AI-based energy edge platforms, systems, and methods

Abstract

This specification provides an AI-based energy edge platform with a wide range of functions, components, and capabilities aimed at managing and improving legacy infrastructure, coordinating with distributed systems, and supporting critical use cases across various enterprises. The AI-based energy edge platform may comprise a graph neural network including a set of nodes, each representing at least one distributed energy resource (DER), and a set of edges interconnecting these node sets. Each edge represents at least one energy-related characteristic between at least two nodes within the node set. The platform may incorporate emerging technologies to achieve efficiency, agility, engagement, and profitability for the ecosystem and individual energy edge nodes. Examples include forecasting, planning, and managing energy demand and utilization in larger-scale distributed environments. Examples leverage intelligent provisioning, data aggregation, and analytics, and utilize a platform to enable connectivity, communication, and trading in energy markets.

Description

Background Art

[0001] This application is a partial continuation application of PCT Application No. PCT / IB2023 / 058962 filed on September 10, 2023, claiming the priority of Indian Patent Application No. 202311057688 filed on August 28, 2023, and claiming the priority of US Provisional Patent Application No. 63 / 375,225 filed on September 10, 2022 and US Provisional Patent Application No. 63 / 537,478 filed on September 8, 2023.

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

[0003] The entire text of all the above application documents is incorporated herein by reference.

[0004] Energy remains a vital component of the global economy and is undergoing evolution and transformation, involving changes in energy generation, storage, planning, demand management, consumption, and supply systems and processes. These changes are made possible by the development and convergence of numerous diverse technologies, including more decentralized, modular, mobile, and portable energy generation and storage technologies that make energy markets more decentralized and localized, as well as various technologies that facilitate energy management in more decentralized systems (such as edge, Internet of Things, networking technologies, advanced computing and artificial intelligence technologies, and tradable technologies (blockchain, distributed ledger, smart contracts, etc.)). The convergence of more decentralized energy technologies and these networking, computing, and intelligence technologies is referred to herein as the “energy edge.”

[0005] Over the next few decades, the energy market is expected to evolve and transform from a highly centralized model reliant on fossil fuels and controlled power grids to a more decentralized model encompassing numerous locally-based generation, storage, and consumption systems. This transition will likely see years of hybrid systems where traditional grids become more intelligent and decentralized systems play a larger role. A platform is needed to facilitate the management and improvement of legacy infrastructure in conjunction with decentralized systems. [Overview of the project]

[0006] This specification provides an AI-based energy edge platform with a wide range of functions, components, and capabilities for managing and improving legacy infrastructure and coordinating with distributed systems to support critical use cases across various enterprises. The platform can incorporate novel technologies that enable efficiency, agility, engagement, and profitability of ecosystems 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 utilize AI and AI enablers such as IoT, which may be deployed in vastly high-density 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. Embodiments of the platform can leverage energy market connectivity, communication, and trade realization platforms. Embodiments can employ intelligent provisioning, data aggregation, and analytics. Among its many use cases, the platform can enable the optimization of energy generation, storage, supply, and / or enterprise consumption in businesses (e.g., buildings, data centers, factories, etc.), the integration and use of new power generation and energy storage technologies and assets (distributed energy resources, or "DERs"), the optimization of energy use across existing networks, and improvements in the digitalization of existing infrastructure and support systems.

[0007] In some embodiments, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, and includes an adaptive energy data pipeline configured to communicate data across a set of nodes in a network, each node in the set of nodes being adapted to operate on an energy dataset related to at least one of energy generation, energy storage, energy supply, or energy consumption, and at least one node in the set of nodes being configured to filter, compress, transform, error correct, and / or route at least a portion of the energy dataset based on at least one of network conditions, data size, data granularity, or a set of data content, by one or both of an algorithm or a set of rules.

[0008] In some embodiments, the technologies described herein relating to an AI-based platform, wherein the adaptive energy data pipeline is further configured to adapt the transport of data over a network and / or communication system, and the adaptation is performed based on one or more of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, and user-defined conditions.

[0009] In some embodiments, the technologies described herein relating to an AI-based platform further include adaptive energy digital twins representing one or more of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, network infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0010] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin configured to perform one or more of the following: 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.

[0011] In some embodiments, the technologies described herein relating to AI-based platforms 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 factories, or one or more vehicles in a vehicle fleet.

[0012] In some embodiments, the technologies described herein relating to AI-based platforms are configured such that the adaptive energy data pipeline further performs one or more of the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transmitting energy-related data, or maintaining the security of energy-related data.

[0013] In some embodiments, the technologies described herein relating to an AI-based platform are such that the energy dataset is based on one or more public data resources, the public data resources include one or more of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, market data resources, or e-commerce data resources.

[0014] In some embodiments, the technologies described herein relating to an AI-based platform are such that the energy dataset is based on one or more enterprise data resources, which include one or more of the following: resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0015] In some aspects, the technologies described herein relating to an AI-based platform further include at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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.

[0016] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that at least one node of a set of nodes orchestrates the delivery of energy to one or more consumption points, the delivery of energy including one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more fuel deliveries, or one or more storage energy deliveries.

[0017] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one node of a set of nodes records one or more energy-related events in a distributed ledger and / or blockchain, the one or more energy-related events including one or more of energy purchase and / or sale events, service fees relating to energy purchase and / or sale events, energy consumption events, energy generation events, energy distribution events, energy storage events, carbon emission events, carbon emission reduction events, renewable energy credit events, pollution events, or pollution reduction events.

[0018] In some respects, the technologies described herein relating to an AI-based platform are such that at least one node of the set of nodes is located in an off-grid environment, and the off-grid environment includes one or more of the following: an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0019] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the adaptive energy data pipeline further monitors either or both of the following: monitoring the overall energy consumption by at least a portion of the set of nodes, or monitoring the role of at least one node in the overall energy consumption by at least a portion of the set of nodes, and based on this monitoring, performs one or more of the following: managing the energy consumption by the set of nodes, predicting the energy consumption by the set of nodes, or provisioning resources associated with the energy consumption by the set of nodes.

[0020] In some embodiments, the technologies described herein relating to an AI-based platform include a set of nodes in a network comprising an adaptive energy data pipeline, which comprises a set of edge network devices, the set of edge network devices controlling (govern) 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.

[0021] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an adaptive energy data pipeline automatically selects the lowest-cost path for data to be communicated between a set of nodes, the selection being based on low-priority energy usage associated with the data.

[0022] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an adaptive energy data pipeline automatically selects a high-quality service route for data to be communicated between a set of nodes, the selection being based on high-priority energy usage associated with the data.

[0023] In some embodiments, the technologies described herein relating to an AI-based platform include an adaptive energy data pipeline comprising a set of artificial intelligence capabilities configured to adapt the pipeline to enable the optimization of data transmission elements in coordination with the needs of energy orchestration.

[0024] In some embodiments, the technologies described herein relating to an AI-based platform include an adaptive energy data pipeline comprising self-organizing data storage, the data storage configured to store data on a device based on one or more of the following: data patterns, data content, or data context.

[0025] In some embodiments, the technology described herein related to an AI-based platform is configured such that an adaptive energy data pipeline performs automated adaptive networking, and the adaptive networking includes 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.

[0026] In some embodiments, the technology described herein related to an AI-based platform is configured to perform enterprise contextual adaptation by automatically processing data based on one or more of an enterprise's operating context, an enterprise's transaction context, or an enterprise's financial context.

[0027] In some embodiments, the technology described herein related to an AI-based platform is further configured such that at least one node of a set of nodes adjusts communication with at least one other node of the set of nodes to adapt the reporting of data related to at least one of energy generation, energy storage, energy delivery, or energy consumption to at least one other node.

[0028] In some embodiments, the technology described herein related to an AI-based platform is further configured such that at least one node of a set of nodes adapts the reported data to at least one other node of the set of nodes, and adapting the reported data is based on the priority of consumption of the reported data.

[0029] In some embodiments, the technology described herein for an AI-based platform includes a heterogeneous set of nodes that includes at least one energy producer and at least one energy consumer, and an adaptive energy data pipeline is further configured to direct at least one of the at least one energy producer and at least one energy consumer to communicate with at least one other node of the set of nodes via at least one communication path.

[0030] In some embodiments, the technology described herein for an AI-based platform is further configured such that an adaptive energy data pipeline requests reported data from at least one node of the set of nodes, the reported data is based on a level of granularity, and the level of granularity is based on the priority of the machine associated with the reported data.

[0031] In some embodiments, the technology described herein for an AI-based platform is further configured such that an adaptive energy data pipeline prioritizes the transmission of reported data through the adaptive energy data pipeline, and the prioritization is based on the monitoring responsibilities associated with the reported data.

[0032] In some embodiments, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, and includes a set of adaptive and autonomous data processing systems, each of which is configured to collect data related to the generation, storage, or supply of energy from a set of edge devices responsible for the operation control of a set of distributed energy resources, and is configured to autonomously adjust a set of operation parameters for such operation control based on the collected data.

[0033] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that each adaptive and autonomous data processing system adapts the transport of data over a network and / or communication system, the adaptation being performed on one or more of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0034] In some embodiments, the technologies described herein relating to AI-based platforms include an adaptive energy digital twin in which each adaptive and autonomous data processing system represents one or more of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preferences.

[0035] In some embodiments, the technologies described herein relating to AI-based platforms include an adaptive energy digital twin in which each adaptive and autonomous data processing system is configured to perform one or more of the following: 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.

[0036] In some embodiments, the technologies described herein relating to AI-based platforms include an adaptive energy digital twin, each of which is configured to generate visual and / or analytical indicators of energy consumption by one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0037] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that each adaptive and autonomous data processing system performs one or more of the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transmitting energy-related data, or maintaining the security of energy-related data.

[0038] In some embodiments, the technologies described herein relating to AI-based platforms are based on energy edge data, one or more public data resources, which include one or more of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, market data resources, or e-commerce data resources.

[0039] In some embodiments, the technologies described herein relating to an AI-based platform are based on energy edge data, one or more enterprise data resources, which include one or more of the following: resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0040] In some aspects, the technologies described herein relating to AI-based platforms further include at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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, supervised learning training processes, semi-supervised learning training processes, or deep learning training processes.

[0041] In some embodiments, the technologies described herein relating to AI-based platforms are configured such that each of the adaptive and autonomous data processing systems is further configured to orchestrate the delivery of energy to one or more consumption points, the delivery of energy including one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more fuel deliveries, or one or more deliveries of stored energy.

[0042] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that each of the adaptive and autonomous data processing systems records one or more energy-related events in a distributed ledger and / or blockchain, where one or more energy-related events include one or more of the following: energy purchase and / or sale events, energy consumption events, energy generation events, energy distribution events, energy storage events, carbon emission events, carbon emission reduction events, renewable energy credit events, pollution events, or pollution reduction events.

[0043] In some embodiments, the technologies described herein relating to an AI-based platform include at least one adaptive and autonomous data processing system deployed in an off-grid environment, the off-grid environment comprising one or more off-grid energy generation systems, off-grid energy storage systems, or off-grid energy mobilization systems.

[0044] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy data pipeline configured to communicate data between a set of nodes in a network.

[0045] In some embodiments, the AI-based platform technology described herein includes a set of nodes in a network comprising an adaptive energy data pipeline, which includes a set of edge networking devices, and controls at least one of energy consumption, energy storage, energy delivery, or energy consumption by a set of operating devices controlled via the edge networking devices.

[0046] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an adaptive energy data pipeline automatically selects the lowest-cost path for data communicated across a set of nodes, the selection being based on lower-priority energy usage associated with the data.

[0047] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an adaptive energy data pipeline automatically selects a high-quality service route for data to be communicated between a set of nodes, the selection being based on high-priority energy usage associated with the data.

[0048] In some embodiments, the technologies described herein relating to an AI-based platform include an adaptive energy data pipeline comprising a set of artificial intelligence capabilities configured to adapt the pipeline to enable the optimization of data transmission elements in coordination with energy orchestration needs.

[0049] In some embodiments, the technologies described herein relating to an AI-based platform include an adaptive energy data pipeline comprising self-organizing data storage, the data storage configured to store data on a device based on one or more of the following: data patterns, data content, or data context.

[0050] In some aspects, the technologies described herein relating to an AI-based platform are configured such that an adaptive energy data pipeline performs automated adaptive networking, which includes one or more of the following: 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.

[0051] In some embodiments, the technologies described herein relating to an AI-based platform are configured to perform contextual adaptation for an enterprise by having an adaptive energy data pipeline automatically process data based on one or more of the enterprise's operating context, enterprise transaction context, or enterprise financial context.

[0052] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one adaptive and autonomous data processing system determines a set of processes based on at least one priority and / or need related to a set of distributed energy resources.

[0053] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one adaptive and autonomous data processing system coordinates communication with at least one edge device of a set of edge devices based on at least one priority and / or need relating to a set of distributed energy resources, the communication relating to the generation, storage, or delivery of energy by the distributed energy resources.

[0054] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one of an adaptive and autonomous data processing system issues commands to at least one edge device of a set of edge devices, the commands being based on an investigation of energy generation, storage, or delivery by distributed energy resources, and the commands causing at least one edge device to coordinate energy generation, storage, or delivery by at least one edge device.

[0055] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include a system configured to perform automated and coordinated governance of a set of energy entities that are operationally coupled within an energy grid and a set of distributed edge energy resources, at least one of which is functionally independent from the energy grid.

[0056] In some embodiments, the technologies described herein relating to AI-based platforms are configured such that the system further adapts the transport of data over a network and / or communication system, the adaptation being performed on one or more of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0057] In some embodiments, the technologies described herein relating to AI-based platforms further include adaptive energy digital twins representing one or more of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, production facilities of energy-dependent stakeholders, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0058] In some embodiments, the technologies described herein relating to AI-based platforms further include adaptive energy digital twins configured to perform one or more of the following: 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.

[0059] 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 factories, or one or more vehicles in a vehicle fleet.

[0060] In some embodiments, the technologies described herein relating to AI-based platforms are configured to further perform one or more of the following: extracting energy-related data, detecting and / or correcting errors in energy-related data, transforming, converting, normalizing, and / or cleansing energy-related data, analyzing energy-related data, detecting patterns, content, and / or objects in energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0061] In some aspects, the technologies described herein relating to AI-based platforms further include at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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, supervised learning training processes, semi-supervised learning training processes, or deep learning training processes.

[0062] In some aspects, the technologies described herein relating to an AI-based platform are configured such that the system further orchestrates the delivery of energy to one or more consumption points, the delivery of energy including one or more of one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more fuel deliveries, or one or more storage energy deliveries.

[0063] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the system records one or more energy-related events in a distributed ledger and / or blockchain, one or more of which include one or more energy purchase and / or sale events, service charges related to energy purchase and / or sale events, energy consumption events, energy generation events, energy distribution events, energy storage events, carbon emission events, carbon emission reduction events, renewable energy credit events, pollution events, or pollution reduction events.

[0064] In some embodiments, the technologies described herein relating to an AI-based platform include a configuration in which at least one of distributed energy edge resources is deployed in an off-grid environment, the off-grid environment comprising one or more of the following: an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0065] In some respects, the technologies described herein relating to AI-based platforms are configured to facilitate the governance of the mining environment.

[0066] In some aspects, the technologies described herein relating to AI-based platforms include: mine-level Internet of Things (IoT) sensing of mining environments; ground penetration sensing of unmined portions of mining environments; mass spectrometry and computer vision-based sensing of mined materials; asset tagging of smart containers; wearable devices for detecting the physiological state of miners; secure recording and resolution of transactions and transaction-related events; smart contracts for automatically allocating revenues derived from mining environments; and automated systems for recording, reporting, and evaluating compliance with contractual, regulatory, and legal policy requirements.

[0067] In some embodiments, the technologies described herein relating to an AI-based platform include a system comprising a set of carbon-aware energy edge solutions, the solutions comprising exploring, setting, and implementing a set of policies regarding carbon generation.

[0068] In some respects, the technologies described herein relating to AI-based platforms require the solution to monitor energy production by the mining environment in order to track the carbon emissions generated by the mining environment.

[0069] In some respects, the technologies described herein relating to AI-based platforms require the solution to offset carbon emissions from the mining environment against energy production from the said mining environment.

[0070] In some embodiments, the technologies described herein relating to an AI-based platform include a platform comprising a user interface, and the system comprising a set of automated energy policy deployment solutions, the solutions being configurable through user interaction with the user interface.

[0071] In some embodiments, the technologies described herein relating to AI-based platforms include an intelligent agent trained to generate policies related to the governance of mining environments, the intelligent agent being trained on a training set of historical data, feedback from results, and human policy-setting interactions.

[0072] In some aspects, the technologies described herein relating to AI-based platforms facilitate governance of mining environments by enabling the system to implement policies that include setting one or more of the following: setting the maximum energy use of an entity for a period of time; setting the maximum energy cost of an entity for a period of time; setting the maximum carbon production of an entity for a period of time; setting the maximum pollution emissions of an entity for a period of time; setting carbon offset requirements; setting renewable energy credit requirements; setting energy mix requirements; setting a minimum profit margin based on the energy and other marginal costs of a production entity; or setting a minimum storage baseline for an energy storage entity.

