies including one or more of:

67. 60. The AI-based platform of claim 59, wherein the system includes a set of energy governance smart contract solutions configured to enable users of the platform to design, generate, and deploy smart contracts that automatically provide a range of degrees of governance for energy transactions.

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

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

70. 50. The AI-based platform of claim 49, wherein the system is further configured to perform monitoring of production rates of energy by the set of energy entities and to adjust the automated and coordinated governance of the set of energy entities based on the monitoring of the production rates.

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

72. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising: an adaptive energy data pipeline configured to communicate data between a set of nodes in a network, wherein at least a subset of the set of nodes is configured to set at least one parameter of data communication associated with the adaptive energy data pipeline according to at least one of a rule or algorithm, the at least one parameter being based on a set of indicators of current network conditions to optimize energy used in the data communication.

73. The at least one parameter is: Routing instructions, Route parameters, Error correction parameters, compression parameters, Storage parameters, or timing parameters, 73. The AI-based platform of claim 72, wherein the AI-based platform is one or more of:

74. The adaptive energy data pipeline is further configured to adapt transport of data over a network and / or communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 73. The AI-based platform of claim 72, wherein the AI-based platform is based on one or more of:

75. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 73. The AI-based platform of claim 72, further comprising an adaptive energy digital twin representing one or more of:

76. providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; Highlighting energy data, or adjusting energy data; 73. The AI-based platform of claim 72, further comprising an adaptive energy digital twin configured to perform one or more of:

77. one or more machines; one or more factories; or one or more vehicles in a vehicle fleet; 73. The AI-based platform of claim 72, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by one or more of:

78. The adaptive energy data pipeline further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 73. The AI-based platform of claim 72, configured to execute one or more of:

79. The data is based on one or more public data resources, the public data resources including: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, 73. The AI-based platform of claim 72, comprising one or more of:

80. The data is based on one or more enterprise data resources, the enterprise data resources including: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 73. The AI-based platform of claim 72, comprising one or more of:

81. and 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, the training dataset comprising: one or more human tags and / or labels; one or more human interactions with a hardware and / or software system; one or more results, one or more AI-generated training data samples; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 73. The AI-based platform of claim 72, wherein the AI-based platform is based on one or more of:

82. The adaptive energy data pipeline is further configured to orchestrate delivery of energy to one or more points of consumption, the delivery of energy comprising: one or more fixed transmission lines; one or more instances of wireless energy transmission; one or more deliveries of fuel; or one or more deliveries of the stored energy; 73. The AI-based platform of claim 72, comprising one or more of:

83. The adaptive energy data pipeline 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: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 73. The AI-based platform of claim 72, comprising one or more of:

84. At least a portion of the adaptive energy data pipeline is deployed in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 73. The AI-based platform of claim 72, comprising one or more of:

85. The adaptive energy data pipeline further comprises: an overall energy consumption by at least a portion of the set of nodes; a role of at least one node of the set of nodes in the overall energy consumption by at least a portion of the set of nodes; and Based on said monitoring, managing energy consumption by the set of nodes; predicting energy consumption by said set of nodes; or providing resources related to energy consumption by the set of nodes; 73. The AI-based platform of claim 72, configured to:

86. 73. The AI-based platform of claim 72, wherein the set of nodes in the network that includes the adaptive energy data pipeline includes a set of edge networking devices that control at least one of energy consumption, energy storage, energy supply, or energy consumption by a set of operating devices controlled via the edge networking devices.

87. 73. The AI ​​based platform of claim 72, wherein the adaptive energy data pipeline is further configured to automatically select a least cost path for data communicated between the set of nodes, the selection being based on low priority energy usage associated with the data.

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

89. 73. The AI-based platform of claim 72, wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities configured to adapt the pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs.

90. 73. The AI-based platform of claim 72, wherein the adaptive energy data pipeline includes self-organizing data storage configured to store data on a device based on one or more of a pattern of the data, a content of the data, or a context of the data.

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

92. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising: a digital twin system having a digital twin of a mining environment, the digital twin including at least one parameter sensed by a sensor in the mining environment.

93. The at least one parameter is: an unmined portion of said mined environment; mining materials from said mining environment; a smart container event involving a smart container associated with the mining environment; the physiological state of the miner in relation to the mining environment; Transaction-related events related to the mining environment; or compliance of said mining environment with one or more contracts, regulations, and / or legal policies; 93. The AI-based platform of claim 92, wherein the AI-based platform is associated with one or more of:

94. The digital twin system further comprises: Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 93. The AI-based platform of claim 92, wherein the AI-based platform represents one or more of:

95. The digital twin system further comprises: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; Highlighting energy data, or adjusting energy data; 93. The AI-based platform of claim 92, configured to execute one or more of:

96. The digital twin system further comprises: one or more machines; one or more factories; or one or more vehicles in a vehicle fleet; 93. The AI-based platform of claim 92, configured to generate visual and / or analytical indicators of energy consumption by one or more of:

97. The parameters are based on one or more public data resources, the public data resources including: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, 93. The AI-based platform of claim 92, comprising one or more of:

98. The parameters are based on one or more enterprise data resources, the enterprise data resources including: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 93. The AI-based platform of claim 92, comprising one or more of:

99. The digital twin system includes at least one AI-based model and / or algorithm, and the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset comprising: one or more human tags and / or labels; one or more human interactions with a hardware and / or software system; one or more results, one or more AI-generated training data samples; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 93. The AI-based platform of claim 92, wherein the AI-based platform is based on one or more of:

100. The digital twin system is further configured to orchestrate delivery of energy to one or more consumption points, the delivery of energy comprising: one or more fixed transmission lines; one or more instances of wireless energy transmission; one or more deliveries of fuel; or one or more deliveries of the stored energy; 93. The AI-based platform of claim 92, comprising one or more of:

101. The digital twin 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 93. The AI-based platform of claim 92, comprising one or more of:

102. The digital twin system is placed in an off-grid environment, and the off-grid environment includes: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 93. The AI-based platform of claim 92, comprising one or more of:

103. 93. The AI-based platform of claim 92, wherein the mining environment is a data mining environment.

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

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

106. 93. The AI-based platform of claim 92, wherein the platform includes a set of carbon-aware energy edge solutions, the solutions including discovering, configuring, and enforcing a set of policies regarding carbon generation.

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

108. 107. The AI-based platform of claim 106, wherein the solution calls for offsetting carbon emissions from the extractive environment against energy production from the extractive environment.

109. 93. The AI-based platform of claim 92, wherein the platform includes a user interface, the platform includes a set of automated energy policy deployment solutions, the solutions being configurable via user interaction with the user interface.

110. 93. The AI-based platform of claim 92, wherein the platform includes an intelligent agent trained to generate policies related to governance of the mining environment, the intelligent agent being trained based on a training set of historical data, feedback from outcomes, and human policy setting interactions.

111. The platform comprises: Setting a maximum energy usage for an entity over a period of time; Setting the maximum energy cost of an entity for a period of time; Setting a maximum carbon production amount for an entity over a period of time; setting the maximum pollution emissions of an entity for a period of time; setting carbon offset requirements; establishing renewable energy credit requirements; setting energy mix requirements; Setting a floor for profit margins based on the energy costs and other marginal costs of the producing entity; or establishing a minimum storage baseline for energy storage entities; 93. The AI-based platform of claim 92, further facilitating governance of the mining environment by enforcing policies including one or more of:

112. 93. The AI-based platform of claim 92, wherein the at least one parameter includes a measurement by the sensor, the measurement associated with at least one piece of equipment involved in industrial operations of the mining environment.

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

114. 93. The AI-based platform of claim 92, wherein the at least one parameter included in the digital twin comprises at least one characteristic of at least one dataset related to the mining environment.

115. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: a governance system for mining operations; a reporting system for communicating at least one parameter sensed by a sensor in a mine of the mining operation, the at least one parameter related to compliance of the mining operation with a set of labor standards; An AI-based platform comprising:

116. The reporting system is further configured to adapt transport of data over a network and / or a communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 116. The AI-based platform of claim 115, wherein the AI-based platform is based on one or more of:

117. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 116. The AI-based platform of claim 115, further comprising an adaptive energy digital twin representing one or more of:

118. providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; highlighting energy data; Adjusting energy data, or generating a visual and / or analytical indicator of energy consumption by one or more of: one or more machines, one or more plants, or one or more vehicles in a vehicle fleet; 116. The AI-based platform of claim 115, further comprising an adaptive energy digital twin configured to perform one or more of the following:

119. The reporting system further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 116. The AI-based platform of claim 115, configured to perform one or more of the following:

120. The reporting system is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, the one or more energy-related events comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 116. The AI-based platform of claim 115, comprising one or more of:

121. At least one of the at least one parameter is: based on one or more public data resources and one or more enterprise data resources; The one or more public data resources: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, [0033] The one or more enterprise data resources: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 116. The AI-based platform of claim 115, comprising one or more of:

122. and 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, the training dataset comprising: one or more human tags and / or labels; one or more human interactions with a hardware and / or software system; one or more results, one or more AI-generated training data samples; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 116. The AI-based platform of claim 115, wherein the AI-based platform is based on one or more of:

123. The governance system is further configured to orchestrate delivery of energy to one or more consumption points, the delivery of energy comprising: one or more fixed transmission lines; one or more instances of wireless energy transmission; one or more deliveries of fuel; or one or more deliveries of the stored energy; 116. The AI-based platform of claim 115, comprising one or more of:

124. 116. The AI-based platform of claim 115, wherein the set of labor standards is associated with at least one activity performed by workers at the mine, and wherein communicating the at least one parameter sensed by the sensor includes communicating instructions for performance of the at least one activity by the workers as sensed by the sensor.