[0073] In some aspects, the technologies described herein relating to AI-based platforms include 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 set of energy transactions.

[0074] In some embodiments, the technologies described herein relating to an AI-based platform include a set of automated energy financial control solutions configured to enable users of the platform to design, generate, configure, or deploy policies relating to the control of financial factors related to one or more of energy generation, storage, distribution, or utilization.

[0075] In some embodiments, the technologies described herein relating to an AI-based platform are further configured to determine priorities relating to at least one of a set of energy entities or a set of distributed edge energy resources, the priorities being based on policies relating to at least one of the set of energy entities or the set of distributed energy resources.

[0076] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the system further performs monitoring of energy production rates by a set of energy entities and adjusts automated and coordinated governance of the set of energy entities based on the monitoring of production rates.

[0077] In some embodiments, the AI-based platform technology described herein is configured to further allocate the processing of a set of distributed edge energy resources based on at least one measurement and / or prediction of energy related to a set of energy entities.

[0078] In some embodiments, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, and includes 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 communication related to the adaptive energy data pipeline by at least one rule or algorithm, the at least one parameter being based on a set of indicators of the current network state to optimize the energy used for data communication.

[0079] In some embodiments, the technologies described herein relating to an AI-based platform include at least one parameter which is one or more of the following: routing instructions, route parameters, error correction parameters, compression parameters, storage parameters, or timing parameters.

[0080] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an adaptive energy data pipeline adapts the transport of data over a network and / or communication system, and the adaptation is performed based on one or more of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, and user-defined conditions.

[0081] In some embodiments, the technologies described herein relating to AI-based platforms further include adaptive energy digital twins representing one or more of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0082] In some embodiments, the technologies described herein relating to AI-based platforms further include adaptive energy digital twins configured to perform one or more of the following: 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.

[0083] In some embodiments, the technologies described herein relating to AI-based platforms 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 factories, or one or more vehicles in a vehicle fleet.

[0084] In some embodiments, the technologies described herein relating to an AI-based platform are further configured so that the adaptive energy data pipeline performs one or more of the following: extracting energy-related data, detecting and / or correcting errors in energy-related data, transforming, converting, normalizing, and / or cleansing energy-related data, parsing energy-related data, detecting patterns, content, and / or objects in energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0085] In some embodiments, the technologies described herein relating to AI-based platforms are based on data from one or more public data resources, where the public data resources include one or more of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, market data resources, or e-commerce data resources.

[0086] In some embodiments, the technologies described herein relating to an AI-based platform are based on data from one or more enterprise data resources, which include one or more of the following: resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0087] In some aspects, the technologies described herein relating to AI-based platforms further include at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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, supervised learning training processes, semi-supervised learning training processes, or deep learning training processes.

[0088] In some aspects, the technologies described herein relating to an AI-based platform are configured such that the adaptive energy data pipeline further orchestrates the delivery of energy to one or more consumption points, the delivery of energy including one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more fuel deliveries, or one or more storage energy deliveries.

[0089] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an adaptive energy data pipeline records one or more energy-related events in a distributed ledger and / or blockchain, where one or more energy-related events include one or more of the following: energy purchase and / or sale events, service charges related to energy purchase and / or sale events, energy consumption events, energy generation events, energy distribution events, energy storage events, carbon emission events, carbon emission reduction events, renewable energy credit events, pollution events, or pollution reduction events.

[0090] In some embodiments, the technologies described herein relating to an AI-based platform include an adaptive energy data pipeline in which at least a portion is deployed in an off-grid environment, the off-grid environment comprising one or more off-grid energy generation systems, off-grid energy storage systems, or off-grid energy mobilization systems.

[0091] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the adaptive energy data pipeline monitors either or both the overall energy consumption by at least a portion of the set of nodes and the role of at least one node in 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 the following: manage the energy consumption by the set of nodes, predict the energy consumption by the set of nodes, or provide resources related to the energy consumption by the set of nodes.

[0092] In some embodiments, the AI-based platform technology described herein includes a set of nodes in a network comprising an adaptive energy data pipeline, which includes a set of edge networking devices, and controls at least one of energy consumption, energy storage, energy delivery, or energy consumption by a set of operating devices controlled via the edge networking devices.

[0093] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an adaptive energy data pipeline automatically selects the lowest-cost path for data being communicated across a set of nodes, the selection being based on lower-priority energy usage associated with the data.

[0094] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an adaptive energy data pipeline automatically selects a high-quality service route for data being communicated across a set of nodes, the selection being based on high-priority energy usage associated with the data.

[0095] In some embodiments, the technologies described herein relating to an AI-based platform include an adaptive energy data pipeline comprising a set of artificial intelligence capabilities configured to adapt the pipeline to enable the optimization of data transmission elements in coordination with the needs of energy orchestration.

[0096] In some embodiments, the technologies described herein relating to an AI-based platform include an adaptive energy data pipeline comprising self-organizing data storage, the data storage configured to store data on a device based on one or more of the following: data patterns, data content, or data context.

[0097] In some aspects, the technologies described herein relating to an AI-based platform are configured such that an adaptive energy data pipeline performs automated adaptive networking, which includes one or more of the following: 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.

[0098] In some respects, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include a digital twin system having a digital twin of a mining environment, the digital twin including at least one parameter detected by sensors of the mining environment.

[0099] In some embodiments, the technologies described herein relating to an AI-based platform relate to at least one parameter relating to one or more of the following: the unmined portion of a mining environment, the mining of materials from a mining environment, smart container events including smart containers related to a mining environment, the physiological state of a miner related to a mining environment, transaction-related events related to a mining environment, or the compliance of a mining environment with one or more contracts, regulations, and / or legal policies.

[0100] In some embodiments, the technologies described herein relating to AI-based platforms include a digital twin system that further represents one or more of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0101] In some embodiments, the technologies described herein relating to AI-based platforms are configured such that the digital twin system further performs one or more of the following: 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.

[0102] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that a digital twin system further generates visual and / or analytical indicators of energy consumption by one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0103] In some embodiments, the technologies described herein relating to AI-based platforms have parameters based on one or more public data resources, where the public data resources include one or more of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, market data resources, or e-commerce data resources.

[0104] In some embodiments, the technologies described herein relating to an AI-based platform have parameters based on one or more enterprise data resources, which include one or more of the following: resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0105] In some aspects, the technologies described herein relating to an AI-based platform include a digital twin system comprising at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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, supervised learning training processes, semi-supervised learning training processes, or deep learning training processes.

[0106] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that a digital twin system further orchestrates the delivery of energy to one or more consumption points, the delivery of energy including one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more fuel deliveries, or one or more deliveries of stored energy.

[0107] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that a digital twin system records one or more energy-related events in a distributed ledger and / or blockchain, where one or more energy-related events include one or more of the following: energy purchase and / or sale events, energy consumption events, energy generation events, energy distribution events, energy storage events, carbon emission events, carbon emission reduction events, renewable energy credit events, pollution events, or pollution reduction events.

[0108] In some respects, the technologies described herein relating to AI-based platforms involve a digital twin system deployed in an off-grid environment, where the off-grid environment includes one or more of the following: an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0109] In some embodiments, the technologies described herein relating to an AI-based platform are such that the mining environment is a data mining environment.

[0110] In some embodiments, the technologies described herein relating to AI-based platforms are such that the mining environment is a set of resources for performing computational operations.

[0111] In some embodiments, the technologies described herein relating to an AI-based platform include the platform's mine-level Internet of Things (IoT) sensing of the mining environment, ground penetration sensing of the unmined portion of the mining environment, mass spectrometry and computer vision-based sensing of mined materials, asset tagging of smart containers, wearable devices for detecting the physiological state of miners, secure recording and resolution of transactions and transaction-related events, smart contracts for automatically allocating revenues from the mining environment, and automated systems for recording, reporting, and evaluating compliance with contractual, regulatory, and legal policy requirements.

[0112] In some embodiments, the technologies described herein relating to an AI-based platform include a platform comprising a set of carbon-aware energy edge solutions, the solutions comprising exploring, setting up, and implementing a set of policies regarding carbon generation.

[0113] In some respects, the technologies described herein relating to AI-based platforms require the solution to monitor energy production by the mining environment in order to track the carbon emissions generated by the mining environment.

[0114] In some respects, the technologies described herein relating to AI-based platforms require the solution to offset carbon emissions from mining environments against energy production from those mining environments.

[0115] In some embodiments, the technologies described herein relating to an AI-based platform include a platform comprising a user interface, a set of automated energy policy deployment solutions, and the solutions being configurable through user interaction with the user interface.

[0116] In some embodiments, the technologies described herein relating to an AI-based platform include an intelligent agent trained to generate policies related to the governance of mining environments, the intelligent agent being trained on a training set of historical data, feedback from results, and human policy-setting interactions.

[0117] In some aspects, the technologies described herein relating to AI-based platforms facilitate governance of mining environments by enabling the platform to implement policies that include setting maximum energy use for entities over a period of time, setting maximum energy costs for entities over a period of time, setting maximum carbon production for entities over a period of time, setting maximum pollution emissions for entities over a period of time, setting carbon offset requirements, setting renewable energy credit requirements, setting energy mix requirements, setting minimum profit margins based on energy and other marginal costs for production entities, or setting minimum storage baselines for energy storage entities.

[0118] In some embodiments, the technologies described herein relating to an AI-based platform include at least one parameter comprising a measurement from a sensor, the measurement of which is associated with at least one piece of equipment involved in the industrial operation of a mining environment.

[0119] In some embodiments, the AI-based platform technology described herein includes a scheduler configured to determine a schedule for generating, storing, and / or transporting energy to at least one piece of equipment related to industrial operations in a mining environment, the schedule being based on at least one parameter detected by a sensor.

[0120] In some embodiments, the AI-based platform technology described herein includes a digital twin in which at least one parameter comprises at least one characteristic of at least one dataset related to a mining environment.

[0121] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include a governance system for mining operations and a reporting system for communicating at least one parameter sensed by sensors in the mines of the mining operations, the at least one parameter relating to the mining operations' compliance with a set of labor standards.

[0122] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that the reporting system adapts the transport of data over a network and / or communication system, the adaptation being performed on one or more of the following: 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-defined conditions.

[0123] In some embodiments, the technologies described herein relating to AI-based platforms further include adaptive energy digital twins representing one or more of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, production facilities of energy-dependent stakeholders, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0124] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical indicators of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; adjusting energy data; or generating visual and / or analytical indicators of energy consumption by one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0125] In some embodiments, the technologies described herein relating to AI-based platforms are further configured to perform one or more of the following: extracting energy-related data, detecting and / or correcting errors in energy-related data, transforming, converting, normalizing, and / or cleansing energy-related data, analyzing energy-related data, detecting patterns, content, and / or objects in energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0126] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the reporting system records one or more energy-related events in a distributed ledger and / or blockchain, where one or more energy-related events include one or more of the following: energy purchase and / or sale events, service charges related to energy purchase and / or sale events, energy consumption events, energy generation events, energy distribution events, energy storage events, carbon emission events, carbon emission reduction events, renewable energy credit events, pollution events, or pollution reduction events.

[0127] In some embodiments, the technologies described herein relating to AI-based platforms are such that at least one of at least one parameter is based on one or more public data resources and one or more corporate data resources, where one or more public data resources include one or more weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, and one or more corporate data resources include one or more 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.

[0128] In some aspects, the technologies described herein relating to an AI-based platform further include at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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.

[0129] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the governance system orchestrates the delivery of energy to one or more consumption points, the delivery of energy including one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more fuel deliveries, or one or more deliveries of stored energy.

[0130] In some embodiments, the technology described herein relating to an AI-based platform includes a set of labor standards associated with at least one activity performed by a mine worker, and transmitting at least one parameter sensed by a sensor, which includes transmitting an indication for the worker to perform at least one activity sensed by the sensor.

[0131] In some embodiments, the technology described herein relating to an AI-based platform includes transmitting a set of labor standards associated with at least one object related to a mine worker, and transmitting at least one parameter sensed by a sensor, and transmitting an indication of detection of at least one object by the sensor.

[0132] In some embodiments, the technology described herein relating to an AI-based platform is configured such that a set of labor standards includes thresholds for mine characteristics, and a reporting system is further configured to communicate decisions based on a comparison between at least one parameter sensed by a sensor and the threshold.

[0133] In some embodiments, the technologies described herein relating to an AI-based platform further include a compliance recovery system configured to perform at least one compliance recovery action based on a determination that at least one parameter sensed by a sensor indicates a state of non-compliance with a set of labor standards.

[0134] In some embodiments, the technologies described herein relating to an AI-based platform further include an emergency response system configured to perform at least one emergency response action based on a determination that at least one parameter sensed by a sensor indicates the occurrence of a mine-related emergency.

[0135] In some embodiments, the technologies described herein relating to an AI-based platform further include a sensor configuration system configured to determine a sensor configuration for performing sensing of at least one parameter, the configuration being based on the compliance of a mining operation with a set of labor standards.

[0136] In some embodiments, the technology described herein relating to an AI-based platform is configured such that a set of labor standards is accessible to a sensor setting system and specified in natural language, and the sensor setting system determines sensor settings based on natural language parsing of the set of labor standards.

[0137] 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 the determination of a sensor that senses at least one parameter, wherein the at least one sensor corrective action includes one or more of the following: initiating the replacement of a sensor; initiating a diagnostic operation involving a sensor; initiating the reconfiguration of a sensor to sense at least one parameter in a different manner; initiating a request for a mine worker to perform manual sensing of a sensor; or initiating the replacement of a mine sensor with at least one other sensor in the mine to sense at least one parameter.

[0138] In some 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 a sensor indicates compliance of a mining operation to a set of labor standards, wherein the verification includes one or more of the following: verifying the calibration of sensors in the mine; verifying at least one parameter sensed by at least one other sensor in the mine based on a comparison with at least one other parameter sensed by at least one other sensor in the mine; requiring manual verification of at least one parameter by a mine worker; or requiring verification by a compliance officer that at least one parameter indicates compliance of a mining operation to a set of labor standards.

[0139] In some embodiments, the technology described herein relating to an AI-based platform further includes a worker communication interface configured to communicate with mining workers based on at least one parameter sensed by a sensor, the communication relating to compliance of mining operations with a set of labor standards.

[0140] In some embodiments, the technologies described herein relating to an AI-based platform further include a user interface configured to display a map of mining operations, the map including a display of compliance of mining operations with a set of labor standards based on at least one parameter sensed by a sensor.

[0141] In some embodiments, the technology described herein relating to an AI-based platform is configured such that a set of labor standards includes a set of work requirements for workers to perform tasks related to mining operations, and a reporting system is further configured to adapt worker assignments to tasks based on the set of work requirements.

[0142] In some embodiments, the technology described herein relating to an AI-based platform includes at least one parameter which includes a worker schedule for performing tasks related to a mining operation, and the reporting system is further configured to adapt the schedule based on the mining operation's compliance with a set of labor standards.

[0143] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the reporting system initiates at least one protocol in response to at least one parameter sensed by a sensor, the at least one protocol being based on adjusting the at least one parameter sensed by the sensor to maintain or restore compliance with a set of labor standards for mining operations.

[0144] In some embodiments, the technology described herein relating to an 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 related to at least one task of a mining operation.

[0145] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, comprising a set of edge devices, each edge device in the set configured to maintain awareness of carbon production 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.

[0146] In some embodiments, the AI-based platform technology described herein is configured such that at least one edge device in a set simulates carbon generation and / or emissions of at least one entity in a set of energy-using entities.

[0147] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that at least one edge device of a set runs a set of machine learning algorithms trained on a training dataset of carbon generation data to compute carbon generation and / or emission metrics for a set of operational entities.

[0148] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that at least one edge device of a set runs a set of machine learning algorithms trained on a training dataset of carbon generation data to compute carbon generation and / or emission metrics for a set of operational entities.

[0149] In some embodiments, the technologies described herein relating to an AI-based platform further include a set of edge devices configured to adapt the transport of data over a network and / or communication system, the adaptation being performed on one or more of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0150] In some embodiments, the technologies described herein relating to AI-based platforms further include adaptive energy digital twins representing one or more of the following: energy stakeholders, energy distribution resources, stakeholder information technology, networking infrastructure entities, production facilities of energy-dependent stakeholders, transport systems of stakeholders, market conditions, or energy use preference conditions.

[0151] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical indicators of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; adjusting energy data; or generating visual and / or analytical indicators of energy consumption by one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0152] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one edge device in the set performs one or more of the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0153] In some embodiments, the technologies described herein relating to an AI-based platform include a set of at least one edge device comprising at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, the training dataset being based on one or more human tags and / or labels, one or more human interactions with a hardware and / or software system, one or more outcomes, one or more AI-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0154] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that at least one edge device in a set orchestrates the delivery of energy to one or more consumption points, the delivery of energy including one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more fuel deliveries, or one or more storage energy deliveries.

[0155] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one edge device of the set records one or more energy-related events in a distributed ledger and / or blockchain, the one or more energy-related events including one or more of the following: energy purchase and / or sale events, service charges related to energy purchase and / or sale events, energy consumption events, energy generation events, energy distribution events, energy storage events, carbon emission events, carbon emission reduction events, renewable energy credit events, pollution events, or pollution reduction events.