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

126. 116. The AI-based platform of claim 115, wherein the set of labor criteria includes thresholds for characteristics of the mine, and the reporting system is further configured to communicate a decision based on a comparison of the at least one parameter sensed by the sensor to the thresholds.

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

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

129. 116. The AI-based platform of claim 115, further comprising a sensor configuration system configured to determine a configuration of the sensor for performing sensing of the at least one parameter, the configuration being based on the compliance of the mining operation with the set of labor standards.

130. 130. The AI-based platform of claim 129, wherein the set of labor criteria is accessible to the sensor configuration system and is specified in natural language, and the sensor configuration system is configured to determine the configuration of the sensor based on natural language analysis of the set of labor criteria.

131. and a sensor remediation system configured to perform at least one sensor remedial action based on a determination of a failure of the sensor sensing the at least one parameter, the at least one sensor remedial action comprising: initiating replacement of the sensor; initiating a diagnostic operation involving said sensor; initiating a reconfiguration of the sensor to sense the at least one parameter in a different manner; initiating a request to the mine worker to perform manual sensing of the at least one parameter; or initiating substitution of the sensor of the mine with at least one other sensor of the mine for sensing the at least one parameter; 116. The AI-based platform of claim 115, comprising one or more of:

132. and a compliance verification system configured to verify that the at least one parameter sensed by the sensor is indicative of compliance of the mining operation with the set of labor standards, the verifying comprising: verifying the calibration of the sensors at the mine; validating the at least one parameter sensed by the sensor at the mine based on a comparison with at least one parameter sensed by at least one other sensor at the mine; requiring manual verification of said at least one parameter by said mine workers; or requiring verification by a compliance officer that the at least one parameter indicates the compliance of the mining operation with the set of labor standards; 116. The AI-based platform of claim 115, comprising one or more of:

133. 116. The AI-based platform of claim 115, further comprising a worker communication interface configured to communicate with workers at the mine based on the at least one parameter sensed by the sensor, the communication relating to the compliance of the mining operation with the set of labor standards.

134. 116. The AI-based platform of claim 115, further comprising a user interface configured to display a map of the mining operation, the map including an indication of compliance of the mining operation with the set of labor standards based on the at least one parameter sensed by the sensor.

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

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

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

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

139. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising: a set of edge devices, each edge device of the set of edge devices configured to maintain awareness of carbon generation and / or emissions of at least one entity of a set of energy using entities linked to and / or managed by the set of edge devices.

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

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

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

143. At least one edge device of the set of edge devices is further configured to adapt transport of data over a network and / or communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 140. The AI-based platform of claim 139, wherein the AI-based platform is based on one or more of:

144. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 140. The AI-based platform of claim 139, further comprising an adaptive energy digital twin representing one or more of:

145. providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; highlighting energy data; Adjusting energy data, or generating a visual and / or analytical indicator of energy consumption by one or more of: one or more machines, one or more plants, or one or more vehicles in a vehicle fleet; 140. The AI-based platform of claim 139, further comprising an adaptive energy digital twin configured to perform one or more of the following:

146. At least one edge device of the set of edge devices further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 140. The AI-based platform of claim 139, configured to perform one or more of the following:

147. At least one edge device of the set of edge devices further includes at least one AI-based model and / or algorithm, wherein the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset comprising: one or more human tags and / or labels; one or more human interactions with a hardware and / or software system; one or more results, one or more AI-generated training data samples; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 140. The AI-based platform of claim 139, wherein the AI-based platform is based on one or more of:

148. At least one edge device of the set of edge devices is configured to orchestrate delivery of energy to one or more consumption points, the delivery of energy comprising: one or more fixed transmission lines; one or more instances of wireless energy transmission; one or more deliveries of fuel; or one or more deliveries of the stored energy; 140. The AI-based platform of claim 139, comprising one or more of:

149. At least one edge device of the set of edge devices 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: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 140. The AI-based platform of claim 139, comprising one or more of:

150. At least one edge device of the set of edge devices is located in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 140. The AI-based platform of claim 139, comprising one or more of:

151. 140. The AI-based platform of claim 139, wherein at least one edge device of the set of edge devices is further configured to measure changes in the carbon production and / or emissions over a period of time based on a comparison of current metrics of the carbon production and / or emissions to historical metrics of the carbon production and / or emissions.

152. 140. The AI-based platform of claim 139, wherein at least one edge device of the set of edge devices is further configured to determine a target for the carbon generation and / or emissions based on a policy for the carbon generation and / or emissions.

153. At least one edge device of the set of edge devices further comprises: performing a comparison of said carbon generation and / or emission metrics with said carbon generation and / or emission targets; determining whether the carbon production and / or emissions comply with the carbon production and / or emissions policy based on the comparison; 140. The AI-based platform of claim 139, configured to:

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

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

156. At least one edge device of the set of edge devices is further configured to associate at least one indicator for associating the carbon generation and / or emission metric with the carbon generation and / or emission target, the indicator comprising: the date and / or duration of said carbon production and / or emission; the location of the source of said carbon generation and / or emissions; the direction and / or speed of transport of the carbon production and / or discharge; Locations affected by said carbon generation and / or emissions; a physical metric of said carbon production and / or emissions; the chemical components of said carbon production and / or emissions; weather patterns occurring in the region associated with said carbon production and / or emissions; the wildlife populations in the area associated with said carbon production and / or emissions; or human activities that are affected by said carbon production and / or emissions; 140. The AI-based platform of claim 139, comprising one or more of:

157. 140. The AI-based platform of claim 139, wherein at least one edge device of the set of edge devices is further configured to send an alert related to the carbon generation and / or emissions based on a comparison of the carbon generation and / or emissions metrics to an alert threshold related to the carbon generation and / or emissions.

158. 140. The AI-based platform of claim 139, wherein at least one edge device of the set of edge devices is further configured to adjust activity related to the carbon generation and / or emissions based on the carbon generation and / or emissions metrics, the adjustment modifying a future state of the carbon generation and / or emissions.

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

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

161. 140. The AI-based platform of claim 139, wherein at least one edge device of the set of edge devices is further configured to modify operation of one or more pieces of equipment and / or processes associated with the at least one entity of the set of energy using entities, wherein modifying the operation is based on at least one measurement of carbon generation and / or emissions associated with the at least one entity of the set of energy using entities.

162. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising: a digital twin updated by a data collection system that dynamically maintains a set of historical, current, and / or forecasted 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 the set of energy demand parameters.

163. A set of operational entities is controlled via a set of edge networking devices linked to the set of operational entities, and the energy demand parameter is a current set of aggregated data derived from demand from the set of operational entities, the set of operational entities being controlled via a set of edge networking devices linked to the set of operational entities; a historical set of aggregated data derived from demand from the set of operational entities, the set of operational entities being controlled via a set of edge networking devices linked to the set of operational entities; or a simulated set of aggregate data derived from demand from said set of operational entities; 163. The AI-based platform of claim 162, wherein the AI-based platform is based on one or more of:

164. The data collection system is further configured to adapt transport of data over a network and / or a communication system, the adapting comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 163. The AI-based platform of claim 162, wherein the AI-based platform is based on one or more of:

165. The digital twin is: Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 163. The AI-based platform of claim 162, wherein the AI-based platform represents one or more of:

166. The digital twin further comprises: providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; highlighting energy data; Adjusting energy data, or generating a visual and / or analytical indicator of energy consumption by one or more of: one or more machines, one or more plants, or one or more vehicles in a vehicle fleet; 163. The AI-based platform of claim 162, configured to execute one or more of:

167. At least one of the energy demand parameters is: based on one or more public data resources and one or more enterprise data resources; The one or more public data resources: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, [0033] The one or more enterprise data resources: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 163. The AI-based platform of claim 162, comprising one or more of:

168. The digital twin includes at least one AI-based model and / or algorithm, and the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset comprising: one or more human tags and / or labels; one or more human interactions with a hardware and / or software system; one or more results, one or more AI-generated training data samples; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 163. The AI-based platform of claim 162, wherein the AI-based platform is based on one or more of:

169. The digital twin is further configured to orchestrate delivery of energy to one or more consumption points, the delivery of energy comprising: one or more fixed transmission lines; one or more instances of wireless energy transmission; one or more deliveries of fuel; or one or more deliveries of the stored energy; 163. The AI-based platform of claim 162, comprising one or more of:

170. 170. The AI-based platform of claim 169, wherein the digital twin is further configured to adjust the delivery of energy to the one or more consumption points based on energy delivery and / or consumption policies.

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

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

173. The digital twin is further configured to determine the delivery of energy to the one or more consumption points based on a comparison of energy availability at each of two or more energy sources, the comparison comprising: the amount of current and / or future energy stored by at least one of the two or more energy sources; current and / or future resource expenditures associated with the acquisition, storage, and / or supply of energy by at least one of the two or more energy sources; or current and / or future demand for energy from at least one of the two or more energy sources by other energy consumers; 170. The AI-based platform of claim 169, comprising one or more of:

174. The digital twin is further configured to record, in a distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 163. The AI-based platform of claim 162, comprising one or more of:

175. The digital twin is deployed in an off-grid environment, and the off-grid environment includes: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 163. The AI-based platform of claim 162, comprising one or more of:

176. 163. The AI-based platform of claim 162, wherein the AI-based platform is configured to measure the performance of the digital twin based on a prediction delta, the prediction delta being based on a comparison of a prediction generated by the digital twin based on the set of energy demand parameters with measurements in the data collection system corresponding to the prediction.

177. The AI-based platform is configured to update the digital twin based on the prediction delta, the update comprising: retraining the digital twin based on the prediction delta; adjusting a prediction correction applied to the digital twin prediction based on the prediction delta; Complementing the digital twin with at least one other trained machine learning model; or replacing the digital twin with an alternative digital twin; 177. The AI-based platform of claim 176, comprising one or more of:

178. The digital twin is: a forecast based on at least one of the energy demand parameters; an indication of the impact of at least one of the energy demand parameters on the forecast; and 163. The AI-based platform of claim 162, further configured to generate:

179. The digital twin is further configured to determine one or more modifications of the set of energy demand parameters to improve future predictions of the digital twin, the one or more modifications comprising: one or more additional historical, current, and / or forecasted energy demand parameters associated with the set of fixed entities and the set of mobile entities within the defined domain; or one or more modifications of one or more historical, current, and / or forecasted energy demand parameters associated with the set of fixed entities and the set of mobile entities within the defined domain; 163. The AI-based platform of claim 162, comprising one or more of:

180. The digital twin is further configured to orchestrate the supply of energy to one or more consumption points based on one or more entity parameters received from at least one entity of the set of fixed entities and / or the set of mobile entities within the defined domain, wherein the one or more entity parameters include: the current and / or future energy state of said at least one entity; current and / or future energy consumption by said at least one entity; or current and / or future activities performed by said at least one entity related to energy consumption; 163. The AI-based platform of claim 162, comprising one or more of:

181. The digital twin is further configured to send a request to at least one entity of the set of fixed entities and / or the set of mobile entities within the defined domain to adjust one or more entity parameters associated with the at least one entity, the one or more entity parameters comprising: the current and / or future energy state of said at least one entity; current and / or future energy consumption by said at least one entity; or current and / or future activities performed by said at least one entity related to energy consumption; 163. The AI-based platform of claim 162, comprising one or more of:

182. The digital twin further comprises: performing a simulation of at least one process of at least one physical machine associated with one or both of the set of fixed entities or the set of mobile entities; outputting at least one energy demand parameter resulting from the at least one process based on the simulation; 163. The AI-based platform of claim 162, configured to:

183. 163. The AI-based platform of claim 162, wherein the digital twin is associated with at least one physical machine associated with one or both of the set of fixed entities or the set of mobile entities, and the digital twin is updated by the data collection system to generate process outputs corresponding to updated detections of the process outputs performed by the at least one physical machine.

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

185. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: An AI-based platform that includes a set of modular distributed energy systems that can be configured based on local demand requirements.

186. 186. The AI-based platform of claim 185, wherein the local demand requirements are predicted by a demand forecasting algorithm running on a set of edge networking devices linked to a set of energy consuming systems.

187. The AI-based platform of claim 185, wherein at least one of the set of modular distributed energy systems is configured by the AI-based platform to be located in proximity to the location and time of demand.

188. The AI-based platform of claim 185, wherein at least one of the set of modular distributed energy systems is configured by the AI-based platform to be deployed based on location and type of local demand requirements.

189. The AI-based platform of claim 185, wherein at least one of the set of modular distributed energy systems is configured by the AI-based platform to generate energy at the point of local demand.

190. The AI-based platform of claim 185, wherein at least one of the set of modular distributed energy systems is configured by the AI-based platform to deliver a modular power generation system to a location of demand.

191. The AI-based platform of claim 185, characterized in that at least one of the set of modular distributed energy systems is configured by the AI-based platform to route the supply of energy by the set of energy supply facilities to a location of demand.

192. The AI-based platform of claim 185, wherein at least one of the set of modular distributed energy systems is orchestrated by the AI-based platform to store energy in proximity to the location and time of demand.

193. At least one of the set of modular distributed energy systems is further configured to adapt transport of data over a network and / or communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 186. The AI-based platform of claim 185, wherein the AI-based platform is based on one or more of:

194. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 186. The AI-based platform of claim 185, further comprising an adaptive energy digital twin representing one or more of:

195. providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; highlighting energy data; Adjusting energy data, or generating a visual and / or analytical indicator of energy consumption by one or more of: one or more machines, one or more plants, or one or more vehicles in a vehicle fleet; 186. The AI-based platform of claim 185, further comprising an adaptive energy digital twin configured to perform one or more of the following:

196. At least one of the modular distributed energy systems further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 186. The AI-based platform of claim 185, configured to execute one or more of:

197. The local demand requirements are: based on one or more public data resources and one or more enterprise data resources; The one or more public data resources: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, [0033] The one or more enterprise data resources: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 186. The AI-based platform of claim 185, comprising one or more of:

198. and 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, the training dataset comprising: one or more human tags and / or labels; one or more human interactions with a hardware and / or software system; one or more results, one or more AI-generated training data samples; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 186. The AI-based platform of claim 185, wherein the AI-based platform is based on one or more of:

199. At least one of the modular distributed energy systems is further configured to orchestrate delivery of energy to one or more points of consumption, the delivery of energy comprising: one or more fixed transmission lines; one or more instances of wireless energy transmission; one or more deliveries of fuel; or one or more deliveries of the stored energy; 186. The AI-based platform of claim 185, comprising one or more of:

200. 200. The AI-based platform of claim 199, wherein a first system of the modular distributed energy system is configured to communicate with a second system of the modular distributed energy system to orchestrate the delivery of energy to the one or more consumption points by coordinating the generation, storage, supply, and / or consumption of energy by one or both of the first system or the second system.

201. 200. The AI-based platform of claim 199, wherein at least one of the modular distributed energy systems is configured to adjust the delivery of energy to the one or more consumption points based on carbon generation and / or emission policies.

202. At least one of the modular distributed energy systems 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: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 186. The AI-based platform of claim 185, comprising one or more of:

203. At least one of the modular distributed energy systems is deployed in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 186. The AI-based platform of claim 185, comprising one or more of:

204. 186. The AI-based platform of claim 185, wherein at least one of the modular distributed energy systems is associated with a digital twin configured to model and / or predict one or more characteristics and / or operation of the at least one of the modular distributed energy systems.

205. The AI-based platform of claim 185, wherein the set of modular distributed energy systems is configurable to vary the amount of reserve capacity to respond to patterns of energy demand related to the local demand requirements.

206. The AI-based platform of claim 185, characterized in that the set of modular distributed energy systems is configurable to relocate energy supply and / or access resources based on measurements and / or predictions of the local demand requirements.

207. 186. The AI-based platform of claim 185, wherein the set of modular distributed energy systems is configurable to alter energy production schedules based on measurements and / or forecasts of the local demand requirements.

208. 186. The AI-based platform of claim 185, wherein the set of modular distributed energy systems is configurable to change allocation of resources associated with the set of modular distributed energy systems, the allocation being based on a subset of the local demand requirements.

209. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: an artificial intelligence system, the artificial intelligence system comprising: performing an analysis of energy patterns associated with an operating process that includes a set of resources, the set of resources being at least partially independent from an electrical grid; and outputting a set of operating parameters for providing energy generation, storage, and / or consumption to enable the operating process, the set of operating parameters being based on the analysis; An AI-based platform configured to execute.

210. 210. The AI-based platform of claim 209, wherein at least one operating parameter of the set of operating parameters is a generation output level of a distributed energy generation resource.

211. 210. The AI-based platform of claim 209, wherein at least one operating parameter in the set of operating parameters is a target storage level for a distributed energy storage resource.

212. 210. The AI-based platform of claim 209, wherein at least one operational parameter of the set of operational parameters is the timing of supply of a distributed energy supply resource.

213. The artificial intelligence system is further configured to adapt transport of data over a network and / or a communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 210. The AI-based platform of claim 209, wherein the AI-based platform is based on one or more of:

214. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 210. The AI-based platform of claim 209, further comprising an adaptive energy digital twin representing one or more of:

215. providing a visual and / or analytical indication of energy consumption by one or more energy consumers; filtering the energy data; highlighting energy data; Adjusting energy data, or generating a visual and / or analytical indicator of energy consumption by one or more of: one or more machines, one or more plants, or one or more vehicles in a vehicle fleet; 210. The AI-based platform of claim 209, further comprising an adaptive energy digital twin configured to perform one or more of the following:

216. The artificial intelligence system further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 210. The AI-based platform of claim 209, configured to perform one or more of the following:

217. At least one of the operating parameters is based on one or more public data resources and one or more enterprise data resources; The one or more public data resources: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, [0033] The one or more enterprise data resources: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 210. The AI-based platform of claim 209, comprising one or more of:

218. The artificial intelligence system is trained based on a training data set, the training data set comprising: one or more human tags and / or labels; one or more human interactions with a hardware and / or software system; one or more results, one or more AI-generated training data samples; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 210. The AI-based platform of claim 209, wherein the AI-based platform is based on one or more of:

219. The artificial intelligence system is further configured to orchestrate delivery of energy to one or more points of consumption, the delivery of energy comprising: one or more fixed transmission lines; one or more instances of wireless energy transmission; one or more deliveries of fuel; or one or more deliveries of the stored energy; 210. The AI-based platform of claim 209, comprising one or more of:

220. The artificial intelligence system is further configured to record one or more energy-related events in a distributed ledger and / or blockchain, the one or more energy-related events comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 210. The AI-based platform of claim 209, comprising one or more of:

221. The artificial intelligence system is deployed in an off-grid environment, and the off-grid environment includes: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 210. The AI-based platform of claim 209, comprising one or more of:

222. 210. The AI-based platform of claim 209, wherein the artificial intelligence system is further configured to determine the environmental impact of carbon generation and / or emissions associated with the operational process on an area associated with the operational process.

223. The artificial intelligence system further comprises: carbon generation and / or emissions policies; or a set of labor standards associated with said operational process; 210. The AI-based platform of claim 209, configured to evaluate compliance of the operational process with one or both of:

224. The artificial intelligence system comprises: carbon generation and / or emissions policies; or a set of labor standards associated with said operational process; 210. The AI-based platform of claim 209, further configured to adjust the set of operational parameters to provide for energy generation, storage, and / or consumption associated with the operational process based on one or both of:

225. 210. The AI-based platform of claim 209, wherein the artificial intelligence system is further configured to send a message to at least one edge device of a set of edge devices associated with the operational process, the message including a request to adjust at least one operation of the at least one edge device based on the set of operational parameters.