[0156] In some embodiments, the technologies described herein relating to an AI-based platform include a set of at least one edge device deployed in an off-grid environment, the off-grid environment comprising one or more of the following: an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0157] In some embodiments, the AI-based platform technology described herein is configured such that at least one edge device in the set further measures changes in carbon production and / or emissions over a period of time based on a comparison of current and historical metrics of carbon production and / or emissions.

[0158] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one edge device in the set determines targets for carbon generation and / or emissions based on policies for carbon generation and / or emissions.

[0159] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that at least one edge device in the set further performs a comparison of carbon generation and / or emission metrics with carbon generation and / or emission targets and determines, based on this comparison, compliance of carbon generation and / or emission with carbon generation and / or emission policies.

[0160] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one edge device in a set determines the environmental impact of carbon generation and / or emissions based on carbon generation and / or emission metrics with respect to carbon generation and / or emission targets.

[0161] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that carbon generation and / or emissions are associated with a set of activities, and at least one edge device of the set is further configured to assign at least a portion of the carbon generation and / or emissions to at least one activity of the set of activities.

[0162] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that at least one edge device in the set further associates at least one indicator for relating carbon generation and / or emission metrics to carbon generation and / or emission targets, the indicator including one or more of the following: date and time and / or duration of carbon generation and / or emission; location of the source of carbon generation and / or emission; direction and / or speed of transport of carbon generation and / or emission; location affected by carbon generation and / or emission; physical metrics of carbon generation and / or emission; chemical composition of carbon generation and / or emission; weather patterns occurring in the area related to carbon generation and / or emission; population size of wildlife in the area related to carbon generation and / or emission; or human activities affected by carbon generation and / or emission.

[0163] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one edge device in a set transmits carbon generation and / or emission-related alerts based on a comparison of carbon generation and / or emission metrics with carbon generation and / or emission-related alert thresholds.

[0164] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one edge device in the set coordinates activities related to carbon production and / or emissions based on carbon production and / or emission metrics, thereby correcting future states of carbon production and / or emissions.

[0165] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one edge device of a set of edge devices maintains recognition by detecting, based on detection intervals, a measurement of carbon production and / or emissions associated with at least one entity of a set of energy-using entities.

[0166] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one edge device of a set of edge devices maintains 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 a set of energy-using entities.

[0167] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one edge device of a set of edge devices modifies the operation of one or more pieces of equipment and / or processes related to at least one entity of a set of energy-using entities, and the modification of operation is based on at least one measurement of carbon production and / or emissions related to at least one entity of the set of energy-using entities.

[0168] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include a digital twin that is updated by a data acquisition system that dynamically maintains a set of historical, current, and / or forecast energy demand parameters for a set of fixed entities and a set of mobile entities within a defined domain, the updates of which are performed based on the set of energy demand parameters.

[0169] In some embodiments, the technologies described herein relating to an AI-based platform are such that a set of operational entities is controlled via a set of edge networking devices linked to the set of operational entities, and energy demand parameters are derived from demand from the set of operational entities and are based on one or more of the following: a current set of aggregated data 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 and controlled via a set of edge networking devices linked to the set of operational entities, and a simulated set of aggregated data derived from demand from the set of operational entities.

[0170] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that the data acquisition system adapts the transport of data over a network and / or communication system, and the adaptation is performed based on one or more of the following conditions: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0171] In some embodiments, the technologies described herein relating to AI-based platforms include digital twins that represent one or more of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0172] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the digital twin further performs one or more of the following: providing visual and / or analytical indicators of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; adjusting energy data; or generating visual and / or analytical indicators of energy consumption by one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0173] In some embodiments, the technologies described herein relating to an AI-based platform are such that at least one of the energy demand parameters is based on one or more public data resources and one or more corporate data resources, where one or more public data resources include one or more meteorological data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, and one or more corporate data resources include one or more 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.

[0174] In some aspects, the technologies described herein relating to an AI-based platform include a digital twin comprising at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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, supervised learning training processes, semi-supervised learning training processes, or deep learning training processes.

[0175] In some respects, the technologies described herein relating to AI-based platforms are configured such that the digital twin further orchestrates the delivery of energy to one or more consumption points, where the energy delivery includes one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more fuel deliveries, or one or more deliveries of stored energy.

[0176] In some aspects, the technologies described herein relating to an AI-based platform are configured such that the digital twin further coordinates the delivery of energy to one or more consumption points based on energy delivery and / or consumption policies.

[0177] In some embodiments, the AI-based platform technology described herein is configured such that the digital twin further determines the carbon-generating and / or emission impact of energy delivery to one or more consumption points.

[0178] 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 sites based on the probability of an energy shortage at one or more consumption sites and the consequences of such an energy shortage at one or more consumption sites.

[0179] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a digital twin determines the supply of energy to one or more consumption points based on a comparison of energy availability in each of two or more energy sources, the comparison including one or more of the current and / or future amounts of energy stored by at least one of the two or more energy sources, current and / or future resource expenditures related to the acquisition, storage, and / or supply of energy by at least one of the two or more energy sources, or current and / or future demands of other energy consumers for energy in at least one of the two or more energy sources.

[0180] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that a digital twin records one or more energy-related events in a distributed ledger and / or blockchain, where one or more energy-related events include one or more of the following: energy purchase and / or sale events, service charges related to energy purchase and / or sale events, energy consumption events, energy generation events, energy distribution events, energy storage events, carbon emission events, carbon emission reduction events, renewable energy credit events, pollution events, or pollution reduction events.

[0181] In some respects, the technologies described herein relating to AI-based platforms involve a digital twin being deployed in an off-grid environment, where the off-grid environment includes one or more of the following: an off-grid energy generation system, an off-grid energy storage system, and an off-grid energy mobilization system.

[0182] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the AI-based platform measures the performance of a digital twin based on a forecast delta, the forecast delta being based on a comparison between a forecast generated by the digital twin based on a set of energy demand parameters and a measurement in a data collection system corresponding to the forecast.

[0183] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the AI-based platform updates a digital twin based on a predictive delta, the update including one or more of the following: retraining the digital twin based on the predictive delta; adjusting predictive corrections applied to the predictions of the digital twin based on the predictive delta; supplementing the digital twin with at least one other trained machine learning model; or replacing the digital twin with an alternative digital twin.

[0184] In some embodiments, the AI-based platform technology described herein is configured such that the digital twin further generates a forecast based on at least one of the energy demand parameters, and a display of the impact of at least one of the energy demand parameters on the forecast.

[0185] In several respects, the technologies described herein relating to AI-based platforms are further configured such that a digital twin determines one or more modifications to a set of energy demand parameters in order 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 a set of fixed entities and a set of mobile entities within a defined domain, or one or more modifications to a set of historical, current, and / or forecast energy demand parameters associated with a set of fixed entities and a set of mobile entities within a defined domain.

[0186] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a 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 a set of fixed entities and / or a set of mobile entities within a defined domain, wherein the one or more entity parameters include one or more of the current and / or future energy state of at least one entity, current and / or future energy consumption by at least one entity, or current and / or future activities performed by at least one entity related to energy consumption.

[0187] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that a digital twin is further configured to send a request to at least one entity from a set of fixed entities and / or a set of mobile entities within a defined domain to adjust one or more entity parameters relating to the at least one entity, the one or more entity parameters including one or more of the current and / or future energy state of the at least one entity, the current and / or future energy consumption by the at least one entity, or the current and / or future activities performed by the at least one entity relating to energy consumption.

[0188] In some embodiments, the AI-based platform technology described herein is configured such that the digital twin further performs a simulation of at least one process of at least one physical machine relating to one or both of a set of fixed entities or a set of mobile entities, and based on the simulation, outputs at least one energy demand parameter arising from the at least one process.

[0189] In some respects, the technologies described herein relating to AI-based platforms are such that a digital twin is associated with at least one physical machine relating to either a set of fixed entities or a set of mobile entities, and the digital twin is updated by a data acquisition system to generate process outputs corresponding to updated detections of the outputs of processes performed by the at least one physical machine.

[0190] In some embodiments, the technologies described herein relating to an AI-based platform are such that a digital twin is updated by a data collection system based on policies to conserve electricity and energy consumption associated with a set of energy demand parameters.

[0191] In several respects, the technologies described herein relate to an AI-based platform that enables intelligent orchestration and management of power and energy, and include a set of modular, distributed energy systems that can be configured based on local demand requirements.

[0192] In some embodiments, the technologies described herein relating to an AI-based platform predict local demand requirements by a demand forecasting algorithm operating on a set of edge networking devices linked to a set of energy-consuming systems.

[0193] 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 positioned by the AI-based platform in close proximity to the location and time of demand.

[0194] In some embodiments, the technologies described herein relating to an AI-based platform are 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.

[0195] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that at least one of a set of modular, distributed energy systems generates energy at the point of local demand by the AI-based platform.

[0196] 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 supplied by the AI-based platform to a location of demand for the modular power generation system.

[0197] 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 configured by the AI-based platform to route the supply of energy by a set of energy supply equipment to locations of demand.

[0198] In some embodiments, the technologies described herein relating to an AI-based platform include at least one of a set of modular, distributed energy systems, which are orchestrated by the AI-based platform to store energy in close proximity to the place and time of demand.

[0199] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that at least one of a set of modular distributed energy systems adapts the transport of data over a network and / or communication system, the adaptation being performed on one or more of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0200] In some embodiments, the technologies described herein relating to AI-based platforms further include adaptive energy digital twins representing one or more of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0201] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical indicators of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; adjusting energy data; or generating visual and / or analytical indicators of energy consumption by one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0202] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that at least one of a modular distributed energy system performs one or more of the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0203] In some embodiments, the technologies described herein relating to AI-based platforms are based on local demand requirements that depend on one or more public data resources and one or more corporate data resources, where one or more public data resources include one or more weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, and one or more corporate data resources include one or more 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.

[0204] In some aspects, the technologies described herein relating to AI-based platforms further include at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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, supervised learning training processes, semi-supervised learning training processes, or deep learning training processes.

[0205] In some aspects, the technologies described herein relating to an AI-based platform are configured such that at least one of a modular, distributed energy system orchestrates the delivery of energy to one or more consumption points, the delivery of energy including one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more fuel deliveries, or one or more storage energy deliveries.

[0206] In some embodiments, the AI-based platform technology described herein is configured such that a first system of a modular distributed energy system communicates with a second system of the modular distributed energy system, and orchestrates the delivery of energy to one or more consumption points by coordinating the generation, storage, supply, and / or consumption of energy by one or both of the first and second systems.

[0207] In some respects, the technologies described herein relating to AI-based platforms are configured such that at least one modular, distributed energy system coordinates the supply of energy to one or more consumption points based on carbon generation and / or emissions policies.

[0208] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that at least one of a modular distributed energy system records one or more energy-related events in a distributed ledger and / or blockchain, one or more of which include one or more energy purchase and / or sale events, service charges related to energy purchase and / or sale events, energy consumption events, energy generation events, energy distribution events, energy storage events, carbon emission events, carbon emission reduction events, renewable energy credit events, pollution events, or pollution reduction events.

[0209] In some embodiments, the technologies described herein relating to an AI-based platform include a configuration in which at least one modular distributed energy system is deployed in an off-grid environment, the off-grid environment comprising one or more off-grid energy generation systems, off-grid energy storage systems, or off-grid energy mobilization systems.

[0210] In some embodiments, the technologies described herein relating to an AI-based platform involve at least one modular distributed energy system being associated with a digital twin configured to model and / or predict one or more characteristics and / or behaviors of at least one of the modular distributed energy systems.

[0211] In some embodiments, the technologies described herein relating to an AI-based platform can be configured such that a set of modular, distributed energy systems can change the amount of reserved capacity to accommodate patterns of energy demand related to local demand requirements.

[0212] In some embodiments, the technologies described herein relating to an AI-based platform can be configured such that a set of modular, distributed energy systems can change the location of energy supply and / or access resources based on the measurement and / or forecasting of local demand requirements.

[0213] In some embodiments, the technologies described herein relating to an AI-based platform can be configured such that a set of modular, distributed energy systems can change the schedule of energy production based on the measurement and / or forecasting of local demand requirements.

[0214] In some embodiments, the technologies described herein relating to an AI-based platform are configurable such that a set of modular distributed energy systems can modify the allocation of resources associated with the set of modular distributed energy systems, the allocation being based on a subset of local demand requirements.

[0215] In some embodiments, the technologies described herein relate 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 energy patterns related to an operating process, which includes a set of resources, the set of resources being at least partially independent of the electric grid, and further configured to output 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.

[0216] In some embodiments, the technology described herein relating to an AI-based platform is such that at least one of the set of operating parameters is the output level of a distributed energy generation resource.

[0217] In some embodiments, the technology described herein relating to an AI-based platform is such that at least one of the set of operating parameters is a target storage level of distributed energy storage resources.

[0218] In some embodiments, the technology described herein relating to an AI-based platform is such that at least one of the set of operating parameters is the timing of the supply of distributed energy supply resources.

[0219] In some embodiments, the technologies described herein relating to AI-based platforms are configured such that the artificial intelligence system further adapts the transport of data over a network and / or communication system, and the adaptation is performed based on one or more of the following conditions: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0220] In some embodiments, the technologies described herein relating to AI-based platforms further include adaptive energy digital twins representing one or more of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0221] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin configured to perform one or more of the following: providing visual and / or analytical indicators of energy consumption by one or more energy consumers; filtering energy data; highlighting energy data; adjusting energy data; or generating visual and / or analytical indicators of energy consumption by one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0222] In some embodiments, the technologies described herein relating to AI-based platforms are further configured to enable an artificial intelligence system to perform one or more of the following: extracting energy-related data, detecting and / or correcting errors in energy-related data, transforming, converting, normalizing, and / or cleansing energy-related data, analyzing energy-related data, detecting patterns, content, and / or objects in energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0223] In some embodiments, the technologies described herein relating to AI-based platforms have at least one of their operating parameters based on one or more public data resources or one or more enterprise data resources, where one or more public data resources include one or more weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, and one or more enterprise data resources include one or more 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.

[0224] In some aspects, the technologies described herein relating to AI-based platforms include an artificial intelligence system that is trained 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.

[0225] In some aspects, the technologies described herein relating to an AI-based platform are configured such that an artificial intelligence system orchestrates the delivery of energy to one or more consumption points, the delivery of energy including one or more of one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more fuel deliveries, or one or more storage energy deliveries.

[0226] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the artificial intelligence system records one or more energy-related events in a distributed ledger and / or blockchain, one or more of which include one or more energy purchase and / or sale events, service charges related to energy purchase and / or sale events, energy consumption events, energy generation events, energy distribution events, energy storage events, carbon emission events, carbon emission reduction events, renewable energy credit events, pollution events, or pollution reduction events.

[0227] In some respects, the technologies described herein relating to AI-based platforms involve an artificial intelligence system deployed in an off-grid environment, where the off-grid environment includes one or more of the following: an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0228] In some embodiments, the AI-based platform technology described herein is configured such that the artificial intelligence system further determines the environmental impact of carbon generation and / or emissions related to the operating process on areas related to the operating process.

[0229] In some embodiments, the technologies described herein relating to AI-based platforms are configured such that the artificial intelligence system further evaluates compliance of an operational process with either or both of the following: carbon generation and / or emission policies, or a set of labor standards related to the operational process.

[0230] In some embodiments, the technologies described herein relating to AI-based platforms are configured such that the artificial intelligence system further adjusts a set of operating parameters to provide energy generation, storage, and / or consumption related to the operating process, based on either or both of carbon generation and / or emission policies, or a set of labor standards related to the operating process.

[0231] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the artificial intelligence system sends a message to at least one edge device of a set of edge devices related to an operating process, the message including a request to adjust the operation of at least one of the at least one edge device based on a set of operating parameters.

[0232] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the artificial intelligence system receives an index of the current and / or predicted energy state of at least one edge device from at least one edge device of a set of edge devices related to an operating process, and the set of operating parameters is based on the index of the current and / or predicted energy state of at least one edge device.

[0233] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that the artificial intelligence system determines a set of operating parameters based on the output of a digital twin representing at least one edge device of a set of edge devices related to an operating process, the output of the digital twin indicating the current and / or predicted energy state of at least one edge device.

[0234] In some aspects, the technologies described herein relating to an AI-based platform are further configured such that an artificial intelligence system orchestrates a set of modular, distributed energy systems for generating, storing, and / or supplying energy, the orchestration being carried out based on a set of operational parameters and local demand requirements.

[0235] In some embodiments, the technologies described herein relating to an AI-based platform include an analysis of energy patterns related to an operating process, and an analysis of the availability of backup power sources available in response to failures of at least a portion of the electric grid.

[0236] In some embodiments, the technology described herein relating to an AI-based platform includes an analysis of energy patterns related to an operating process, an analysis of at least one auxiliary function related to a set of resources, and a set of operating parameters, at least one operating parameter related to the at least one auxiliary function.