226. 210. The AI-based platform of claim 209, wherein the artificial intelligence system is further configured to receive, from at least one edge device of a set of edge devices associated with the operational process, an indication of a current and / or predicted energy state of the at least one edge device, and wherein the set of operational parameters is based on the indication of the current and / or predicted energy state of the at least one edge device.

227. 209. The AI-based platform of claim 209, wherein the artificial intelligence system is further configured to determine the set of operational parameters based on an output of a digital twin representing at least one edge device of a set of edge devices associated with the operational process, the output of the digital twin indicating a current and / or predicted energy state of the at least one edge device.

228. The AI-based platform of claim 209, wherein the artificial intelligence system is further configured to orchestrate a set of modular distributed energy systems to generate, store, and / or supply energy, said orchestration being based on said set of operating parameters and local demand requirements.

229. 210. The AI-based platform of claim 209, wherein the analysis of the patterns of energy associated with the operating process includes an analysis of backup power availability based on a failure of at least a portion of the electrical grid.

230. 210. The AI-based platform of claim 209, wherein the analysis of the pattern of energy associated with the operational process includes an analysis of at least one auxiliary function associated with the set of resources, and wherein the set of operational parameters includes at least one operational parameter associated with the at least one auxiliary function.

231. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform 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, the rules and / or policies being associated with a configuration of a set of edge devices operating in local data communication with a set of energy generation equipment, energy storage equipment, energy supply equipment, or energy consumption systems.

232. 232. The AI-based platform of claim 231, wherein upon configuration in the policy and governance engine, policies associated with energy generation instructions are automatically applied by at least one of the edge devices to control energy generation by at least one energy generation system controlled via the edge device.

233. 232. The AI-based platform of claim 231, wherein upon configuration in the policy and governance engine, policies associated with energy consumption instructions are automatically applied by at least one of the edge devices to control energy consumption by at least one energy consuming system controlled via the edge device.

234. 232. The AI-based platform of claim 231, wherein upon configuration in the policy and governance engine, policies associated with energy supply instructions are automatically applied by at least one of the edge devices to control energy supply by at least one energy supply system controlled via the edge device.

235. 232. The AI-based platform of claim 231, wherein upon configuration in the policy and governance engine, policies associated with energy storage instructions are automatically applied by at least one of the edge devices to control energy storage by at least one energy storage system controlled via the edge device.

236. 232. The AI-based platform of claim 231, wherein the policy and governance engine is configured to operate on a set of stored policy templates to configure policies.

237. 232. The AI-based platform of claim 231, wherein a set of recommended policies is automatically generated for presentation in the policy and governance engine based on a dataset of past policies, a dataset representing the operational state and / or configuration of a set of distributed energy resources, and a set of past results.

238. The policy and governance engine is further configured to adjust the rules and / or policies based on at least one contextual factor, the at least one contextual factor being: Historical energy transaction data, at least one operational factor; at least one market factor; At least one expected market behavior; or at least one predicted customer behavior; 232. The AI-based platform of claim 231, comprising at least one of:

239. The policy and governance engine is further configured to adapt transport of data over a network and / or communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 232. The AI-based platform of claim 231, wherein the AI-based platform is based on at least one of:

240. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 232. The AI-based platform of claim 231, further comprising an adaptive energy digital twin representing at least one of:

241. providing a visual and / or analytical indication of energy consumption by at least one energy consumer; filtering the energy data; Highlighting energy data, or adjusting energy data; 232. The AI-based platform of claim 231, further comprising an adaptive energy digital twin configured to perform at least one of the following:

242. At least one machine, At least one factory, or at least one vehicle in the vehicle fleet; 232. The AI-based platform of claim 231, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of:

243. The policy and governance engine further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 232. The AI-based platform of claim 231, configured to perform at least one of:

244. At least one of the rules and / or policies is based on at least one public data resource, the at least one public data resource being: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market Data Resources E-commerce data resources 232. The AI-based platform of claim 231, comprising at least one of:

245. At least one of the rules and / or policies is based on at least one enterprise data resource, the at least one enterprise data resource comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 232. The AI-based platform of claim 231, comprising at least one of:

246. and 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, the training dataset comprising: at least one human tag and / or label; at least one human interaction with the hardware and / or software system; At least one result at least one AI-generated training data sample; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 232. The AI-based platform of claim 231, wherein the AI-based platform is based on at least one of:

247. The policy and governance engine is configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy comprising: At least one fixed transmission line; at least one instance of wireless energy transmission; At least one delivery of fuel; or At least one delivery of the stored energy; 232. The AI-based platform of claim 231, comprising at least one of:

248. The policy and governance engine 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 232. The AI-based platform of claim 231, comprising at least one of:

249. The policy and governance engine is located in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 232. The AI-based platform of claim 231, comprising at least one of:

250. 232. The AI-based platform of claim 231, wherein the policy and governance engine is further configured to generate and / or execute at least one smart contract, each of the at least one smart contract applying the rules and / or policies to at least one energy-related transaction.

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

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

253. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: a set of edge devices, the set of edge devices comprising: in communication with at least one energy generating facility, energy storage facility, and / or energy consuming system; automatically executing a set of pre-defined policies governing the energy generation, energy storage, or energy consumption of each of the energy generating facilities, energy storage facilities, or energy consuming systems; An AI-based platform configured to:

254. 254. The AI-based platform of claim 253, wherein the automatically executed policies are a set of contextual policies that adjust based on the current status of a set of energy generating entities within the energy grid.

255. The AI-based platform of claim 253, wherein the automatically executed policies are a set of contextual policies that adjust based on the current status of a set of energy generating entities in an energy generation environment, including an energy grid and a set of distributed energy resources that operate independently of the energy grid.

256. 254. The AI-based platform of claim 253, wherein the automatically executed policies are a set of contextual policies that adjust based on the current status of a set of energy storage entities within an energy grid.

257. The AI-based platform of claim 253, wherein the automatically executed policies are a set of contextual policies that adjust based on a current status 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, and the automatically executed policies are a set of contextual policies that adjust based on a current status of a set of energy distribution entities within the energy grid.

258. The AI-based platform of claim 253, wherein the automatically executed policies are a set of contextual policies that adjust based on the current status 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.

259. 254. The AI-based platform of claim 253, wherein the automatically executed policies are a set of contextual policies that adjust based on the current state of a set of energy consuming entities that consume energy from an energy grid.

260. The AI-based platform of claim 253, wherein the automatically executed policies are a set of contextual policies that adjust based on the current state of a set of energy consuming entities that consume energy from an energy grid and from a set of distributed energy resources that operate independently of the energy grid.

261. The set of edge devices is further configured to adjust the set of pre-configured policies based on at least one contextual factor, the at least one contextual factor comprising: Historical energy transaction data, at least one operational factor; at least one market factor; At least one expected market behavior; or at least one predicted customer behavior; 254. The AI-based platform of claim 253, comprising at least one of:

262. At least one of the edge devices is further configured to adapt transport of data over a network and / or communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 254. The AI-based platform of claim 253, wherein the AI-based platform is based on at least one of:

263. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 254. The AI-based platform of claim 253, further comprising an adaptive energy digital twin representing at least one of:

264. providing a visual and / or analytical indication of energy consumption by at least one energy consumer; filtering the energy data; Highlighting energy data, or adjusting energy data; 254. The AI-based platform of claim 253, further comprising an adaptive energy digital twin configured to perform at least one of the following:

265. At least one machine, At least one factory, or at least one vehicle in the vehicle fleet; 254. The AI-based platform of claim 253, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of:

266. At least one of the edge devices further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 254. The AI-based platform of claim 253, configured to perform at least one of:

267. At least one of the pre-defined policies is based on at least one public data resource, the at least one public data resource comprising: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market Data Resources E-commerce data resources 254. The AI-based platform of claim 253, comprising at least one of:

268. At least one of the pre-defined policies is based on at least one enterprise data resource, the at least one enterprise data resource comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 254. The AI-based platform of claim 253, comprising at least one of:

269. At least one of the edge devices includes at least one AI-based model and / or algorithm, and the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset comprising: at least one human tag and / or label; at least one human interaction with the hardware and / or software system; At least one result at least one AI-generated training data sample; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 254. The AI-based platform of claim 253, wherein the AI-based platform is based on at least one of:

270. At least one of the edge devices is further configured to orchestrate a delivery of energy to at least one point of consumption, the delivery of energy comprising: At least one fixed transmission line; at least one instance of wireless energy transmission; At least one delivery of fuel; or At least one delivery of the stored energy; 254. The AI-based platform of claim 253, comprising at least one of:

271. At least one of the edge devices 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 254. The AI-based platform of claim 253, comprising at least one of:

272. At least one of the edge devices is located in an off-grid environment, and the off-grid environment includes: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 254. The AI-based platform of claim 253, comprising at least one of:

273. The set of edge devices further comprises: determining at least one pattern of energy availability based on communication with the at least one energy generation facility, energy storage facility, and / or energy consumption system; and updating the execution of the set of pre-defined policies based on the at least one pattern; 254. The AI-based platform of claim 253, configured to:

274. 254. The AI-based platform of claim 253, wherein at least one edge device of the set of edge devices is configured to manage the operation of an industrial facility, and wherein the set of preconfigured policies is based on at least one energy objective associated with the industrial facility.