[0237] In some embodiments, the technologies described herein relate 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 manage 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 that operate in local data communication with a set of energy generation facilities, energy storage facilities, energy supply facilities, or energy consumption systems.

[0238] In some embodiments, the AI-based platform technology described herein, at configuration time in the policy and governance engine, automatically applies policies associated with energy generation commands by at least one edge device to control energy generation by at least one energy generation system controlled via an edge device.

[0239] In some embodiments, the AI-based platform technology described herein, at configuration time in the policy and governance engine, automatically applies policies associated with energy consumption commands by at least one edge device to control energy consumption by at least one energy consumption system controlled via the edge device.

[0240] In some embodiments, the AI-based platform technology described herein, at configuration time in the policy and governance engine, automatically applies policies associated with energy supply orders by at least one edge device to control energy supply by at least one energy supply system controlled via the edge device.

[0241] In some embodiments, the AI-based platform technology described herein, at configuration time in the policy and governance engine, automatically applies policies associated with energy storage orders by at least one edge device to control energy storage by at least one energy storage system controlled via an edge device.

[0242] In some respects, the technologies described herein relating to AI-based platforms are configured such that the policy and governance engine operates on a set of stored policy templates to construct policies.

[0243] In some embodiments, the technologies described herein relating to AI-based platforms automatically generate a set of recommended policies for presentation to a policy and governance engine, based on a dataset of historical policies, a dataset representing the operational state and / or configuration of a set of distributed energy resources, and a set of historical results.

[0244] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the policy and governance engine adjusts rules and / or policies based on at least one contextual factor, the at least one contextual factor including at least one of historical data of energy transactions, at least one operational factor, at least one market factor, at least one expected market behavior, or at least one expected customer behavior.

[0245] In some embodiments, 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 a network and / or communication system, and the adaptation is performed on at least one of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0246] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin representing at least one of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0247] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical indicators of energy consumption by at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0248] 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 at least one of at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.

[0249] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the policy and governance engine performs at least one of the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0250] In some embodiments, the technologies described herein relating to an AI-based platform include at least one of the following: rules and / or policies, which are based on at least one public data resource, which includes at least one of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychological data resources, market data resources, or e-commerce data resources.

[0251] In some embodiments, the technologies described herein relating to an AI-based platform include at least one of the rules and / or policies being based on at least one enterprise data resource, which includes at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0252] In some embodiments, the technologies described herein relating to an AI-based platform further include at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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.

[0253] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that a policy and governance engine orchestrates the delivery of energy to at least one consumption point, the delivery of energy including at least one of 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 stored energy.

[0254] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the policy and governance engine records at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one of the following: an energy purchase and / or sale event, a service charge related to 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 event, a carbon emission reduction event, a renewable energy credit event, a pollution event, or a pollution reduction event.

[0255] In some respects, the technologies described herein relating to AI-based platforms include a policy and governance engine located in an off-grid environment, where the off-grid environment includes at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0256] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the policy and governance engine generates and / or executes at least one smart contract, each of which applies rules and / or policies to at least one energy-related transaction.

[0257] In some embodiments, the technologies described herein relating to an AI-based platform are such that a set of rules and / or policies is based on at least one purpose associated with a set of workloads for energy generation, storage, and / or consumption, and the policy and governance engine is further configured to deploy updates to the set of rules and / or policies to a set of edge devices, based on the purpose.

[0258] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a policy and governance engine deploys at least one instruction to a set of edge devices to adapt at least one operating parameter related to at least one industrial machine and / or industrial process controlled by the set of edge devices.

[0259] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include a set of edge devices configured to communicate with at least one energy generating facility, energy storage facility, and / or energy consumption system and to automatically execute a set of pre-configured policies that manage the energy generation, energy storage, or energy consumption of each energy generating facility, energy storage facility, or energy consumption system.

[0260] In some embodiments, the technologies described herein relating to an AI-based platform are a set of contextual policies in which policies are automatically executed and adjusted based on the current status of a set of energy-generating entities in an energy grid.

[0261] In some embodiments, the technologies described herein relating to an AI-based platform are a set of contextual policies that are automatically executed and coordinate based on the current state of a set of energy-generating entities in an energy-generating environment, including an energy grid and a set of distributed energy resources operating independently of the energy grid.

[0262] In some embodiments, the technologies described herein relating to an AI-based platform are a set of contextual policies in which policies are automatically executed and adjusted based on the current state of a set of energy storage entities in an energy grid.

[0263] In some embodiments, the technologies described herein relating to an AI-based platform are a set of contextual policies that automatically execute 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 operating independently of the energy grid, and a set of contextual policies that automatically execute policies that adjust based on the current state of a set of energy supply entities in an energy grid.

[0264] In some embodiments, the technologies described herein relating to an AI-based platform are a set of contextual policies that are automatically executed and coordinate 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 operating independently of the energy grid.

[0265] In some embodiments, the technologies described herein relating to an AI-based platform are a set of contextual policies that are automatically executed and adjusted based on the current state of a set of energy-consuming entities that consume energy from the energy grid.

[0266] In some embodiments, the technologies described herein relating to an AI-based platform are a set of contextual policies, which are automatically executed and coordinated based on the current state of a set of energy-consuming entities that consume energy from the energy grid and a set of distributed energy resources operating independently of the energy grid.

[0267] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a set of edge devices adjust a set of pre-configured policies based on at least one contextual factor, the at least one contextual factor including at least one of historical data of energy transactions, at least one operational factor, at least one market factor, at least one expected market behavior, or at least one expected customer behavior.

[0268] In some embodiments, the technologies described herein relating to an AI-based platform further include an edge device configured to adapt the transport of data over a network and / or communication system, the adaptation being performed on at least one of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0269] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin representing at least one of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0270] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical indicators of energy consumption by at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0271] 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 at least one of at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.

[0272] In some embodiments, the technologies 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 the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

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

[0274] In some embodiments, the technologies described herein relating to an AI-based platform include at least one of the following: at least one of the pre-configured policies is based on at least one enterprise data resource, which includes at least one of the following: resource planning data, sales data and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0275] In some embodiments, the technologies described herein relating to an AI-based platform include at least one edge device comprising at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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.

[0276] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one edge device orchestrates the delivery of energy to at least one consumption point, the delivery of energy including at least one of at least one fixed transmission line, at least one instance of wireless energy transmission, at least one fuel delivery, or at least one delivery of stored energy.

[0277] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one of the edge devices records at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one of the following: an energy purchase and / or sale event, a service charge related to 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 event, a carbon emission reduction event, a renewable energy credit event, a pollution event, or a pollution reduction event.

[0278] In some embodiments, the technologies described herein relating to an AI-based platform include an off-grid environment in which at least one edge device is deployed, and the off-grid environment includes at least one off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0279] In some embodiments, the AI-based platform technology described herein is configured such that a set of edge devices further determines 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 updates the execution of a set of pre-configured policies based on the at least one pattern.

[0280] In some embodiments, the AI-based platform technology described herein is configured such that at least one edge device from a set of edge devices manages the operation of an industrial facility, and a set of pre-configured policies is based on at least one energy target associated with the industrial facility.

[0281] In some embodiments, the technologies described herein relating to an AI-based platform include at least one energy generation facility, energy storage facility, and / or energy consumption system located in a geographical area, and a set of pre-configured policies based on at least one energy objective related to the geographical area.

[0282] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that a set of edge devices automatically executes a set of predefined policies by coordinating at least one of the following: the allocation of energy resources associated with at least one energy generation facility, energy storage facility, and / or energy consumption system, or the scheduling of processes performed by at least one energy generation facility, energy storage facility, and / or energy consumption system.

[0283] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include a machine learning system trained on a set of energy intelligence data and deployed on edge devices, the machine learning system being configured to receive additional training from the edge devices to improve energy management.

[0284] In some embodiments, the technologies described herein relating to AI-based platforms include energy management that involves managing the generation of energy by a set of distributed energy-generating resources.

[0285] In some embodiments, the technologies described herein relating to an AI-based platform include energy management, which involves managing energy storage by a set of distributed energy storage resources.

[0286] In some embodiments, the technologies described herein relating to AI-based platforms include energy management, which involves managing the supply of energy through a set of distributed energy supply resources.

[0287] In some embodiments, the technologies described herein relating to an AI-based platform include energy management, which involves managing energy consumption by a set of distributed energy-consuming resources.

[0288] In some embodiments, the technologies described herein relating to AI-based platforms are based 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.

[0289] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that a machine learning system adapts the transport of data over a network and / or communication system, the adaptation being performed on at least one of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0290] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin representing at least one of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0291] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical indicators of energy consumption by at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0292] 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 at least one of at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.

[0293] In some embodiments, the technologies described herein relating to AI-based platforms are further configured so that the machine learning system performs at least one of the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0294] In some embodiments, the technologies described herein relating to an AI-based platform are based on energy intelligence data, which is based on at least one public data resource, the at least one public data resource including at least one of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, market data resources, or e-commerce data resources.

[0295] In some embodiments, the technology described herein related to an AI-based platform is such that energy intelligence data is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0296] In some embodiments, the technology described herein related to an AI-based platform is such that a machine learning system is further trained based on a training dataset, and the training dataset is based on at least one of at least one human tag and / or label, at least one human interaction with a hardware and / or software system, at least one result, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0297] In some embodiments, the technology described herein related to an AI-based platform is such that a machine learning system is further configured to orchestrate the delivery of energy to at least one consumption point, and the delivery of energy includes at least one of at least one fixed power line, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy.

[0298] In some aspects, the technology described herein related to an AI-based platform is further configured such that a machine learning system records at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of 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.

[0299] In some aspects, the technology described herein related to an AI-based platform has an edge device deployed in an off-grid environment, and the off-grid environment includes at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0300] In some aspects, the technology described herein related to an AI-based platform has an edge device disposed in proximity to at least one entity that generates, stores, supplies, and / or uses energy.

[0301] In some aspects, the technology described herein related to an AI-based platform has an edge device provide information regarding the energy state and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

[0302] In some aspects, the technology described herein related to an AI-based platform has an edge device include and / or manage at least one sensor of a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

[0303] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the edge device is associated with a circumstance and / or environment, and the edge device performs additional training of a machine learning system in response to changes in the circumstance and / or environment.

[0304] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the edge device performs additional training of the machine learning system based on the machine learning system's determination of model drift.

[0305] In some embodiments, the AI-based platform technologies described herein involve additional training based on the set of energy intelligence data in which the machine learning system was initially trained, and additional energy intelligence data in which the machine learning system has not yet been trained.

[0306] In some embodiments, the technologies described herein relating to AI-based platforms include additional training, which involves adding a machine learning system to an ensemble that includes at least one other artificial intelligence system.

[0307] In some embodiments, the technologies described herein relating to an AI-based platform are such that the set of energy intelligence data is based on at least one energy-related policy and / or rule, and additional training is based on a change in at least one energy-related policy and / or rule.

[0308] In some embodiments, the technology described herein relates to an AI-based platform for enabling intelligent orchestration and management of power and energy, and includes a set of edge devices, which includes an artificial intelligence system configured to process data handled by the edge devices and, based on that data, determine combinations of energy generation, storage, supply, and / or consumption characteristics for a set of systems communicating locally with the edge devices, and output a dataset showing the proportions of those combinations.

[0309] In some embodiments, the technologies described herein relating to an AI-based platform have output datasets that show the proportion of energy generated by the energy grid and the proportion of energy generated by a set of distributed energy resources operating independently of the energy grid.

[0310] In some embodiments, the technologies described herein relating to an AI-based platform have output datasets that show the proportion of energy generated by renewable energy resources and the proportion of energy generated by non-renewable resources.

[0311] In some embodiments, the AI-based platform technology described herein provides an output dataset that shows the percentage of energy generation by type for each interval in a series of time intervals.

[0312] In some embodiments, the AI-based platform described herein provides an output dataset that shows carbon production related to energy production by energy type in combinations of energy within each interval of a series of time intervals.

[0313] In some embodiments, the AI-based platform technology described herein provides an output dataset showing carbon emissions associated with energy generation by energy type in combinations of energy within each time interval of a series of time intervals.

[0314] In some embodiments, the technologies described herein relating to an AI-based platform further include an edge device configured to adapt the transport of data over a network and / or communication system, the adaptation being performed on at least one of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0315] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin representing at least one of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0316] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical indicators of energy consumption by at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0317] 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 at least one of at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.

[0318] In some embodiments, the technologies 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 the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0319] In some embodiments, the technologies described herein relating to an AI-based platform are based on data from at least one public data resource, the public data resource including at least one of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, market data resources, or e-commerce data resources.

[0320] In some embodiments, the technologies described herein relating to an AI-based platform are based on data from at least one enterprise data resource, the enterprise data resource including at least one of resource planning data, sales data and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0321] In some embodiments, the technology described herein for an AI-based platform includes at least one of the edge devices containing at least one AI-based model and / or algorithm, where the at least one AI-based model and / or algorithm is trained based on a training dataset, and the training dataset is based on at least one human tag and / or label, at least one human interaction with a hardware and / or software system, at least one result, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or at least one of a deep learning training process.

[0322] In some embodiments, the technology described herein for an AI-based platform is further configured such that at least one of the edge devices orchestrates the delivery of energy to at least one point of consumption, and the delivery of energy includes at least one of at least one fixed transmission line, at least one instance of wireless energy transmission, at least one fuel delivery, or at least one delivery of stored energy.

[0323] In some embodiments, the technology described herein for an AI-based platform is further configured such that at least one of the edge devices records at least one energy-related event in a distributed ledger and / or blockchain, and the at least one energy-related event includes at least one of an energy purchase and / or sale event, a service fee related to 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.

[0324] In some embodiments, the technologies described herein relating to an AI-based platform include an off-grid environment in which at least one edge device is deployed, and the off-grid environment includes at least one off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0325] In some embodiments, the AI-based platform technologies described herein involve at least a portion of a set of edge devices being positioned in close proximity to at least one entity that generates, stores, supplies, and / or uses energy.

[0326] In some embodiments, the technologies described herein relating to an AI-based platform involve a set of edge devices providing information about the energy state and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

[0327] In some embodiments, the technologies described herein relating to an AI-based platform include a set of edge devices which include and / or manage at least one sensor of a set of sensors, and the set of sensors is associated with a set of infrastructure assets configured to generate, store, supply, and / or use energy.

[0328] In some embodiments, the technologies described herein relating to an AI-based platform are based on a combination of energy generation, storage, supply, and / or consumption characteristics, which are associated with at least one energy demand requirement related to a set of edge devices.

[0329] In some embodiments, the technologies described herein relating to AI-based platforms are based on a combination of energy generation, storage, supply, and / or consumption characteristics, and on prioritizing the collection, storage, transport, and / or use of energy associated with each energy source related to a set of edge devices.

[0330] In some embodiments, the technologies described herein relating to AI-based platforms are based on a combination of energy generation, storage, supply, and / or consumption characteristics, and are associated with storage, transport, and / or use schedules for each energy source related to a set of edge devices.

[0331] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include a data processing system configured to merge at least one entity of a grid dataset of generation, storage, supply, or consumption of energy grid entities with at least one entity of a dataset of generation, storage, supply, and / or consumption of off-grid energy entities.

[0332] In some embodiments, the AI-based platform technologies described herein are configured such that the data processing system automatically synchronizes energy grid entity data with off-grid energy entity data in time.

[0333] In some embodiments, the AI-based platform technology described herein is configured such that a data processing system automatically collects off-grid energy entity sensor data from a set of edge devices that control a set of off-grid energy entities.

[0334] In some embodiments, the technologies described herein relating to an AI-based platform are configured to automatically normalize energy grid entity data and off-grid energy entity data so that the data processing system presents the data according to a set of common units.

[0335] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that the data processing system adapts the transport of data over a network and / or communication system, and the adaptation is performed on at least one of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0336] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin representing at least one of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0337] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical indicators of energy consumption by at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0338] 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 at least one of at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.

[0339] In some embodiments, the technologies described herein relating to AI-based platforms are further configured to perform at least one of the following: extracting energy-related data, detecting and / or correcting errors in energy-related data, transforming, converting, normalizing, and / or cleansing energy-related data, analyzing energy-related data, detecting patterns, content, and / or objects in energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0340] In some embodiments, the technologies described herein relating to an AI-based platform further include at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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.

[0341] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the data processing system further orchestrates the delivery of energy to at least one consumption point, the delivery of energy including at least one of 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 stored energy.

[0342] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a data processing system records at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one of the following: an energy purchase and / or sale event, a service charge related to 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 event, a carbon emission reduction event, a renewable energy credit event, a pollution event, or a pollution reduction event.

[0343] In some respects, the technologies described herein relating to AI-based platforms include at least one entity of a dataset of off-grid energy generation, storage, and / or consumption, which is located in an off-grid environment, and which includes at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0344] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a data processing system intelligently orchestrates and manages power and / or energy based on a dataset of energy generation, storage, and / or consumption data for a set of infrastructure assets, the dataset being generated at least in part by a set of sensors included in and / or managed by a set of edge devices.