275. 254. The AI-based platform of claim 253, wherein the at least one energy generation facility, energy storage facility, and / or energy consumption system is located in a geographic region, and the set of pre-defined policies is based on at least one energy objective associated with the geographic region.

276. 254. The AI-based platform of claim 253, wherein the set of edge devices is configured to automatically implement the set of pre-defined policies by adjusting at least one of the allocation of energy resources associated with the at least one energy generation facility, energy storage facility, and / or energy consumption system and the scheduling of processes performed by the at least one energy generation facility, energy storage facility, and / or energy consumption system.

277. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising: a machine learning system trained on a set of energy intelligence data and located on an edge device, the machine learning system configured to undergo additional training by the edge device to improve energy management.

278. The AI-based platform of claim 277, wherein the energy management includes managing the generation of energy by a set of distributed energy generation resources.

279. 278. The AI-based platform of claim 277, wherein said energy management includes managing the storage of energy through a set of distributed energy storage resources.

280. The AI-based platform of claim 277, wherein the energy management includes managing the supply of energy by a set of distributed energy supply resources.

281. 278. The AI-based platform of claim 277, wherein said energy management includes managing the consumption of energy by a set of distributed energy consuming resources.

282. The AI-based platform of claim 277, wherein the energy management is based on a set of rules and / or policies associated with the edge device and a set of energy generation equipment, energy storage equipment, energy supply equipment, or energy consumption systems.

283. The machine learning system is further configured to adapt transport of data over a network and / or a communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 278. The AI-based platform of claim 277, wherein the AI-based platform is based on at least one of the following:

284. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 278. The AI-based platform of claim 277, further comprising an adaptive energy digital twin representing at least one of:

285. providing a visual and / or analytical indication of energy consumption by at least one energy consumer; filtering the energy data; Highlighting energy data, or adjusting energy data; 278. The AI-based platform of claim 277, further comprising an adaptive energy digital twin configured to perform at least one of the following:

286. At least one machine, At least one factory, or at least one vehicle in the vehicle fleet; 278. The AI-based platform of claim 277, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of:

287. The machine learning system further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 278. The AI-based platform of claim 277, configured to perform at least one of:

288. The energy intelligence data is based on at least one public data resource, the at least one public data resource comprising: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market Data Resources E-commerce data resources 278. The AI-based platform of claim 277, comprising at least one of:

289. The energy intelligence data is based on at least one enterprise data resource, the at least one enterprise data resource comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 278. The AI-based platform of claim 277, comprising at least one of:

290. The machine learning system is further trained based on a training dataset, the training dataset comprising: at least one human tag and / or label; at least one human interaction with the hardware and / or software system; At least one result at least one AI-generated training data sample; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 278. The AI-based platform of claim 277, wherein the AI-based platform is based on at least one of:

291. The machine learning system is further configured to orchestrate a delivery of energy to at least one point of consumption, the delivery of energy comprising: At least one fixed transmission line; at least one instance of wireless energy transmission; At least one delivery of fuel; or At least one delivery of the stored energy; 278. The AI-based platform of claim 277, comprising at least one of:

292. The machine learning 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 278. The AI-based platform of claim 277, comprising at least one of:

293. The edge device is disposed in an off-grid environment, and the off-grid environment includes: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 278. The AI-based platform of claim 277, comprising at least one of:

294. The AI-based platform of claim 277, wherein the edge device is located in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

295. The AI-based platform of claim 277, wherein the edge device provides information regarding the energy status and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

296. The AI-based platform of claim 277, wherein the edge device includes and / or manages at least one sensor of a set of sensors, the set of sensors being associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

297. 278. The AI-based platform of claim 277, wherein the edge device is associated with a situation and / or environment, and the edge device is further configured to perform the additional training of the machine learning system in response to changes in the situation and / or environment.

298. 278. The AI-based platform of claim 277, wherein the edge device is further configured to perform the additional training of the machine learning system based on a determination of model drift by the machine learning system.

299. The AI-based platform of claim 277, wherein the additional training is based on the set of energy intelligence data on which the machine learning system was initially trained and additional energy intelligence data on which the machine learning system has not yet been trained.

300. 278. The AI-based platform of claim 277, wherein the further training includes adding the machine learning system to an ensemble that includes at least one other artificial intelligence system.

301. The AI-based platform of claim 277, wherein the set of energy intelligence data is based on at least one energy-related policy and / or rule, and the additional training is based on changes to the at least one energy-related policy and / or rule.

302. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: a set of edge devices including a set of artificial intelligence systems, the set of artificial intelligence systems including: Processing data handled by the edge device; determining a combination of energy generation, storage, supply, and / or consumption characteristics for a set of systems in local communication with the edge device based on the data, and outputting a data set indicative of the composition ratio of the combination; An AI-based platform characterized by being configured as follows.

303. 303. The AI-based platform of claim 302, wherein the output dataset indicates the percentage of energy generated by an energy grid and the percentage of energy generated by a set of distributed energy resources that operate independently from the energy grid.

304. 303. The AI-based platform of claim 302, wherein the output dataset indicates the percentage of energy generated by renewable energy resources and the percentage of energy generated by non-renewable resources.

305. 303. The AI-based platform of claim 302, wherein the output data set indicates the percentage of energy production by type for each interval over a series of time intervals.

306. 303. The AI-based platform of claim 302, wherein the output data set indicates carbon production associated with energy production for each type of energy in the combination of energy during each interval of a series of time intervals.

307. 303. The AI-based platform of claim 302, wherein the output data set indicates carbon emissions associated with energy production for each type of energy in the combination of energy during each interval of a series of time intervals.

308. At least one of the edge devices is further configured to adapt transport of data over a network and / or communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 303. The AI-based platform of claim 302, wherein the AI-based platform is based on at least one of:

309. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 303. The AI-based platform of claim 302, further comprising an adaptive energy digital twin representing at least one of:

310. providing a visual and / or analytical indication of energy consumption by at least one energy consumer; filtering the energy data; Highlighting energy data, or adjusting energy data; 303. The AI-based platform of claim 302, further comprising an adaptive energy digital twin configured to perform at least one of the following:

311. At least one machine, At least one factory, or at least one vehicle in the vehicle fleet; 303. The AI-based platform of claim 302, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of:

312. At least one of the edge devices further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 303. The AI-based platform of claim 302, configured to perform at least one of:

313. The data is based on at least one public data resource, the public data resource comprising: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market Data Resources E-commerce data resources 303. The AI-based platform of claim 302, comprising at least one of:

314. The data is based on at least one enterprise data resource, the enterprise data resource comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 303. The AI-based platform of claim 302, comprising at least one of:

315. At least one of the edge devices includes at least one AI-based model and / or algorithm, and the at least one AI-based model and / or algorithm is trained based on a training dataset, the training dataset comprising: at least one human tag and / or label; at least one human interaction with the hardware and / or software system; At least one result at least one AI-generated training data sample; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 303. The AI-based platform of claim 302, wherein the AI-based platform is based on at least one of:

316. At least one of the edge devices is further configured to orchestrate a delivery of energy to at least one point of consumption, the delivery of energy comprising: At least one fixed transmission line; at least one instance of wireless energy transmission; At least one delivery of fuel; or At least one delivery of the stored energy; 303. The AI-based platform of claim 302, comprising at least one of:

317. At least one of the edge devices 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 303. The AI-based platform of claim 302, comprising at least one of:

318. At least one of the edge devices is located in an off-grid environment, and the off-grid environment includes: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 303. The AI-based platform of claim 302, comprising at least one of:

319. 303. The AI-based platform of claim 302, wherein at least a portion of the set of edge devices are located in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

320. 303. The AI-based platform of claim 302, wherein the set of edge devices provides information regarding the energy status and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

321. 303. The AI-based platform of claim 302, wherein the set of edge devices includes and / or manages at least one sensor of a set of sensors, the set of sensors being associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

322. 303. The AI-based platform of claim 302, wherein the combination of energy generation, storage, supply, and / or consumption characteristics is based on at least one energy demand requirement associated with the set of edge devices.

323. 303. The AI-based platform of claim 302, wherein the combination of energy generation, storage, supply, and / or consumption characteristics is based on prioritizing energy collection, storage, transportation, and / or usage associated with each energy source associated with the set of edge devices.

324. 303. The AI-based platform of claim 302, wherein the combination of energy generation, storage, supply, and / or consumption characteristics is based on storage, transportation, and / or usage schedules associated with each energy source associated with the set of edge devices.

325. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising: a data processing system configured to fuse at least one entity of a generation, storage, supply, or consumption grid dataset of an energy grid entity with at least one entity of a generation, storage, supply, and / or consumption dataset of an off-grid energy entity.

326. 326. The AI-based platform of claim 325, wherein the data processing system is configured to automatically time align energy grid entity data with off-grid energy entity data.

327. 326. The AI-based platform of claim 325, wherein the data processing system is configured to automatically collect off-grid energy entity sensor data from a set of edge devices from which a set of off-grid energy entities are controlled.

328. 326. The AI-based platform of claim 325, wherein the data processing system is configured to automatically normalize the energy grid entity data and the off-grid energy entity data and present the data according to a common set of units.