[0345] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a data processing system manages at least one of the following: energy generation by a set of distributed energy generation resources, energy storage by a set of distributed energy storage resources, energy supply by a set of distributed energy supply resources, or energy consumption by a set of distributed energy consumption resources.

[0346] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a data processing system intelligently orchestrates and manages the power and / or energy of a set of entities, the set of entities including at least one of weather data resources, satellite data resources, census, population, demographic, and / or psychological data resources, market data resources, or e-commerce data resources.

[0347] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a data processing system runs at least one algorithm that performs a simulation of energy consumption by at least one of entities, the simulation being based on a dataset containing alternative state or event parameters for at least one of 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 changes in the price of operations or activities in which energy is consumed.

[0348] In some embodiments, the technologies described herein relating to an AI-based platform include a policy and governance engine configured in which a data processing system deploys a set of rules and / or policies to at least one edge device that communicates locally with at least one of the entities, the edge device being configured to manage at least one of the entities based on the rules and / or policies.

[0349] In some embodiments, the technology described herein relating to an AI-based platform includes a data processing system comprising an analysis system that represents the operating parameters and current state of at least one entity based on a set of sensed parameters, the set of sensed parameters being generated by a set of edge devices adjacent to at least one of the entities, and the analysis system being configured to provide recommendations relating to at least one of the entities or at least one additional available entity.

[0350] In some embodiments, the technology described herein relating to an AI-based platform includes an artificial intelligence system trained on historical datasets relating to the energy generation, storage, and / or utilization of an operational process associated with at least one of entities, wherein the data processing system is further configured to analyze the energy patterns of the operational process and output predictions of the energy requirements of the operational process based on the current state and / or information associated with at least one of the entities.

[0351] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the data processing system merges at least one entity of a backup and / or auxiliary energy generation, storage, supply, or consumption grid dataset with an energy grid entity generation, storage, supply, or consumption grid dataset and an off-grid energy entity generation, storage, supply, and / or consumption dataset.

[0352] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that a data processing system coordinates the development of energy grid resources and / or off-grid energy resources based on the fusion of generation, storage, supply, or consumption grid datasets of energy grid entities and generation, storage, supply, and / or consumption datasets of off-grid energy entities.

[0353] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include a set of autonomous orchestration systems for improving the supply 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 the peak power requirements at the consumption points, the continuity of power demand at the consumption points, and the types of energy available at the consumption points.

[0354] In several respects, the technologies described herein relating to AI-based platforms involve a set of autonomous orchestration systems orchestrating the delivery of defined types of energy generation capabilities to consumption points.

[0355] In some embodiments, the technologies described herein relating to an AI-based platform involve a set of autonomous orchestration systems orchestrating the delivery of defined types of energy storage capacity to consumption points.

[0356] In some embodiments, the technologies described herein relating to AI-based platforms have the type of energy available determined, at least in part, based on a set of operational suitability parameters.

[0357] In some embodiments, the technologies described herein relating to AI-based platforms determine, at least in part, the type of energy available based on a set of governance parameters.

[0358] In some embodiments, the technologies described herein relating to an AI-based platform have a set of governance parameters relating to the utilization of renewable energy resources.

[0359] In some respects, the technologies described herein relating to AI-based platforms have a set of governance parameters that relate to carbon production or emissions.

[0360] In some embodiments, the technologies described herein relating to an AI-based platform further include at least one of a set of autonomous end orchestration systems configured to adapt the transport of data over a network and / or communication system, the adaptation being performed on at least one of congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0361] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin representing at least one of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0362] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical indicators of energy consumption by at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0363] In some embodiments, the technology described in the specification 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 at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.

[0364] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one of a set of autonomous orchestration systems performs at least one of the following: 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 energy-related data; compressing energy-related data; streaming energy-related data; filtering energy-related data; loading and / or storing energy-related data; routing and / or transporting energy-related data; or maintaining the security of energy-related data.

[0365] In some embodiments, the technologies described herein relating to an AI-based platform are such that at least one of the consumer attributes is based on at least one public data resource, the public data resource includes at least one of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychological data resources, market data resources, or e-commerce data resources.

[0366] In some embodiments, the technologies described herein relating to an AI-based platform are such that at least one of the consumer attributes is based on at least one enterprise data resource, which includes at least one of the following: resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0367] In some embodiments, the technologies described herein relating to an AI-based platform further include at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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.

[0368] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one of a set of autonomous orchestration systems orchestrates the delivery of energy to at least one consumption point, the delivery of energy including at least one of 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 stored energy.

[0369] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one of a set of autonomous orchestration systems records at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one of 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 event, a carbon emission reduction event, a renewable energy credit event, a pollution event, or a pollution reduction event.

[0370] In some respects, the technologies described herein relating to AI-based platforms are configured such that at least one of a set of autonomous orchestration systems is deployed in an off-grid environment, the off-grid environment includes at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0371] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a 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 the configuration of a set of energy generation equipment, energy storage equipment, energy supply equipment, or energy consumption systems and a set of edge devices operating with local data communications.

[0372] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a set of autonomous orchestration systems determines the supply of a 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 containing at least one alternative state or event parameter of at least one energy consumer reflecting alternative consumption scenarios, and the simulation being based on a demand response model that explains how energy demand responds to changes in the price of energy or changes in the price of the operation or activity on which the energy is consumed.

[0373] In some embodiments, the technologies described herein relating to an AI-based platform improve the supply of heterogeneous sets of energy types to consumption points by having a set of autonomous orchestration systems match each of the heterogeneous sets of energy types with at least one consumer associated with the consumption point.

[0374] In some embodiments, the technologies described herein relating to AI-based platforms improve the supply of a heterogeneous set of energy types to consumption sites by having a set of autonomous orchestration systems determine the development of additional energy sources of one or more energy types, and the development is based on forecasts of energy demand requirements associated with the consumption sites.

[0375] In some embodiments, the technologies described herein relating to AI-based platforms improve the supply of a heterogeneous set of energy types to consumption sites by having a set of autonomous orchestration systems compare the characteristics of energy demand associated with consumption sites with the characteristics of each energy type in a heterogeneous set of energy types.

[0376] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include an intelligent agent trained on a dataset of expert interactions with energy supply systems, the intelligent agent being trained to generate at least one recommendation and / or instruction with respect to the optimization of at least one energy objective and at least one other objective.

[0377] In some respects, the technologies described herein relating to AI-based platforms are other than the operational objectives of the enterprise.

[0378] In some embodiments, the technologies described herein relating to an AI-based platform operate based on status data from a set of edge devices that control a set of energy-generating resources, in which an intelligent agent operates.

[0379] In some embodiments, the technologies described herein relating to an AI-based platform operate based on status data from a set of edge devices over which a set of energy-consuming resources are controlled by an intelligent agent.

[0380] In some embodiments, the technologies described herein relating to an AI-based platform involve an intelligent agent operating based on status data from a set of edge devices that control a set of energy storage resources.

[0381] In some embodiments, the technologies described herein relating to an AI-based platform operate based on status data from a set of edge devices that control a set of energy supply resources, in which an intelligent agent operates.

[0382] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an intelligent agent adapts the transport of data over a network and / or communication system, the adaptation being performed on at least one of the following: 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-defined conditions.

[0383] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin representing at least one of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0384] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical indicators of energy consumption by at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0385] 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 at least one of at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.

[0386] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an intelligent agent performs at least one of the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0387] In some embodiments, the technologies described herein relating to an AI-based platform are such that the dataset is based on at least one public data resource, the public data resource includes at least one of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, market data resources, or e-commerce data resources.

[0388] In some embodiments, the technologies described herein relating to an AI-based platform are such that the dataset is based on at least one enterprise data resource, which includes at least one of the following: resource planning data, sales data and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0389] In some aspects, the technologies described herein relating to AI-based platforms include an intelligent agent trained 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.

[0390] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that an intelligent agent further orchestrates the delivery of energy to at least one consumption point, the delivery of energy including at least one of 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 stored energy.

[0391] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an intelligent agent records at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one of the following: an energy purchase and / or sale event, a service charge related to 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 event, a carbon emission reduction event, a renewable energy credit event, a pollution event, or a pollution reduction event.

[0392] In some embodiments, the technologies described herein relating to an AI-based platform include an intelligent agent deployed in an off-grid environment, the off-grid environment comprising at least one of an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0393] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that an intelligent agent is positioned in close proximity to at least one entity that generates, stores, supplies, and / or uses energy.

[0394] In some embodiments, the technologies described herein relating to an AI-based platform include an intelligent agent that provides information regarding the energy state and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

[0395] In some embodiments, the technologies described herein relating to an AI-based platform include an intelligent agent managing at least one sensor from 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.

[0396] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an intelligent agent manages at least one processing task associated with at least one device, and at least one recommendation and / or instruction includes coordinating at least one processing task based on at least one energy purpose and / or at least one other purpose.

[0397] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the intelligent agent further migrates between at least two devices and, while residing in each of the at least two devices, applies at least one recommendation and / or instruction to the device in which the intelligent agent resides.

[0398] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that an intelligent agent exchanges information with at least one other intelligent agent, the information being based on at least one recommendation and / or instruction, or at least one energy purpose and / or at least one other purpose, or both.

[0399] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that recommendations and / or instructions are associated with at least one device, and an intelligent agent exchanges collected and / or determined data associated with at least one other intelligent agent with at least one other intelligent agent.

[0400] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, comprising an artificial intelligence system trained on a set of results of energy generation, energy storage, energy supply, and / or energy consumption, 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 to provide recommendations that include at least one operating parameter that satisfies both the energy demand of a mobile entity or the energy demand of a stationary location in a given domain.

[0401] In some embodiments, the technologies described herein relating to AI-based platforms include a given domain that includes a given geographical location and a given period of time.

[0402] In some embodiments, the AI-based platform technology described herein has at least one operating parameter that indicates a generation command for a set of energy generation resources.

[0403] In some embodiments, the AI-based platform technology described herein has at least one operating parameter that indicates a storage command for a set of energy storage resources.

[0404] In some embodiments, the AI-based platform technology described herein has at least one operating parameter that indicates a supply command for a set of energy supply resources.

[0405] In some embodiments, the technology described herein relating to an AI-based platform has at least one operating parameter that indicates a consumption command to a set of energy-consuming entities.

[0406] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that the artificial intelligence system adapts the transport of data over a network and / or communication system, and the adaptation is performed on at least one of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0407] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin representing at least one of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0408] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical indicators of energy consumption by at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0409] 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 at least one of at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.

[0410] In some embodiments, the technologies described herein relating to AI-based platforms are further configured so that the artificial intelligence system performs at least one of the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0411] In some embodiments, the technologies described herein relating to an AI-based platform are such that the dataset is based on at least one public data resource, the at least one public data resource includes at least one of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, market data resources, or e-commerce data resources.

[0412] In some embodiments, the technologies described herein relating to an AI-based platform are such that the dataset is based on at least one enterprise data resource, the at least one enterprise data resource includes at least one of the following: resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0413] In some aspects, the technologies described herein relating to AI-based platforms include an artificial intelligence system that is trained 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.

[0414] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the artificial intelligence system further orchestrates the delivery of energy to at least one consumption point, the delivery of energy including at least one of 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 stored energy.

[0415] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the artificial intelligence system records at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one of the following: an energy purchase and / or sale event, a service charge related to 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 event, a carbon emission reduction event, a renewable energy credit event, a pollution event, or a pollution reduction event.

[0416] In some respects, the technologies described herein relating to AI-based platforms are configured such that the artificial intelligence system is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0417] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the artificial intelligence system is located in close proximity to at least one entity that generates, stores, supplies, and / or uses energy.

[0418] In some embodiments, the technologies described herein relating to an AI-based platform include an artificial intelligence system that provides information about the energy state and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

[0419] In some embodiments, the technologies described herein relating to an AI-based platform include an artificial intelligence system that controls at least one sensor from 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.

[0420] In some embodiments, the technologies described herein relating to an AI-based platform include a given domain that comprises at least one boundary, and a dataset is constrained based on at least one boundary associated with the given domain.

[0421] In some embodiments, the technologies described herein relating to an AI-based platform are such that the recommendations are based on at least one constraint associated with at least one operating parameter, and the artificial intelligence system is trained to analyze a dataset based on at least one constraint.

[0422] In some embodiments, 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 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 being configurable to both generate and consume energy, and the configuration causes at least one distributed system to generate and / or consume energy based on the monitored local conditions.

[0423] In some embodiments, the technologies described herein relating to AI-based platforms constitute multiple distributed systems of a set of distributed systems such that an artificial intelligence system comprises multiple distributed systems such that an aggregate set of performance requirements is met across multiple distributed systems.

[0424] In some embodiments, the technologies described herein relating to AI-based platforms have aggregated performance requirements that are a set of economic performance requirements.

[0425] In some embodiments, the technologies described herein relating to AI-based platforms have aggregated performance requirements that are a set of regulatory performance requirements.

[0426] In some respects, the technologies described herein relating to AI-based platforms have aggregated performance requirements that relate to carbon generation or emissions.

[0427] In some embodiments, the technologies described herein relating to AI-based platforms have aggregated performance requirements that are a set of consumption requirements.

[0428] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that the artificial intelligence system adapts the transport of data over a network and / or communication system, and the adaptation is performed on at least one of the following: congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0429] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin representing at least one of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0430] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical indicators of energy consumption by at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0431] 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 at least one of at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.

[0432] In some embodiments, the technologies described herein relating to AI-based platforms are further configured so that the artificial intelligence system performs at least one of the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0433] In some embodiments, the technologies described herein relating to an AI-based platform are such that the dataset is based on at least one public data resource, the public data resource includes at least one of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, market data resources, or e-commerce data resources.

[0434] In some embodiments, the technologies described herein relating to an AI-based platform are such that the dataset is based on at least one enterprise data resource, the enterprise data resource includes at least one of the following: resource planning data, sales data and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0435] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the artificial intelligence system further orchestrates the delivery of energy to at least one consumption point, the delivery of energy including at least one of 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 stored energy.

[0436] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that the artificial intelligence system records at least one energy-related event in a distributed ledger and / or blockchain, the at least one energy-related event including at least one of the following: an energy purchase and / or sale event, a service charge related to 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 event, a carbon emission reduction event, a renewable energy credit event, a pollution event, or a pollution reduction event.

[0437] In some respects, the technologies described herein relating to AI-based platforms are configured such that the artificial intelligence system is deployed in an off-grid environment, and the off-grid environment includes at least one of the following: an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0438] In some aspects, the technologies described herein relating to AI-based platforms include an artificial intelligence system that is trained 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.

[0439] In some embodiments, the technologies described herein relating to an AI-based platform are configured such that the artificial intelligence system is located in close proximity to at least one entity that generates, stores, supplies, and / or uses energy.

[0440] In some embodiments, the technologies described herein relating to an AI-based platform include an artificial intelligence system that provides information about the energy state and / or energy flow of at least one entity that generates, stores, supplies, and / or uses energy.

[0441] In some embodiments, the technologies described herein relating to an AI-based platform include an artificial intelligence system that controls at least one sensor from 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.

[0442] In some embodiments, the technologies described herein relating to AI-based platforms have a recommended configuration based on at least one auxiliary power resource associated with a set of distributed systems.

[0443] In some embodiments, the technologies described herein relating to AI-based platforms have a recommended configuration based on at least one of the following: the current location and / or predicted location of at least one distributed system in a set of distributed systems, or the current location and / or predicted location of at least one energy resource associated with a set of distributed systems.

[0444] In some embodiments, the technologies described herein relating to AI-based platforms, the recommended configuration further depends on at least one of the following: local demand conditions relating to the current and / or predicted locations of at least one distributed system in a set of distributed systems, or local demand conditions relating to the current and / or predicted locations of at least one energy resource associated with a set of distributed systems.

[0445] In some embodiments, the technologies described herein relate 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 collecting and transmitting energy data from a set of edge networking devices on which a set of distributed energy entities are controlled, the data processing systems being trained on a training dataset to recognize a set of events and / or signals that indicate at least one energy pattern of the set of distributed energy entities.

[0446] In some embodiments, the technologies described herein relating to an AI-based platform include a set of distributed energy entities comprising at least one energy-generating resource.

[0447] In some embodiments, the technologies described herein relating to an AI-based platform include a set of distributed energy entities comprising at least one energy consumption entity.

[0448] In some embodiments, the technologies described herein relating to an AI-based platform include a set of distributed energy entities comprising at least one energy storage resource.

[0449] In some embodiments, the technologies described herein relating to an AI-based platform include a set of distributed energy entities comprising at least one energy supply resource.

[0450] In some embodiments, the technologies described herein relating to an AI-based platform include a training dataset that comprises historical energy generation data for a set of entities similar to entities controlled via an edge networking device.

[0451] In some embodiments, the technologies described herein relating to an AI-based platform include a training dataset that comprises historical energy consumption data for a set of entities similar to entities controlled via edge networking devices.

[0452] In some embodiments, the technologies described herein relating to an AI-based platform include a training dataset that comprises historical energy supply data for a set of entities similar to entities controlled via edge networking devices.

[0453] In some embodiments, the technologies described herein relating to an AI-based platform include a training dataset that comprises historical energy storage data for a set of entities similar to entities controlled via edge networking devices.

[0454] In some embodiments, the technologies described herein relating to AI-based platforms further include at least one adaptive and autonomous data processing system configured to adapt the transport of data over a network and / or communication system, the adaptation being performed on at least one of congestion conditions, delay and / or latency conditions, packet loss conditions, error rate conditions, transport cost conditions, quality of service (QoS) conditions, usage conditions, market factor conditions, or user-defined conditions.

[0455] In some embodiments, the technologies described herein relating to AI-based platforms further include an adaptive energy digital twin representing at least one of the following: energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy use preference conditions.

[0456] In some embodiments, the technologies described herein relating to an AI-based platform further include an adaptive energy digital twin configured to perform at least one of the following: providing visual and / or analytical indicators of energy consumption by at least one energy consumer; filtering energy data; highlighting energy data; or adjusting energy data.

[0457] In some embodiments, the technology described in the specification 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 at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.

[0458] In some embodiments, the technologies described herein relating to AI-based platforms are further configured such that at least one of an adaptive and autonomous data processing system performs at least one of the following: 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 energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining the security of energy-related data.

[0459] In some embodiments, the technologies described herein relating to an AI-based platform are such that the energy edge set is based on at least one public data resource, the public data resource includes at least one of the following: weather data resources, satellite data resources, census, population, demographic, and / or psychometric data resources, market data resources, or e-commerce data resources.

[0460] In some embodiments, the technologies described herein relating to an AI-based platform are such that the energy edge set is based on at least one enterprise data resource, the at least one enterprise data resource includes at least one of the following: resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0461] In some embodiments, the technologies described herein relating to an AI-based platform further include at least one AI-based model and / or algorithm, the at least one AI-based model and / or algorithm being trained on a training dataset, 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.

[0462] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that at least one of an adaptive and autonomous data processing system organizes the delivery of energy to at least one consumption point, the delivery of energy including 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 stored energy.

[0463] In some embodiments, the technologies described herein relating to an AI-based platform further comprises at least one adaptive and autonomous data processing system configured to record at least one energy-related event in a distributed ledger and / or blockchain, wherein the at least one energy-related event includes at least one of the following: an energy purchase and / or sale event, a service charge related to 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 event, a carbon emission reduction event, a renewable energy credit event, a pollution event, or a pollution reduction event.

[0464] In some embodiments, the technologies described herein relating to an AI-based platform include at least one of 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.

[0465] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a set of adaptive and autonomous data processing systems performs additional training on 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.

[0466] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a set of adaptive and autonomous data processing systems instructs at least one edge networking device from a set of edge networking devices to adjust operational parameters related to a set of distributed energy entities based on the recognition of events and / or signals from a set of events and / or signals.

[0467] In some embodiments, the technologies described herein relating to an AI-based platform are further configured to detect events and / or signals based on data collected from a set of edge networking devices over a period of time, and the data processing systems are trained to recognize a set of events and / or signals based on at least one feature of the period of time.

[0468] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include a data integration module that integrates energy intelligence data collected from at least one internal edge device located in the environment and at least one external edge device located outside the environment.

[0469] In some embodiments, the AI-based platform technologies described herein involve vectorizing data collected from at least one internal edge device or at least one external edge device.

[0470] In some embodiments, the AI-based platform technologies described herein involve data collected from at least one internal edge device or at least one external edge device being stored in a distributed database.

[0471] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that a data integration module determines an energy pattern based on local energy patterns associated with data collected from at least one internal edge device and at least one external edge device.

[0472] In some respects, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include a digital dynamic twin configured to model at least one of historical energy demand, current historical energy demand, or forecasted energy demand, and an AI-based digital twin update device that updates the dynamic digital twin based on a set of energy parameters.

[0473] In some embodiments, the technologies described herein relating to an AI-based platform include an AI-based digital twin update device that performs a dynamic digital twin update to determine forecasts of energy demand for a future period, the update being based on forecasts of energy demand for a future period by another AI model.

[0474] In some aspects, the technology described herein relating to an AI-based platform is such that a dynamic digital twin is associated with a device type, and an AI-based digital twin update device analyzes data associated with energy consumption by devices of the device type in order to update the dynamic digital twin to model the energy consumption by devices of the device type.

[0475] In some embodiments, the technologies described herein relating to an AI-based platform are further configured such that a dynamic digital twin models energy demand by at least one entity, the modeling being based on data showing energy consumption by at least one entity.

[0476] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, and include an energy access arbitrator that mediates access to at least one energy source by at least one energy-consuming device among a set of energy-consuming devices.

[0477] In some embodiments, 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 that communicate locally with at least one energy-consuming device in order to identify at least one feature of energy consumption by at least one energy-consuming device, wherein at least one edge device of the set of edge devices identifies at least one feature of energy consumption by at least one energy-consuming device based on a plurality of perspectives relating to energy consumption by at least one energy-consuming device.

[0478] In some embodiments, the technologies described herein relating to an AI-based platform further include an edge device monitoring system that monitors energy consumption by at least one downstream device among at least one energy-consuming device and enforces an energy policy on at least one downstream device based on energy consumption.

[0479] In some embodiments, the technologies described herein relating to AI-based platforms are based on energy policies that rely on generation mechanisms that generate energy related to energy consumption.

[0480] In some embodiments, the technology described herein relating to an AI-based platform is further configured so that the edge device monitoring system determines carbon emissions associated with energy consumption by at least one downstream device.

[0481] In some embodiments, the technologies described herein relate to an AI-based platform for enabling intelligent orchestration and management of power and energy, comprising a set of general-purpose artificial intelligence (AGI) agents, each AGI agent assigned to manage a set of energy generation, storage, and / or consumption workloads by a set of entities.

[0482] In some embodiments, the technology described herein relating to an AI-based platform is further configured such that at least one AGI agent from a set of AGI agents adjusts at least one parameter related to the AI-based platform based on at least one interaction between the at least one AGI agent and at least one of a human, another AGI agent, or another component of the AI-based platform.

[0483] In some embodiments, the technology described herein relating to an AI-based platform includes an AI where at least one AGI agent from a set of AGI agents monitors decisions made by at least one other AGI agent from the set of AGI agents and adjusts at least one parameter associated with the AI-based platform based on the decisions made by the at least one other AGI agent.

[0484] In some embodiments, the technologies described herein relating to an AI-based platform include an AGI agent, at least one of a set of AGI agents, monitoring energy-related data relating to at least one of the following: 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 related to at least one nuclear power plant; at least one cyberattack related to at least one energy resource; at least one land cleanup operation; at least one AI entity; or at least one robotic entity.

[0485] In some embodiments, the technologies described herein relating to an AI-based platform include an AGI agent in which at least one of a set of AGI agents performs data reconciliation related to at least one of a data collection process, a data storage process, a data reporting process, or a data transmission process, wherein the reconciliation is performed based on at least one of an anonymity request or a privacy request from an individual relating to the data.

[0486] In some embodiments, the technology described herein relating to an AI-based platform includes an AGI agent, at least one of a set of AGI agents, that monitors the movement of at least one energy resource within a networked element and updates the policy associated with that energy resource based on that movement.

[0487] In some embodiments, the technology described herein relating to an AI-based platform includes an AGI agent, at least one of a set of AGI agents, updating energy allocations in response to movement to facilitate energy availability to at least one energy resource.

[0488] In some embodiments, the technologies described herein relate to an AI-based platform that enables intelligent orchestration and management of power and energy, the AI-based platform comprising a graph neural network including a set of nodes, each representing at least one distributed energy resource (DER), and a set of edges interconnecting the sets of nodes, where each edge represents at least one energy-related feature between at least two nodes in the set of nodes.

[0489] In some embodiments, the technologies described herein relate to an AI-based platform that enables intelligent orchestration and management of power and energy. This AI-based platform includes a supply graph neural network and a set of nodes each representing at least one distributed energy resource (DER) configured to generate, store, convert, and / or transport energy, and a demand graph neural network and a set of nodes each representing at least one distributed energy resource (DER) configured to consume energy.

[0490] In some aspects, the technology described herein relates to an AI-based platform that enables intelligent orchestration and management of power and energy. This AI-based platform includes at least one digital twin representing at least one distributed energy resource (DER), and a graph neural network including a set of nodes associated with the at least one digital twin and a set of edges interconnecting the set of nodes.

[0491] In some embodiments, the technologies described herein relate to an AI-based platform that enables intelligent orchestration and management of power and energy. This AI-based platform includes a graph neural network comprising a set of nodes, each representing at least one distributed energy resource (DER), and a set of edges interconnecting the sets of nodes, and an attention model that dictates at least one attention relationship between at least two nodes in the set of nodes of the graph neural network.

[0492] In some embodiments, the technology described herein relates to an AI-based platform that enables intelligent coordination and management of power and energy. This AI-based platform includes a graph neural network including a set of nodes, each representing at least one distributed energy resource (DER), and a set of edges connecting the sets of nodes to each other, and a large-scale language model configured to generate at least one description of the sets of nodes and sets of edges contained in the graph neural network. [Brief explanation of the drawing]

[0493] This disclosure will be better understood from the detailed description and accompanying drawings.

[0494] [Figure 1] Figure 1 is a schematic diagram illustrating the platform and key elements in several embodiments.

[0495] [Figure 2A] Figures 2A and 2B are schematic diagrams illustrating the main subsystems of the main ecosystem in several embodiments. [Figure 2B] Figures 2A and 2B are schematic diagrams illustrating the main subsystems of the main ecosystem in several embodiments.

[0496] [Figure 3] Figure 3 is a schematic diagram showing more details of a distributed energy generation system according to several embodiments.

[0497] [Figure 4] Figure 4 is a schematic diagram showing the details of data resources in several embodiments.

[0498] [Figure 5] Figure 5 is a schematic diagram showing more details of the configured energy edge stakeholders in several embodiments.

[0499] [Figure 6] Figure 6 is a schematic diagram showing more details of an intelligence realization system according to several embodiments.

[0500] [Figure 7] Figure 7 is a schematic diagram illustrating AI-based energy orchestration in more detail, based on several embodiments.

[0501] [Figure 8] Figure 8 is a schematic diagram showing more details of configurable data and intelligence in several embodiments.

[0502] [Figure 9] Figure 9 is a schematic diagram illustrating the dual-process learning capabilities of a dual-process artificial neural network in several embodiments.

[0503] [Figure 10] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 11]Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 12] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 13] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 14]Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 15] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 16] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 17]Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 18] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 19] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 20]Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 21] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 22] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 23]Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 24] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 25] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 26]Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 27] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 28] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 29]Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 30] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 31] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 32]Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 33] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 34] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 35]Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 36] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure. [Figure 37] Figures 10 to 37 are schematic diagrams of embodiments of neural network systems that may be connected to, integrated with, and accessible by a platform for enabling intelligent transactions, including expert systems, self-organizing systems, machine learning systems, artificial intelligence systems, and neural network systems trained for pattern recognition, classification of one or more parameters, characteristics, or phenomena, support for autonomous control, and other purposes, according to embodiments of the present disclosure.

[0504] [Figure 38] Figure 38 is a schematic diagram of exemplary embodiments of quantum computing services according to some embodiments of the present disclosure.

[0505] [Figure 39]Figure 39 shows the quantum computing service request processing according to some embodiments of the present disclosure.

[0506] [Figure 40] Figure 40 is a perspective view showing a thalamic service in accordance with this disclosure and how it is coordinated within the module.

[0507] [Figure 41] Figure 41 is another perspective showing the thalamic service in accordance with this disclosure and how it coordinates within the module.

[0508] [Figure 42] Figure 42 is a schematic diagram showing attention determination by a machine learning model in accordance with this disclosure.

[0509] [Figure 43] Figure 43 is a schematic diagram of a transformer model in accordance with this disclosure.

[0510] [Figure 44] Figure 44 is a schematic diagram of an energy edge convergence technology stack in accordance with this disclosure.

[0511] [Figure 45] Figure 45 is a schematic diagram of a set of functions of the energy edge convergence technology stack according to this disclosure. [Modes for carrying out the invention]

[0512] Figure 1: Introduction to the Platform and Key Components In embodiments, provided herein is an AI-based energy edge platform, which for convenience may, in some cases, simply referred to herein as Platform 102, and includes a set of intelligent, and in some cases, autonomous or semi-autonomous, distributed entities (hereinafter referred to herein as “Distributed Energy Resources” or “DER”) and other energy resources and systems that generate, store, consume, and / or transport energy, and includes an intelligent, and in some cases autonomous or semi-autonomous, connected and other elements that work together to enable the orchestration and management of power and energy in various ecosystems and environments, including IoT, edge, and other devices and systems that process data in relation to DERs and other energy resources and can be used to inform, analyze, control, optimize, predict, and otherwise assist in the orchestration of distributed energy resources and other energy resources.

[0513] For example, distributed energy resources ("DERs") include (but are not limited to): wind turbines (including wind turbine farms), photovoltaics (PV), flexible and / or floating solar energy systems (including solar energy farms), fuel cells (including natural gas-fired and biomass-fired fuel cells), coal mines, oil wells, natural gas wells, modular reactors, nuclear batteries, modular hydroelectric systems, microturbines and turbine arrays, reciprocating engines, combustion turbines, cogeneration plants, and more. OMAS generators, municipal solid waste incinerators, battery energy storage (including chemical batteries, etc.), capacitive energy storage, geothermal energy systems, molten salt energy storage, electrothermal energy storage (ETES), gravity storage, compressed fluid energy storage, pumped-storage hydroelectric energy storage (PHES), liquid air energy storage (LAES), coal storage facilities, oil storage tanks, natural gas storage tanks, liquefied natural gas (LNG) storage tanks, physical energy storage systems such as flywheels, gravity batteries (e.g., fuel transport vehicles, fuel transport pipelines, wired power transmission systems, wireless power transmission systems, etc.).

[0514] 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, but are not limited to, by using or organizing a set of advanced energy resources and systems 104, including DERs, etc. The configured set of stakeholder energy edge solutions 108 can integrate domain-specific stakeholder data, such as proprietary datasets generated in relation to a company's operations, analytics, and / or strategy; real-time data from stakeholder assets (such as collected by IoT and edge devices located in close proximity to stakeholder assets and operations); and stakeholder-specific energy resources and systems 104 (such as available energy generation, storage, or distribution systems), which can be deployed at stakeholder locations to supplement or replace the electric grid, to meet the energy needs and capabilities of stakeholders, including baseline, period, and peak energy needs for carrying out operations such as large-scale data processing, transportation, production of goods and materials, resource extraction and processing, heating and cooling, and many others.

[0515] In an embodiment, the platform 102 (and / or its elements) and / or configured set of stakeholder energy edge solutions 108 can acquire, provide, and / or exchange data from a set of data resources for energy edge orchestration 110. The platform 102 acquires information from a set of data resources for energy edge orchestration 110. These data resources may include datasets ranging from real-time energy consumption metrics to predictive analytics on future energy demand. By using these resources, the platform 102 can make timely and informed decisions. The platform 102 also provides a set of data resources for energy edge orchestration 110. Such data may include feedback on energy optimization strategies, insights 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 continuously updated, promoting more accurate and dynamic energy management. Furthermore, a set of configured stakeholder energy edge solutions 108, tailored to meet the unique needs of various stakeholders, can provide data to platform 102, from which insights can be derived. For example, a stakeholder solution designed for a solar energy farm can provide real-time data on the efficiency of solar panels, which platform 102 can use to optimize energy distribution. Such data exchange between platform 102, the configured set of stakeholder energy edge solutions 108, and the set of data resources for energy edge orchestration 110 ensures that optimizations are based on the most up-to-date available data.

[0516] Platform 102 includes a set of intelligence-enabling 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, which can be integrated with, exchange data with, and / or linked to. The set of intelligence-enabling systems 112 functions as the cognitive backbone of Platform 102. Utilizing advanced algorithms and computing tools, the set of intelligence-enabling systems 112 provides Platform 102 with the intelligence necessary to analyze vast datasets, recognize patterns, and make informed decisions. The set of AI-based energy orchestration, optimization, and automation systems 114 ensures that 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 Platform 102 into a dynamic entity that responds to real-time changes and is proactive in its strategy. The set of configurable data and intelligence modules and services 118 provides platform 102 with modularity and customization flexibility. Depending on the specific use case, stakeholders can configure these modules to address their unique requirements.

[0517] The set of intelligence-enabling systems 112 may include a set of intelligent data layers 130 for managing and processing information, a set of distributed ledger and smart contract systems 132 for ensuring secure and transparent transactions and data management, a set of adaptive energy digital twin systems 134 for creating virtual replicas of physical energy assets for better monitoring and optimization, and / or a set of energy simulation systems 136 for modeling potential energy scenarios to support decision-making. These integrated systems function collectively within the intelligence-enabling system 112 to provide a comprehensive solution for advanced energy management.

[0518] The AI-based energy orchestration, optimization, and automation system 114 set may include a set of energy generation orchestration systems 138 for managing and coordinating energy sources, a set of energy consumption orchestration systems 140 for overseeing and optimizing energy usage, a set of energy market orchestration systems 146 for facilitating energy trading and transactions, a set of energy distribution orchestration systems 147 for ensuring efficient and reliable energy distribution, and a set of energy storage orchestration systems 142 for managing energy storage. Combining these systems provides a comprehensive approach to orchestrating the entire energy lifecycle.