329. The data processing system is further configured to adapt transport of data over a network and / or a communication system, the adapting comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 326. The AI-based platform of claim 325, wherein the AI-based platform is based on at least one of:

330. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 326. The AI-based platform of claim 325, further comprising an adaptive energy digital twin representing at least one of:

331. providing a visual and / or analytical indication of energy consumption by at least one energy consumer; filtering the energy data; Highlighting energy data, or adjusting energy data; 326. The AI-based platform of claim 325, further comprising an adaptive energy digital twin configured to perform at least one of the following:

332. At least one machine, At least one factory, or at least one vehicle in the vehicle fleet; 326. The AI-based platform of claim 325, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of:

333. The data processing system further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 326. The AI-based platform of claim 325, configured to perform at least one of:

334. and 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, the training dataset comprising: at least one human tag and / or label; at least one human interaction with the hardware and / or software system; At least one result at least one AI-generated training data sample; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 326. The AI-based platform of claim 325, wherein the AI-based platform is based on at least one of:

335. The data processing system is further configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy comprising: At least one fixed transmission line; at least one instance of wireless energy transmission; At least one delivery of fuel; or At least one delivery of the stored energy; 326. The AI-based platform of claim 325, comprising at least one of:

336. The 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 326. The AI-based platform of claim 325, comprising at least one of:

337. At least one entity of the off-grid energy generation, storage, and / or consumption dataset is located in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 326. The AI-based platform of claim 325, comprising at least one of:

338. 326. The AI-based platform of claim 325, wherein the data processing system is further configured to intelligently orchestrate and manage power and / or energy based on a dataset of energy generation, storage, and / or consumption data for a set of infrastructure assets, the dataset generated at least in part by a set of sensors included in and / or managed by a set of edge devices.

339. The data processing system further comprises: Generation of energy through a set of distributed energy generation resources; storing energy through a set of distributed energy storage resources; The provision of energy by a set of distributed energy supply resources, or Energy consumption by a set of distributed energy-consuming resources, 326. The AI-based platform of claim 325, configured to manage at least one of:

340. The data processing system is further configured to intelligently orchestrate and manage power and / or energy of a set of entities, the set of entities comprising: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, 326. The AI-based platform of claim 325, comprising at least one of:

341. 326. The AI-based platform of claim 325, wherein the data processing system is further configured to execute at least one algorithm that performs a simulation of energy consumption by at least one of the entities, the simulation being based on a dataset including alternative state or event parameters of at least one of the entities that reflect alternative consumption scenarios, and the algorithm accesses a demand response model that describes how energy demand responds to changes in the price of energy or to changes in the price of the operation or activity in which the energy is consumed.

342. 326. The AI-based platform of claim 325, wherein the data processing system includes a policy and governance engine configured to deploy a set of rules and / or policies to at least one edge device in local communication with at least one of the entities, the edge device configured to manage at least one of the entities based on the rules and / or policies.

343. 326. The AI-based platform of claim 325, wherein the data processing system includes an analysis system that represents operational parameters and a current state of at least one of the entities based on a set of sensed parameters, the set of sensed parameters being generated by a set of edge devices in proximity to at least one of the entities, and the analysis system is configured to provide recommendations related to at least one of the entities or at least one additional available entity.

344. The data processing system includes an artificial intelligence system trained with a historical data set related to energy generation, storage, and / or utilization of an operating process associated with at least one of the entities, the data processing system further comprising: analyzing the energy patterns of said operating process; outputting a prediction of the energy requirements of the operating process based on current conditions and / or information associated with at least one of the entities; 326. The AI-based platform of claim 325, configured to:

345. 326. The AI-based platform of claim 325, wherein the data processing system is further configured to blend at least one entity of a backup and / or auxiliary energy generation, storage, supply, or consumption grid dataset with the energy grid entity generation, storage, supply, and / or consumption grid dataset and the off-grid energy entity generation, storage, supply, and / or consumption grid dataset.

346. 326. The AI-based platform of claim 325, wherein the data processing system is further configured to coordinate the development of energy grid resources and / or off-grid energy resources based on fusing the energy grid entity's generation, storage, supply, or consumption grid dataset with the off-grid energy entity's generation, storage, supply, and / or consumption dataset.

347. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: a set of autonomous orchestration systems for improving delivery of a heterogeneous set of energy types to points of consumption, the set of autonomous orchestration systems comprising: the location of the point of consumption; a set of consumption attributes; The improvement is based on The consumption attribute is: peak electricity demand at said consumption point; The continuity of electricity demand at the point of consumption; and the type of energy available at said point of consumption; An AI-based platform comprising at least one of:

348. The AI-based platform of claim 347, wherein the set of autonomous orchestration systems orchestrates the provision of defined types of energy generation capacity to the consumption points.

349. The AI-based platform of claim 347, wherein the set of autonomous orchestration systems orchestrates the provision of defined types of energy storage capacity to the points of consumption.

350. 348. The AI-based platform of claim 347, wherein the type of energy available is determined, at least in part, based on a set of operational compatibility parameters.

351. 348. The AI-based platform of claim 347, wherein the type of energy available is determined, at least in part, based on a set of governance parameters.

352. 352. The AI-based platform of claim 351, wherein the set of governance parameters relates to the use of renewable energy resources.

353. 352. The AI-based platform of claim 351, wherein the set of governance parameters relates to carbon generation or emissions.

354. At least one of the set of autonomic orchestration systems is further configured to adapt transport of data over a network and / or communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 348. The AI-based platform of claim 347, wherein the AI-based platform is based on at least one of the following:

355. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 348. The AI-based platform of claim 347, further comprising an adaptive energy digital twin representing at least one of:

356. providing a visual and / or analytical indication of energy consumption by at least one energy consumer; filtering the energy data; Highlighting energy data, or adjusting energy data; 348. The AI-based platform of claim 347, further comprising an adaptive energy digital twin configured to perform at least one of the following:

357. At least one machine, At least one factory, or at least one vehicle in the vehicle fleet; 348. The AI-based platform of claim 347, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of:

358. At least one of the set of autonomic orchestration systems further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 348. The AI-based platform of claim 347, configured to perform at least one of the following:

359. At least one of the consumption attributes is based on at least one public data resource, the public data resource comprising: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, 348. The AI-based platform of claim 347, comprising at least one of:

360. At least one of the consumption attributes is based on at least one enterprise data resource, the enterprise data resource comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 348. The AI-based platform of claim 347, comprising at least one of:

361. and 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, the training dataset comprising: at least one human tag and / or label; at least one human interaction with the hardware and / or software system; At least one result at least one AI-generated training data sample; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 348. The AI-based platform of claim 347, wherein the AI-based platform is based on at least one of:

362. At least one of the set of autonomous orchestration systems is further configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy comprising: At least one fixed transmission line; at least one instance of wireless energy transmission; At least one delivery of fuel; or At least one delivery of the stored energy; 348. The AI-based platform of claim 347, comprising at least one of:

363. At least one of the set of autonomous orchestration 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 348. The AI-based platform of claim 347, comprising at least one of:

364. At least one of the set of autonomic orchestration systems is deployed in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 348. The AI-based platform of claim 347, comprising at least one of:

365. The AI-based platform of claim 347, wherein the set of autonomous orchestration systems is further configured to determine the supply of the heterogeneous set of energy types based on a set of rules and / or policies governing a set of energy generation, storage, and / or consumption workloads, the rules and / or policies being associated with a configuration of a set of edge devices operating in local data communication with a set of energy generation equipment, energy storage equipment, energy supply equipment, or energy consumption systems.

366. The AI-based platform of claim 347, wherein the set of autonomous orchestration systems is further configured to determine the supply of the heterogeneous set of energy types based on a simulation of energy consumption by at least one energy consumer, the simulation being based on a dataset including at least one alternative state or event parameter of the at least one energy consumer reflecting alternative consumption scenarios, and the simulation being based on a demand response model that describes how energy demand responds to changes in the price of energy or changes in the price of the operation or activity in which the energy is consumed.

367. The AI-based platform of claim 347, wherein the set of autonomous orchestration systems improves the supply of the heterogeneous set of energy types to the consumption points by matching each of the heterogeneous set of energy types with at least one consumer associated with the consumption point.

368. The AI-based platform of claim 347, wherein the set of autonomous orchestration systems improves the supply of the heterogeneous set of energy types to the consumption points by determining the development of additional energy sources of one or more energy types, the development being based on predictions of energy demand requirements associated with the consumption points.

369. The AI-based platform of claim 347, wherein the set of autonomous orchestration systems improves the supply of the heterogeneous set of energy types to the consumption points by comparing characteristics of energy demand associated with the consumption points with characteristics of each energy type in the heterogeneous set of energy types.

370. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising an intelligent agent trained based on a dataset of expert interactions with an energy supply system, the intelligent agent being trained to generate at least one recommendation and / or instruction regarding optimization of at least one energy objective and at least one other objective.

371. 371. The AI-based platform of claim 370, wherein the other purpose is an enterprise operational purpose.

372. 371. The AI-based platform of claim 370, wherein the intelligent agent operates based on status data from a set of edge devices through which a set of energy generating resources are controlled.

373. 371. The AI-based platform of claim 370, wherein the intelligent agent operates based on status data from a set of edge devices over which a set of energy consuming resources are controlled.

374. 371. The AI-based platform of claim 370, wherein the intelligent agent operates based on status data from a set of edge devices from which a set of energy storage resources are controlled.

375. 371. The AI-based platform of claim 370, wherein the intelligent agent operates based on status data from a set of edge devices through which a set of energy supply resources are controlled.