[0519] The set of configurable data and intelligence modules and services 118 may include a set of data integration microservices 150 that enable or contribute to the realization of 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 virtual representations of stakeholder-specific energy assets for better monitoring and management, and a set of configured stakeholder energy edge solutions 108 that ensure an integrated approach to energy management.

[0520] Platform 102 includes, integrates with, links with, exchanges data with, is controlled by, takes input from, and / or provides 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 in this disclosure and in documents incorporated herein by reference. Unless the context specifically indicates otherwise, references to AI, or to one or more examples of AI, should be understood to encompass these various alternative methods and systems. For example, but not limited to, AI systems described to enable any of the wide variety of functions, capabilities, and solutions described herein (such as optimization, autonomous operation, prediction, control, and orchestration) should be understood to be implementable by operations on models or rule sets, by training on training datasets such as human tags, labels, etc., and by training on training datasets of human interactions (e.g., such as the following). g., training on training datasets of human interactions (e.g., human interactions with software interfaces or hardware systems), training on resulting training datasets, training on AI-generated training datasets (e.g., a complete training dataset is generated by the 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 such that the neural network type most advantageous for performing each element of a multifunction or multicapable system or method is implemented within the set.As one of many examples, 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 operation as long as the results are favorably perceived by the user. More transparent models or systems and simpler neural networks can be used in automated governance systems where a deeper understanding of how inputs are translated into outputs may be necessary to comply with regulations or policies. AI-based energy orchestration, optimization, and automation systems

[0521] In embodiments, platform 102 may employ demand forecasting, including automated forecasting by artificial intelligence or automated forecasting by ingesting a data stream of forecast information from a third party. In particular, demand forecasting helps inform site selection and intelligently planned network expansion. In embodiments, machine learning algorithms can generate multiple forecasts regarding weather, price, solar power, energy demand, and other factors, and analyze how energy assets can best capture or generate value at different times and / or locations.

[0522] In embodiments, the AI-based energy orchestration, optimization, and automation system 114 can enable the optimization of energy patterns by analyzing the energy use of a building or other operational energy use and seeking to reshape the patterns for optimization (for example, by modeling demand responses 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 assess how a building's energy consumption changes at different times of day or in different seasons. Using this knowledge, the automation system 114 can reconstruct 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 the simultaneous use of lighting, heating, and cooling systems. By modeling how a building responds to specific 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 propose measures to distribute energy consumption more evenly throughout the day, thereby reducing peak demand and associated costs.

[0523] An AI-based energy orchestration, optimization, and automation system 114 may be realized by a set of intelligence-enhancing systems 112 that provide functions and capabilities to support a variety of applications and use cases.

[0524] In an embodiment, platform 102 may be configured to integrate data from at least one internal edge device located within the environment (e.g., a sensor in a building, vehicle, machine, or utility) and at least one external edge device located outside the environment (e.g., a sensor in a weather monitoring station broadcasting real-time data, a vehicle, etc.). Platform 102 can collect real-time energy intelligence data and provide it to an intelligence circuit that learns from the data and results and automatically takes actions to optimize energy management. For example, an edge device connected to a DER may be taken in combination with an edge device from a local weather monitoring station. Local weather data (e.g., cloud cover, temperature, wind, precipitation, etc.) may correlate with the energy output from the DER, and a machine learning model may be trained to utilize variables from the second edge device to predict environment-related actions for the first edge device. As a further example, radar signals output by a weather station edge device can be used to act on ramping up or down energy from the DER.

[0525] In embodiments, data output from one or more edge devices may be vectorized and / or stored in a distributed database. The acquisition of energy data from devices may be further optimized through the use of vector-based updates of data, where only changes affecting a model of consumption information are transmitted. Vectors can be developed based on an analysis of data from the aforementioned consuming devices. For complex energy consumption systems, vectors may be multidimensional vectors representing consumption type, purpose, device, etc., to form a highly efficient way of communicating a complex energy usage environment. As an example, consider a smart grid system where thousands of appliances, HVAC systems, and lighting solutions continuously transmit energy consumption data. The system analyzes this data and creates a vector based on consumption patterns. This vector includes various parameters, particularly in the case of complex energy consumption systems, such as consumption type, consumption purpose, and specific devices consuming energy.

[0526] In embodiments, energy usage patterns may include local patterns, such as those based on a consumer's daily work schedule. However, energy usage patterns may also be based on broader data, such as weather forecast data, energy consumption in areas currently affected by weather forecasts, and the preparation of areas expected to receive weather forecasts. Pattern analysis may include not only raw usage but also information about consumers (e.g., where energy-consuming appliances are being operated) that may influence learning. For example, a consumer's daily work schedule may include turning off all household appliances during working hours and increasing energy consumption in the evening, and this may be a local pattern that can be recognized and adapted by the system.

[0527] Demographics and other human-based activities can play a role in energy pattern analysis. For example, demographics in a region suggesting that consumers replace older vehicles with newer ones more frequently than in other regions may indicate that the region's energy demand for electric vehicle charging may increase earlier in such a region. If demographics and / or consumer behavior suggest that consumers in a region tend to trade in their vehicles for used ones, it may indicate that maintaining legacy energy sources is preferable in such a region. Subsystems and modules of the intelligence realization system Intelligent Data Layer

[0528] The set of intelligence realization systems 112 may include a set of intelligent data layers 130, for example, a set of services (including microservices), APIs, interfaces, modules, applications, programs, etc., that can consume any of the data entities and types described throughout this disclosure and undertake a wide range of processing functions such as extraction, cleansing, normalization, computation, transformation, loading, batch processing, streaming, filtering, routing, parsing, transformation, pattern recognition, content recognition, and object recognition. Through a set of interfaces, users of platform 102 can configure a set of intelligent data layers 130 or its outputs to meet the needs of the internal platform and / or enable further configurations such as a set of configured stakeholder energy edge solutions 108. The set of intelligent data layers 130, more generally the set of intelligence enablement systems 112, and / or configurable data and intelligence modules and services 118 may access data from various sources throughout the platform 102, and in embodiments may be contained in a set of centralized and / or distributed databases, or may operate from a set of shared data resources consisting of a set of distributed or distributed data sources such as IoT or edge devices that generate energy-related event logs or streams.The set of intelligent data layers 130 can be configured for a wide range of energy-related tasks, including prediction / forecasting of energy consumption, generation, storage, or distribution parameters (e.g., at the individual device, subdevice, system, machine, or fleet level), optimization of energy generation, storage, distribution, or consumption (also at various levels of optimization), automated discovery, configuration, and / or execution of energy trading (including micro-trades and / or larger trades in spot and futures markets, as well as trades in peer-to-peer groups or single counterparty trades), monitoring and tracking of energy consumption, generation, distribution, and / or storage parameters and attributes (e.g., baseline levels, volatility, periodic patterns, episodic events, peak levels, etc.), monitoring and tracking of energy-related parameters and attributes (pollution, carbon production, renewable energy credits, waste heat production, etc.), automated generation of energy-related alerts, recommendations, and other content (such as messaging to encourage or promote the user's preferred actions), and much more.

[0529] In an embodiment, platform 102 may be configured to analyze a monitored energy dataset and generate configuration recommendations for distributed systems to generate and consume energy. Platform 102 may also be configured to analyze streams from one or more local power-consuming entities and generate recommendations. For example, a manufacturing plant may have a set of needs that are quite different from those of a hospital campus. Thus, the AI-based platform can perform an analysis of each of multiple energy consumption scenarios, as well as the associated devices and demands, and recommend the type of DER for supplying and regulating energy that addresses the needs and demands of the local power-consuming entities. A hospital may have ERs with a specific set of demands, such as operating room hours or incidental demand based on emergencies. Examples of monitored energy datasets include one or more grid-based energy resources and mobile energy resources. Grid-based energy resources may include, for example, fossil fuel-based energy production facilities (such as coal, oil, and natural gas) and 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 equipment, mobile fossil fuel-based generators, mobile renewable energy producers, mobile transformers and power regulation systems, drone-based power supply / storage systems, and vehicle-based power supply / storage systems. Distributed Ledger and Smart Contract System

[0530] A set of intelligence realization systems 112 may include a smart contract system 132 for processing a set of smart contracts, each of which may optionally operate on a set of blockchain-based distributed ledgers. Each smart contract may operate on data stored on a set of distributed ledgers or blockchain to record energy-related transaction events such as the purchase and sale of energy (in spot, forward, and peer-to-peer markets, as well as direct counterparty transactions), associated service charges, transaction-related energy events such as consumption, generation, distribution, and / or storage events, and other transaction-related events often related to energy, such as carbon production or reduction events, renewable energy credit events, and pollution production or reduction events. A set of smart contracts handled by the smart contract system 132 can consume any of the data types and entities described throughout this disclosure as a set of inputs, undertake a set of calculations (optionally consisting of flows that take inputs from heterogeneous systems in a multi-step transaction), and provide a set of outputs that enable the completion of the transaction, reporting (optionally recorded in a set of distributed ledgers), etc. A set of energy transaction realization systems 144 may be realized or enhanced by artificial intelligence, including autonomously discovering, structuring, and executing transactions according to a strategy, and / or providing automated or semi-automated transactions based on training and / or supervision by a set of transaction experts.

[0531] In embodiments, the smart contract system 132 can be used by a set of energy trading realization systems 144 (described elsewhere in this disclosure) to constitute a trading solution. Each smart contract within the smart contract system 132 is intricately designed to process data stored in these distributed ledgers or blockchains. The functionality of the smart contracts extends to documenting various energy-related trading events. This includes, but is not limited to, recording peer-to-peer energy transactions and direct transactions between parties. Furthermore, the smart contracts acquire data related to energy events associated with service charges and other transactions, including information on energy consumption, generation, distribution, and storage. For example, in an urban energy grid integrating renewable energy such as solar and wind, the smart contract system 132 can autonomously execute contracts to purchase solar energy during peak solar hours and wind energy during windy hours. Simultaneously, it records each transaction, the associated service charges, and even the carbon offset achieved by the use of renewable energy sources. Adaptive Energy Digital Twin System

[0532] Any entities, analytical results, artificial intelligence outputs, states, operating states, or other features pointed out throughout this disclosure may, in embodiments, be presented in digital twins 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 suit the needs of a particular stakeholder or stakeholder solution. The 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 plants, a fleet of vehicles, a subset of the same (e.g., to compare energy parameters by each of similar sets of machines to identify out-of-range operations), and many other aspects. The digital twins may be adaptive, such as filtering, highlighting, or otherwise adjusting the presented data based on real-time circumstances, such as changes in energy costs or changes in driving behavior.

[0533] In embodiments, platform 102 may be configured to create, manage, and / or otherwise provide dynamic digital twins of historical, current, and predicted distributed energy demand for both mobile and stationary entities within an underlying 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 can be modeled via digital twins. In a specific example, for a manufacturing facility with numerous machines, assembly lines, and automation systems, platform 102 can create a digital twin of this environment and capture all the details of energy consumption patterns. Such a digital twin can provide real-time information on the facility's energy demand, from historical energy usage data for each machine to current consumption rates, and even forecasts of future energy demand based on predicted production schedules. In large-scale environments, models can be conceivable that allow for significant cost shifts based on energy adjustments across the entire environment. For example, in large-scale environments with high energy consumption, even small adjustments can have a significant financial impact. Having a dynamic digital twin allows stakeholders to simulate various energy adjustments and analyze their impacts. For example, in office buildings, energy costs can be significantly reduced by adjusting the operation of HVAC systems based on real-time occupancy data or optimizing lighting based on the availability of natural light.

[0534] In some embodiments, Platform 102 can be configured to model government entities through one or more digital twins, such as states, counties, cities, towns, development areas, and communities. For example, in the case of a city with thousands to hundreds of thousands of residents, businesses, public transport systems, and numerous amenities, Platform 102 can create a digital twin of such a city and capture all aspects of its energy consumption. This digital display could include everything from lighting in public parks and air conditioning systems in government buildings to the energy demands of public transport. In doing so, Platform 102 provides city administrators with a holistic view of the city's energy footprint and facilitates informed decision-making regarding energy management. Platform 102 can also model large entities such as states and counties, capturing the diverse energy demands of various regions, from urban centers to rural areas. Platform 102 can also represent smaller entities such as towns. For example, in a new town being developed for industrial purposes, Platform 102 can model the expected energy demands based on the planned industries and ensure that the energy infrastructure is adequately prepared to meet those demands. In another example, a prefecture planning a transition to renewable energy can use a digital twin to simulate the impact of integrating solar power plants and wind turbines. This simulation can provide insights into potential energy savings, grid stability, and the environmental benefits of such a transition.

[0535] In an embodiment, platform 102 may include an AI-based system for updating a digital twin based on a set of energy parameters, which may include adapting energy consumption data from the physical device of the digital twin based on the set of energy parameters, for example, adjusting the cost incurred on the consumed energy based on the dynamic energy market from which the device supplies energy. As an example, consider a device that sources 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, so that the financial impact of energy consumption in the virtual and real worlds can be accurately reflected. When updating the device, the AI-based system may also incorporate the energy sourcing preferences of the device's user(s) (optionally represented in the device's digital twin). 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 the update of the energy consumption data. In the case of a shared device (e.g., an e-bike), the energy consumed during user sharing of the device (while the e-bike is checked out in the user's account), and / or energy associated with user sharing of the device, may be allocated to specific energy sources based on the user profile. For example, if a user checks out an e-bike in their user account, the energy consumed during use may be supplied from their preferred energy source, as detailed in the user profile associated with the user account. Furthermore, or alternatively, the owner of the device and / or digital twin may specify the energy consumption allocation to each of multiple energy sources. As an example, there may be a scenario where the device owner has a specific energy consumption allocation that spans multiple energy sources. In such cases, the AI ​​system ensures that the digital twin accurately reflects this allocation.For example, the owner can specify that 50% of the energy consumed by the device be supplied by wind energy and the remaining 50% by hydroelectric energy. The AI ​​system can ensure that this allocation is accurately reflected when updating the digital twin. In this way, platform 102 with an AI-based system provides a digital twin that is not merely a static representation, but is dynamic, responsive, and tailored to individual preferences and real-world scenarios.

[0536] In an embodiment, an AI-based system for updating a digital twin based on a set of energy parameters may include adapting energy production and / or allocation control for future periods (e.g., during upcoming high-demand events) based on the set of energy parameters. This may include relying on AI-based forecasts of energy demand for future periods to adjust how the energy supply system operates, such as energy parameters that determine how much energy to store and how much to produce and supply. For example, in a scenario where high demand is predicted, possibly due to a festival, the AI-based system could predict this surge in demand by analyzing energy parameters and adapt energy production and / or allocation control accordingly. In another example, based on historical data and current trends, the AI-based system could predict an increase in energy demand during the summer. In addition to AI-based energy demand forecasting, the AI-based system could assess macro trends / activities based on energy parameters. For example, an AI-based system updating an energy consumption system might detect pricing patterns that suggest a potential sharp rise in energy costs (e.g., due to a major weather event), and a set of energy parameters could guide the AI-based system to adapt energy consumption and / or storage guidance for at least selected consumers (e.g., public systems (e.g., tax-based systems)) to avoid unnecessary burdens on taxpayers. For example, if the AI-based system detects a pattern that suggests a potential rise in energy costs due to an upcoming major weather event, proactive measures can be taken. By analyzing a set of energy parameters, the AI-based system could guide specific consumers to adapt their energy consumption or storage patterns, or guide public systems to reduce consumption or increase storage. In this way, platform 102 with an AI-based system ensures that energy management is proactive and efficient.

[0537] In an embodiment, platform 102 may be configured to provide and / or facilitate a digital twin of a common device type (e.g., the same model of e-bike). The digital twin can exchange consumption data across a range of usage instances to gain a better understanding of how this common device type consumes energy in different environments, different time zones, different geographical locations, and user demographics (including demographics local to the usage point). For example, an e-bike used primarily in hilly areas may exhibit a different energy consumption pattern 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 a digital twin of a particular device (a particular e-bike) to better predict energy demand, particularly dynamic charging profiles. One device may be located in a high-demand area, suggesting the need for more frequent charging, while another device may be allowed to maintain a lower average energy charge due to shorter usage times and lower usage frequency, for example. For instance, an e-bike installed in a busy urban area may be perceived as requiring frequent charging due to high demand. On the other hand, another e-bike might be installed in an area with low usage and could operate optimally without frequent charging. It's also possible to aggregate demand profiles from various geographical areas to identify demands such as charging needs and available energy. For example, in areas with a high concentration of e-bikes, platform 102 could suggest staggering charging schedules to balance demand and prevent grid overload. This could lead to managing e-bike charging activities, including balancing demand for other rechargeable devices within the area.