376. The intelligent agent is further configured to adapt transport of data over a network and / or communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 371. The AI-based platform of claim 370, wherein the AI-based platform is based on at least one of:

377. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 371. The AI-based platform of claim 370, further comprising an adaptive energy digital twin representing at least one of:

378. providing a visual and / or analytical indication of energy consumption by at least one energy consumer; filtering the energy data; Highlighting energy data, or adjusting energy data; 371. The AI-based platform of claim 370, further comprising an adaptive energy digital twin configured to perform at least one of the following:

379. At least one machine, At least one factory, or at least one vehicle in the vehicle fleet; 371. The AI-based platform of claim 370, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of:

380. The intelligent agent further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 371. The AI-based platform of claim 370, configured to perform at least one of:

381. The dataset is based on at least one public data resource, the public data resource comprising: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, 371. The AI-based platform of claim 370, comprising at least one of:

382. The data set is based on at least one enterprise data resource, the enterprise data resource comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 371. The AI-based platform of claim 370, comprising at least one of:

383. The intelligent agent is trained based on a training dataset, the training dataset comprising: at least one human tag and / or label; at least one human interaction with the hardware and / or software system; At least one result at least one AI-generated training data sample; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 371. The AI-based platform of claim 370, wherein the AI-based platform is based on at least one of:

384. The intelligent agent is further configured to orchestrate delivery of energy to at least one consumption point, the delivery of energy comprising: At least one fixed transmission line; at least one instance of wireless energy transmission; At least one delivery of fuel; or At least one delivery of the stored energy; 371. The AI-based platform of claim 370, comprising at least one of:

385. The intelligent agent 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 371. The AI-based platform of claim 370, comprising at least one of:

386. The intelligent agent is deployed in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 371. The AI-based platform of claim 370, comprising at least one of:

387. 371. The AI-based platform of claim 370, wherein the intelligent agent is located in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

388. 371. The AI-based platform of claim 370, wherein the intelligent agent provides information regarding the energy status and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

389. 371. The AI-based platform of claim 370, wherein the intelligent agent manages at least one sensor of a set of sensors, the set of sensors being associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

390. 371. The AI-based platform of claim 370, wherein the intelligent agent is further configured to manage at least one processing task associated with at least one device, and wherein the at least one recommendation and / or instruction includes adjusting the at least one processing task based on the at least one energy objective and / or the at least one other objective.

391. The intelligent agent further comprises: Move between at least two devices, applying the at least one recommendation and / or instruction to the device on which the intelligent agent is present while present on each of the at least two devices.

371. The AI-based platform of claim 370, configured to:

392. 371. The AI-based platform of claim 370, wherein the intelligent agent is further configured to exchange information with at least one other intelligent agent, the information being based on at least one recommendation and / or instruction or one or both of the at least one energy objective and / or the at least one other objective.

393. 371. The AI-based platform of claim 370, wherein the recommendations and / or instructions are associated with at least one device, and wherein the intelligent agent is further configured to exchange collected and / or determined data associated with the at least one device with at least one other intelligent agent.

394. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: an artificial intelligence system trained on a set of energy generation, energy storage, energy supply, and / or energy consumption outcomes, the artificial intelligence system comprising: analyzing a dataset of current energy generation, current energy storage, current energy supply, and / or current energy consumption information; providing a recommendation including at least one operating parameter that satisfies both mobile energy needs or fixed location energy needs in a predetermined domain; An AI-based platform configured to:

395. 402. The AI-based platform of claim 394, wherein the predetermined domain includes a predetermined geographic location and a predetermined time period.

396. 395. The AI-based platform of claim 394, wherein the at least one operating parameter indicates production instructions for a set of energy generating resources.

397. 395. The AI-based platform of claim 394, wherein the at least one operating parameter indicates storage instructions for a set of energy storage resources.

398. The AI-based platform of claim 394, wherein the at least one operating parameter indicates a supply instruction for a set of energy supply resources.

399. 395. The AI-based platform of claim 394, wherein said at least one operational parameter indicates consumption instructions for a set of energy consuming entities.

400. The artificial intelligence system is further configured to adapt transport of data over a network and / or a communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 395. The AI-based platform of claim 394, wherein the AI-based platform is based on at least one of:

401. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 395. The AI-based platform of claim 394, further comprising an adaptive energy digital twin representing at least one of:

402. providing a visual and / or analytical indication of energy consumption by at least one energy consumer; filtering the energy data; Highlighting energy data, or adjusting energy data; 395. The AI-based platform of claim 394, further comprising an adaptive energy digital twin configured to perform at least one of the following:

403. At least one machine, At least one factory, or at least one vehicle in the vehicle fleet; 395. The AI-based platform of claim 394, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of:

404. The artificial intelligence system further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 402. The AI-based platform of claim 394, configured to perform at least one of:

405. The dataset is based on at least one public data resource, the at least one public data resource comprising: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, 395. The AI-based platform of claim 394, comprising at least one of:

406. The data set is based on at least one enterprise data resource, the at least one enterprise data resource comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 395. The AI-based platform of claim 394, comprising at least one of:

407. The artificial intelligence system is trained based on a training data set, the training data set comprising: at least one human tag and / or label; at least one human interaction with the hardware and / or software system; At least one result at least one AI-generated training data sample; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 395. The AI-based platform of claim 394, wherein the AI-based platform is based on at least one of:

408. The artificial intelligence system is further configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy comprising: At least one fixed transmission line; at least one instance of wireless energy transmission; At least one delivery of fuel; or At least one delivery of the stored energy; 395. The AI-based platform of claim 394, comprising at least one of:

409. The artificial intelligence 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 395. The AI-based platform of claim 394, comprising at least one of:

410. The artificial intelligence system is deployed in an off-grid environment, and the off-grid environment includes: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 395. The AI-based platform of claim 394, comprising at least one of:

411. 395. The AI-based platform of claim 394, wherein the artificial intelligence system is located in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

412. The AI-based platform of claim 394, wherein the artificial intelligence system provides information regarding the energy status and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

413. 395. The AI-based platform of claim 394, wherein the artificial intelligence system manages at least one sensor of a set of sensors, the set of sensors being associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

414. 395. The AI-based platform of claim 394, wherein the predetermined domain includes at least one boundary, and the data set is restricted based on the at least one boundary associated with the predetermined domain.

415. 395. The AI-based platform of claim 394, wherein the recommendation is based on at least one constraint associated with the at least one operating parameter, and the artificial intelligence system is trained to analyze the data set based on the at least one constraint.

416. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: an artificial intelligence system, the artificial intelligence system comprising: Analyzing the monitored local situation dataset; and generating a recommended configuration of at least one distributed system of a set of distributed systems, each distributed system of the set of distributed systems being configurable to both produce and consume energy, the configuration causing the at least one distributed system to produce and / or consume energy based on the monitored local conditions; An AI-based platform configured to execute.

417. 417. The AI-based platform of claim 416, wherein the artificial intelligence system configures the plurality of distributed systems such that an aggregate set of performance requirements is met across the plurality of distributed systems in the set of distributed systems.

418. 418. The AI-based platform of claim 417, wherein the aggregated performance requirements are a set of economic performance requirements.

419. 418. The AI-based platform of claim 417, wherein the aggregated performance requirements are a set of regulatory performance requirements.

420. The AI-based platform of claim 417, wherein the aggregated performance requirements relate to carbon production or emissions.

421. 418. The AI-based platform of claim 417, wherein the aggregated performance requirements are a set of consumption requirements.

422. The artificial intelligence system is further configured to adapt transport of data over a network and / or a communication system, the adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 417. The AI-based platform of claim 416, wherein the AI-based platform is based on at least one of:

423. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 417. The AI-based platform of claim 416, further comprising an adaptive energy digital twin representing at least one of:

424. providing a visual and / or analytical indication of energy consumption by at least one energy consumer; filtering the energy data; Highlighting energy data, or adjusting energy data; 417. The AI-based platform of claim 416, further comprising an adaptive energy digital twin configured to perform at least one of the following:

425. At least one machine, At least one factory, or at least one vehicle in the vehicle fleet; 417. The AI-based platform of claim 416, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of:

426. The artificial intelligence system further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 417. The AI-based platform of claim 416, configured to perform at least one of:

427. The dataset is based on at least one public data resource, the public data resource comprising: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, 417. The AI-based platform of claim 416, comprising at least one of:

428. The data set is based on at least one enterprise data resource, the enterprise data resource comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 417. The AI-based platform of claim 416, comprising at least one of:

429. The artificial intelligence system is further configured to orchestrate delivery of energy to at least one point of consumption, the delivery of energy comprising: At least one fixed transmission line; at least one instance of wireless energy transmission; At least one delivery of fuel; or At least one delivery of the stored energy; 417. The AI-based platform of claim 416, comprising at least one of:

430. The artificial intelligence 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 417. The AI-based platform of claim 416, comprising at least one of:

431. The artificial intelligence system is deployed in an off-grid environment, and the off-grid environment includes: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 417. The AI-based platform of claim 416, comprising at least one of:

432. The artificial intelligence system is trained based on a training data set, the training data set comprising: at least one human tag and / or label; at least one human interaction with the hardware and / or software system; At least one result at least one AI-generated training data sample; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 417. The AI-based platform of claim 416, wherein the AI-based platform is based on at least one of:

433. The AI-based platform of claim 416, wherein the artificial intelligence system is located in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

434. The AI-based platform of claim 416, wherein the artificial intelligence system provides information regarding the energy status and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

435. The AI-based platform of claim 416, wherein the artificial intelligence system manages at least one sensor of a set of sensors, the set of sensors being associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

436. 417. The AI-based platform of claim 416, wherein the recommended configuration is based on at least one auxiliary power resource associated with the set of distributed systems.

437. The recommended configuration is: the current and / or predicted location of said at least one distributed system of said set of distributed systems; or a current and / or predicted location of at least one energy resource associated with the set of distributed systems; 417. The AI-based platform of claim 416, wherein the AI-based platform is based on at least one of:

438. The recommended configuration further comprises: a local demand situation associated with the current location and / or the predicted location of the at least one distributed system of the set of distributed systems; or a local demand situation associated with the current location and / or the predicted location of at least one energy resource associated with the set of distributed systems; 438. The AI-based platform of claim 437, wherein the AI-based platform is based on at least one of:

439. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising: a set of adaptive and autonomous data processing systems for collecting and transmitting energy data from a set of edge networking devices controlled by a set of distributed energy entities, the data processing systems being trained based on a training dataset to recognize a set of events and / or signals indicative of an energy pattern of at least one of the set of distributed energy entities.

440. The AI-based platform of claim 439, wherein the set of distributed energy entities includes at least one energy generating resource.

441. The AI-based platform of claim 439, wherein the set of distributed energy entities includes at least one energy consuming entity.

442. The AI-based platform of claim 439, wherein the set of distributed energy entities includes at least one energy storage resource.

443. The AI-based platform of claim 439, wherein the set of distributed energy entities includes at least one energy supply resource.

444. 440. The AI-based platform of claim 439, wherein the training data set includes historical energy generation data for a set of entities similar to the entity controlled via the edge networking device.

445. 440. The AI-based platform of claim 439, wherein the training data set includes historical energy consumption data for a set of entities similar to the entity controlled via the edge networking device.

446. The AI-based platform of claim 439, wherein the training data set includes historical energy supply data for a set of entities similar to the entity controlled via the edge networking device.

447. 440. The AI-based platform of claim 439, wherein the training data set includes historical energy storage data for a set of entities similar to the entity controlled via the edge networking device.

448. At least one of the adaptive and autonomous data processing systems is further configured to adapt transport of data over a network and / or communication system, said adaptation comprising: congestion conditions, delay and / or waiting time conditions; packet loss conditions, Error rate condition, Shipping costs terms, Quality of Service (QoS) requirements, Terms of use, Market conditions, or User-defined conditions, 440. The AI-based platform of claim 439, wherein the AI-based platform is based on at least one of the following:

449. Energy stakeholder entities, Energy distribution resources, Stakeholder information technology; networking infrastructure entities, Energy-dependent stakeholders' production facilities; Stakeholder transport systems; market conditions, or Energy use priorities, 440. The AI-based platform of claim 439, further comprising an adaptive energy digital twin representing at least one of:

450. providing a visual and / or analytical indication of energy consumption by at least one energy consumer; filtering the energy data; Highlighting energy data, or adjusting energy data; 440. The AI-based platform of claim 439, further comprising an adaptive energy digital twin configured to perform at least one of the following:

451. At least one machine, At least one factory, or at least one vehicle in the vehicle fleet; 440. The AI-based platform of claim 439, further comprising an adaptive energy digital twin configured to generate visual and / or analytical indicators of energy consumption by at least one of:

452. At least one of the adaptive and autonomous data processing systems further comprises: Extracting energy-related data; Detecting and / or correcting errors in energy-related data; Transforming, converting, normalizing, and / or cleansing energy-related data; Analyzing energy-related data; Detecting patterns, content, and / or objects in the energy-related data; compressing energy-related data; Streaming energy-related data; filtering energy-related data; Loading and / or saving energy-related data; Routing and / or transmitting energy-related data; or Maintaining the security of energy-related data; 440. The AI-based platform of claim 439, configured to perform at least one of:

453. The energy edge set is based on at least one public data resource, the public data resource comprising: Weather data resources, satellite data resources, census, population, demographic, and / or psychographic data resources; Market data resources, or e-commerce data resources, 440. The AI-based platform of claim 439, comprising at least one of:

454. The energy edge set is based on at least one enterprise data resource, the at least one enterprise data resource comprising: resource planning data, sales and / or marketing data; financial planning data, Demand planning data, supply chain data, Procurement data, Price data, Customer data, Product data, or Operating data, 440. The AI-based platform of claim 439, comprising at least one of:

455. and 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, the training dataset comprising: at least one human tag and / or label; at least one human interaction with the hardware and / or software system; At least one result at least one AI-generated training data sample; Supervised learning training process, Semi-supervised learning training process, or Deep learning training process, 440. The AI-based platform of claim 439, wherein the AI-based platform is based on at least one of:

456. At least one of the adaptive and autonomous data processing systems is further configured to orchestrate a delivery of energy to at least one point of consumption, the delivery of energy comprising: At least one fixed transmission line; at least one instance of wireless energy transmission; At least one delivery of fuel; or At least one delivery of the stored energy; 440. The AI-based platform of claim 439, comprising at least one of:

457. At least one of the adaptive and autonomous data processing 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 comprising: energy purchase and / or sales events; service charges related to energy purchase and / or sales events; Energy-consuming events, Energy-generating events, Energy distribution events, Energy storage events, carbon emission generating events, Carbon Emissions Reduction Events, Renewable Energy Credit Event, Contamination events, or pollution reduction events, 440. The AI-based platform of claim 439, comprising at least one of:

458. At least one of the adaptive and autonomous data processing systems is located in an off-grid environment, the off-grid environment comprising: Off-grid energy generation systems, Off-grid energy storage systems, or Off-grid energy mobilization systems, 440. The AI-based platform of claim 439, comprising at least one of:

459. 440. The AI-based platform of claim 439, wherein the set of adaptive and autonomous data processing systems is further configured to perform additional training of the data processing systems based on an initial set of energy intelligence data on which the data processing systems were initially trained and additional energy intelligence data on which the data processing systems have not yet been trained.

460. 440. The AI-based platform of claim 439, wherein the set of adaptive and autonomous data processing systems is further configured to instruct at least one edge networking device of the set of edge networking devices to adjust operational parameters associated with the set of distributed energy entities based on recognition of an event and / or signal from the set of events and / or signals.

461. 440. The AI-based platform of claim 439, wherein the set of adaptive and autonomous data processing systems are further configured to detect events and / or signals based on data collected from the set of edge networking devices during a period of time, the data processing systems being trained to recognize the set of events and / or signals based on at least one characteristic of the period of time.

462. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising: a data integration module that integrates energy intelligence data collected from at least one internal edge device located within an environment and at least one external edge device located outside the environment.

463. 463. The AI-based platform of claim 462, wherein data collected from at least one of the at least one internal edge device or the at least one external edge device is vectorized.

464. 463. The AI-based platform of claim 462, wherein data collected from at least one of the at least one internal edge device or the at least one external edge device is stored in a distributed database.

465. The AI-based platform of claim 462, wherein the data integration module is further configured to determine energy patterns based on local energy patterns associated with data collected from the at least one internal edge device and the at least one external edge device.

466. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: a digital dynamic twin configured to model at least one of historical energy demand, current historical energy demand, or forecasted energy demand; an AI-based digital twin update device that updates the dynamic digital twin based on a set of energy parameters; An AI-based platform comprising:

467. The AI-based platform of claim 466, wherein the AI-based digital twin update device performs updates to the dynamic digital twin to determine a prediction of energy demand for a future period, the updates being based on a prediction of energy demand for the future period by another AI model.

468. The AI-based platform of claim 466, wherein the dynamic digital twin is associated with a device type, and the AI-based digital twin updater analyzes data associated with the energy consumption by devices of the device type to update the dynamic digital twin to model energy consumption by devices of the device type.

469. The AI-based platform of claim 466, wherein the dynamic digital twin is further configured to model energy demand by at least one entity, the modeling being based on data indicative of energy consumption by the at least one entity.

470. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: An AI-based platform comprising: an energy access arbitrator that arbitrates among a set of energy consuming devices access to at least one energy source by at least one energy consuming device of the set of energy consuming devices.

471. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising: An AI-based platform comprising: a set of edge devices in local communication with at least one energy consuming device to identify at least one characteristic of energy consumption by the at least one energy consuming device, wherein at least one edge device of the set of edge devices identifies the at least one characteristic of energy consumption by the at least one energy consuming device based on a plurality of aspects related to energy consumption by the at least one energy consuming device.

472. The AI-based platform of claim 471, further comprising an edge device monitoring system that monitors energy consumption by at least one downstream device of the at least one energy consuming device and enforces an energy policy on the at least one downstream device based on the energy consumption.

473. The AI-based platform of claim 472, wherein the energy policy is based on a generation mechanism by which the energy associated with the energy consumption is generated.

474. 473. The AI-based platform of claim 472, wherein the edge device monitoring system is further configured to determine carbon emissions associated with the energy consumption by the at least one downstream device.

475. 1. An AI-based platform that enables intelligent orchestration and management of power and energy, comprising:

1. An AI-based platform comprising a set of artificial general intelligence (AGI) agents, each of the AGI agents assigned to manage a set of energy generation, storage, and / or consumption workloads by a set of entities.

476. 476. The AI-based platform of claim 475, wherein at least one AGI agent of the set of AGI agents is further configured to adjust at least one parameter associated with the AI-based platform based on at least one interaction between the at least one AGI agent and at least one of a human, another AGI agent, or another component of the AI-based platform.

477. 476. The AI-based platform of claim 475, wherein at least one AGI agent in the set of AGI agents monitors decisions by 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 by the at least one other AGI agent.

478. At least one AGI agent of the set of AGI agents comprises: at least one interaction between at least one human and at least one component of the AI-based platform; At least one pattern of wildlife utilization; 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 activity; at least one instance of radioactive waste processing associated with at least one nuclear power plant; at least one cyber-attack related to at least one energy resource; at least one land cleanup operation; at least one AI entity; or at least one robotic entity; 476. The AI-based platform of claim 475, wherein the AI-based platform monitors energy-related data relating to at least one of:

479. 476. The AI-based platform of claim 475, wherein at least one AGI agent of the set of AGI agents performs adjustments on data associated with at least one of a data collection process, a data storage process, a data reporting process, or a data transmission process, and wherein the adjustments are made based on at least one of an anonymity request by an individual associated with the data or a privacy request by an individual associated with the data.

480. The AI-based platform of claim 475, wherein at least one AGI agent of the set of AGI agents monitors the behavior of at least one energy resource within a networked element and updates a policy associated with the at least one energy resource based on the behavior.

481. The AI-based platform of claim 480, wherein at least one AGI agent of the set of AGI agents updates energy allocation to promote energy availability to the at least one energy resource in response to the movement.

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