[0538] In embodiments, platform 102 can be configured so that not every physical instance of a device (e.g., a particular model of e-bike) needs to have its own permanent digital twin. Since most of these types of devices are dormant for considerably longer periods than they are in use (their duty cycles are very infrequent), even the energy demand for processing to support the digital twins of these types of devices can be managed based on the demand profile. Instances of physical devices (or instances of configured genes) can be activated (energy resources can be allocated) based on demand forecasts. Consider the scenario of a particular model of e-bike. These e-bikes are scattered in various locations and may always be available, but their actual usage frequency or "duty cycle" is infrequent, and the devices may be dormant for long periods. Understanding this unique characteristic, platform 102 is configured to activate the digital twins of these devices based on predicted demand, instead of continuously maintaining a digital twin of each e-bike. For example, in an urban environment, if platform 102 predicts a surge in e-bike demand, for example, during the morning rush hour, it can activate the digital twin of the e-bike during that time. These digital twins facilitate energy management and ensure that e-bikes are charged and ready for use. After rush hour, these digital twins can be deactivated to conserve processing energy. This demand-driven approach ensures optimal utilization of energy resources for processing the digital twins.

[0539] In embodiments, platform 102 can provide and / or facilitate the sharing, exchange, and / or aggregation of energy consumption data provided to a digital twin by physical device instances that can be collected 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 several smart homes, each equipped with multiple smart devices. While each home may have its own unique energy consumption patterns, the aggregated 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 vacation periods. Such insights can be reflected in predictive models to ensure that energy providers are well-prepared to meet the predicted demand.

[0540] In an embodiment, platform 102 may be configured so that energy consumption data provided to the digital twin also facilitates the forecasting of energy-related demands, such as the maintenance of energy supply infrastructure. For example, the need to address waste from energy production can be better predicted not only by consumption but also by the digital twin based on available supply sources. In other words, physical devices not only consume energy but also have to be supplied with energy (or generate energy themselves). Energy supply and / or procurement can be used by the digital twin to indicate the time / region / specific source of energy production for support (waste removal, repair, etc.). For example, if a regional energy production facility relies primarily on non-renewable energy sources, the amount of associated waste will be high. By predicting this, the digital twin can ensure that appropriate waste management measures are implemented. Furthermore, the digital twin of a regional 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 projected demand for e-bike use for upcoming events (such as graduation ceremonies or freshman orientation) can be predicted along with the availability of, for instance, solar energy, then local energy supply bases can procure up-the-chain energy only when necessary and / or as needed. For instance, if the solar energy forecast is favorable, the depot can rely primarily on solar energy; otherwise, it can procure energy from up-the-chain energy suppliers to meet its demand. Energy simulation system

[0541] In an embodiment, the set of energy simulation systems 136 is provided for developing and evaluating detailed simulations of energy generation, demand response, and charge management, including a simulation environment that simulates the results of using various algorithms that may govern power generation across different generations of assets, consumption by energy-demanding devices and systems, and energy storage. The data can be used to simulate the interaction between uncontrollable loads and optimized charging processes, among other use cases. The simulation environment can provide outputs to, integrate with, or share data with a set of adaptive energy digital twin systems 134. For 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. This simulation can predict how solar panels will respond to weather changes, how wind turbines will operate in different seasons, or how energy storage solutions will need to be managed during peak demand periods.

[0542] In embodiments, as more enterprises adopt hybrid infrastructure, 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 for automatically managing the energy of devices and systems within such devices. For example, artificial intelligence may enable autonomous data center cooling or industrial control. 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 the infrastructure model. An introduction to the main subsystems and modules of AI-based energy orchestration, optimization, and automation systems.

[0543] The AI-based energy orchestration, optimization, and automation system set 114 may include, in particular, 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 can enable the orchestration of energy delivery to consumption points, such as fixed power lines, wireless energy transmission, fuel delivery, and delivery of stored energy (e.g., chemical or nuclear batteries), 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) and the purpose or type of consumption (e.g., whether there is a need for very high peak energy supply, such as in a power-intensive production process). Consider the case where a remote industrial unit far from the main grid needs power for its production process. A set of energy generation orchestration systems 138 can analyze the location and determine that connecting such units 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 chemical battery supply may be the most suitable in this case. Configurable data and intelligence modules and services

[0544] In an embodiment, platform 102 may include a set of configurable data and intelligence modules and services 118. These may include a set of energy tradable systems 144, a set of stakeholder energy digital twins 148, a set of data integration microservices 150, and so on. Each module or service (optionally configured in a microservice architecture) can exchange data with various data resources to provide relevant outputs, such as supporting a set of internal functions or capabilities of platform 102 and / or supporting one or more functions or capabilities of a set of configured stakeholder energy edge solutions 108. As one of many examples, a service may be configured to acquire event data from an IoT device having a camera or sensor that monitors a generator and integrate it with weather data from a public data resource 162 to provide a weather-related timeline of the generator's energy generation data, which can then be consumed by a set of configured stakeholder energy edge solutions 108 to help predict the day before energy generation by the generator based on the day before weather forecast. Such a wide range of configured data and intelligence modules and services 118 may be enabled by platform 102 to represent, for example, various outputs consisting of the fusion or combination of a 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 data patterns, automation and control outputs, and many other outputs.

[0545] In embodiments, platform 102 may be configured so that energy consuming devices and / or systems (e.g., a set of energy consuming devices in a home) can locally coordinate access to energy sources such as mainline energy, first-level stored energy (e.g., in a device), and local stored energy (e.g., a local battery capable of supplying energy to multiple devices). Furthermore, devices can consume energy for a variety of purposes, including consumption, storage, balancing supply, and acting as a substitute for other devices. In addition, energy consuming devices may be configured / configurable to use multiple energy types, such as the power grid, solar energy, geothermal energy, fossil fuels (combustion engines), and hydrogen. Also, within an energy consuming system (a collection of devices as described above), energy consumption may span various energy sources (e.g., hydrogen for cooking, solar energy for energy storage, waste 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 where these devices can locally coordinate access to various energy sources based on urgent needs and available resources. For example, on a sunny day, a house's solar panels may be generating excess energy, in which case platform 102 can primarily utilize the energy from the solar panels and reduce energy consumption from the grid.

[0546] In an embodiment, platform 102 can capture energy consumption information from / through edge devices and develop a dataset representing multiple perspectives on consumed energy. Edge devices that can 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 device may consume, what sources the device has consumed, and the purpose / use of the consumed energy. Further examples may include whether the device appears to be performing some optimization, such as utilizing local storage during periods of high energy costs (including high transmission costs that may be measured based on delivery efficiency, etc.), consuming energy to replenish storage during off-peak hours, and / or utilizing low-cost sources (e.g., solar power) when readily available. Extensive analysis 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 and reveals details such as a preference for utilizing local storage during high-energy-cost times. By aggregating this data from various edge devices, platform 102 can identify patterns, predict future energy demand, and optimize energy consumption across devices. Energy trading support system

[0547] The configurable data and intelligence modules and services 118 may include a set of energy tradability systems 144. A set of energy tradability systems 144 may include a smart contract that can operate on a set of data stored on a distributed ledger or blockchain, which can operate on a set of data stored on a distributed ledger or blockchain, recording energy-related trading events such as consumption, generation, distribution and / or storage events, as well as other energy-related trading events such as carbon production or reduction events, renewable energy credit events, and pollution production or reduction events. A set of smart contracts can consume any of the data types and entities described through this disclosure as a set of inputs, perform a set of calculations (optionally consisting of flows that take inputs from heterogeneous systems in a multi-step transaction), and provide a set of outputs that enable the completion of the transaction, reporting (optionally recorded in a set of distributed ledgers), etc. A set of energy transaction realization systems 144 can be realized or enhanced by artificial intelligence, including autonomously discovering, structuring, and executing transactions according to a strategy, and / or providing automated or semi-automated transactions based on training and / or supervision by a set of transaction experts. Autonomy and / or automation (supervised or semi-supervised) can be enabled by robotic process automation, such as by training a set of intelligent agents on the interaction of transaction discovery, structuring, or execution between a group of transaction experts and a transaction realization system (such as a software system used to construct and execute energy transaction activities).

[0548] As energy production and consumption shift towards local, decentralized markets, energy markets are likely to follow patterns of other peer-to-peer and shared economy markets, such as ride-sharing, apartment sharing, and second-hand goods markets. Technology will enable the avoidance of top-down, centralized energy supply, and operators can build platforms that allow them to manage and monetize surplus capacity through leasing and trading of assets and outputs.

[0549] As more decentralized or peer-to-peer tradable energy markets develop, platform 102 may include, link, integrate, or enable systems to other platforms that facilitate P2P trading, wholesale contracts, renewable energy certificate (REC) tracking, and broader decentralized energy supply, payment management, and other trading elements. In embodiments, the above may utilize a blockchain, a distributed ledger, and / or a smart contract system 132. For example, a homeowner with surplus solar energy may decide to sell this surplus energy. This transaction is securely recorded on the blockchain.

[0550] Specifically, increased transparency, choice, and flexibility will enable consumers to actively participate in the energy market not only by consuming electricity, but also by generating, storing, and selling it. For example, suppose a community decides to utilize solar power. Platform 102 would allow households with solar panels to trade surplus energy with those without, so that the entire community can benefit.

[0551] In an embodiment, transaction elements may be configured by a set of energy transaction activation systems 144 to optimize energy generation, storage, or consumption, such as utility usage-based pricing. An IoT-based platform that can identify the time of day when energy costs are lowest can shift energy demand away from high-priced times. For example, in areas where utility costs vary by usage time, platform 102 can shift energy demand to times when energy is cheaper. For example, smart home devices linked to platform 102 can identify the time of day when energy costs are lowest and adjust their operation accordingly, ensuring efficient and cost-effective energy consumption. Stakeholder Energy Digital Twin

[0552] The configurable data and intelligence module and service 118 may, in embodiments, include a set of digital twins 148 configured to represent a set of energy-related stakeholder entities, including energy generating resources, energy distribution resources, and / or energy distribution resources (including representing them by type, such as indicating renewable energy systems, carbon generating systems, and others) owned and operated by the stakeholder, the stakeholder energy digital twin 148, and other stakeholders information technology and network infrastructure entities (e.g., edge and IoT devices and systems, networking systems, data centers, cloud data systems, on-premises information technology systems, etc.), energy-intensive stakeholder production facilities such as machinery and systems used in manufacturing, stakeholder transportation systems, market conditions (e.g., relating to energy, stakeholder supply chains, stakeholder products and services, etc., current and future market prices, etc.). The stakeholder energy digital twin 148 set can provide real-time information on status, operating conditions, etc., particularly related to energy consumption, generation, storage, and / or distribution, including sensor data, event logs, and other information streams provided from IoT and edge devices.

[0553] The Stakeholder Energy Digital Twin 148 set can provide a visual, real-time view of the energy impact on all aspects of a company. The digital twins may also be role-based, providing visual analytical metrics tailored to the user's role, such as financial reporting information for a Chief Financial Officer (CFO), operational parameter information for a power plant manager, or energy market information for an energy trader. For example, a CFO might need a visual representation highlighting the financial costs of energy consumption, such as how shifting operations to off-peak hours impacts energy costs. In contrast, a power plant manager might be interested in operational parameters such as the efficiency of energy-generating resources. Meanwhile, an energy trader might seek insights into energy markets, such as price tracking. By providing insights tailored to individual roles, the Stakeholder Energy Digital Twin 148 set ensures that various stakeholders have access to the relevant information they need to make informed decisions. Data Integration Microservices

[0554] The configurable data and intelligence modules and services 118 may include a set of data integration microservices 150, organized in a service-oriented architecture so that various microservices can be grouped in series, parallel, or in more complex flows to create higher-level, more complex services, each providing a defined set of outputs by processing a defined set of outputs, such as enabling a set of configured stakeholder energy edge solutions 108 or facilitating AI-based orchestration, optimization, and / or automation systems 114. The configurable data and intelligence modules and services 118 may consist of various functions and capabilities of a set of intelligent data layers 130, which operate on various data resources for energy edge orchestration 110, and / or internal event logs, outputs, data streams of platform 102, etc. Figures 2A and 2B: Introduction to the main subsystems of the ecosystem's major components. Data resources for energy edge orchestration

[0555] Referring to Figure 2A, the data resources for the 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 data processing, filtering, compression, storage, routing, transport, error correction, security, extraction, transformation, loading, normalization, cleansing, and / or other data processing capabilities involved in the transport of data over a network or communication system. This includes factors based on data content (e.g., packet inspection or other mechanisms for understanding the same content), network conditions (e.g., congestion, latency, packet loss, error rate, transport cost, quality of service (QoS), etc.), usage context (e.g., based on user, system, use case, application, etc., including similar prioritization), market factors (e.g., price or cost factors), user configuration, or other factors, as well as various combinations of the same. For example, among many other factors, the lowest-cost route might 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 might be selected for data supporting high-priority uses or energy, such as supporting critical medical infrastructure.

[0556] Referring to Figure 2B, the platform 102 and orchestration can integrate, link, combine, use, create, or otherwise process advanced energy resources and systems 104, a set of configured stakeholder energy edge solutions 108, and / or a wide range of data resources for 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 Figure 1. The data resources may include separate databases, distributed databases, and / or collaborative data resources, among many others. Edge and IoT Networking Systems

[0557] A wide range of energy-related data is collected and processed by a set of edge and IoT networking systems 160 (including by artificial intelligence services and other functions), and control commands may be processed when the aforementioned are located within or around energy-related entities, such as those used by consumers or businesses, including those integrated into devices, components, or systems, those located in IoT devices and systems, and those located in edge devices and systems, etc. These include any of the wide range of software, data, and networking systems described herein. Public data resources

[0558] In one embodiment, platform 102 can track public data resources 162, such as weather data. Weather conditions can affect energy use, particularly in relation to HVAC systems. By collecting, compiling, and analyzing weather data in conjunction with other building information, building managers can become more proactive about HVAC energy consumption. Public data resources 162 can include satellite data, demographic and psychometric data, population data, census data, market data, website data, e-commerce data, and many other types. Enterprise Data Resources

[0559] The set of 168 enterprise data resources 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

[0560] In embodiments, the advanced energy resources and system 104 may include distributed energy resources (DERs) 128. More distributed energy resources mean that more individuals, networked groups, and energy communities can generate and share their own energy and coordinate the system to achieve ultimate efficiency. The DERs 128 may be small or medium-sized units of generation and / or storage, which may be connected to a larger power grid at the local, distribution level. For example, the DERs 128 may be connected to a local power grid or isolated from the grid for standalone use. Transforming Energy Infrastructure

[0561] The advanced energy resources and systems 104 orchestrated by platform 102 may include a set of transformed energy infrastructure systems 120. The energy edge is involved in increasing the digitalization of power generation, transmission, substations, and distribution assets, which in turn shape the operation, maintenance, and expansion of legacy grid infrastructure. In embodiments, the set of transformed energy infrastructure systems 120 may be integrated with or linked to platform 102. The transition to the improved infrastructure may include a transition from SCADA systems and other existing control, automation, and monitoring systems to an advanced IoT platform.

[0562] In some embodiments, new assets (e.g., DERs128) added to or tuned to the grid may be compatible with existing infrastructure to maintain voltage, frequency, and phase synchronization. For example, consider a city integrating renewable energy sources such as wind turbines and solar panels (DERs128) into its existing power grid. These new assets need to be integrated with the older infrastructure to ensure a stable power supply. This compatibility allows residents to maintain a stable power supply without experiencing fluctuations in voltage, frequency, or phase synchronization, even as the city transitions to more environmentally friendly energy sources.

[0563] Improvements to legacy grid assets, new grid connection equipment, and support systems can specifically include complying with regulatory standards of NERC, FERC, NIST, and other relevant authorities; positively impacting grid reliability; making the grid less susceptible to cyberattacks and other security threats; enhancing the grid's ability to adapt to widespread bidirectional energy flows (such as the proliferation of DERs); and providing interoperability with technologies that improve grid efficiency (such as providing and facilitating demand response and mitigating grid congestion).

[0564] The digitalization of legacy grid assets may involve assets used for power generation, transmission, storage, and distribution, such as power plants, substations, and transmission lines.

[0565] In embodiments, to maintain and improve existing energy infrastructure, platform 102 may include a variety of functions, including fully integrated predictive maintenance across all of the utility's 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 where a utility has a network of generation and distribution assets, some of which are decades old. To ensure the lifespan and efficiency of these assets, platform 102 can provide predictive maintenance and alert the utility company to potential problems before they become critical.

[0566] In one embodiment, power grid maintenance can be provided. Proactive maintenance allows utility companies to accurately detect defects, reduce unplanned outages, and better serve customers. AI systems deployed with IoT and / or edge computing can help monitor energy assets and reduce maintenance costs. For example, if signs of wear or damage are detected in transmission lines, platform 102 can alert the utility company for timely repairs. This proactive approach not only reduces unplanned outages but also lowers maintenance costs, resulting in a more efficient and cost-effective power grid. Digitized resources

[0567] In embodiments, platform 102 can leverage the digital transformation of a wide range of digitized resources. Machines are becoming smarter, and software intelligence is being integrated into every aspect of business, helping to drive new levels of operational efficiency and innovation. Digital transformation is also ongoing, with a growing presence of smart devices and systems capable of data processing and communication, near-ubiquitous sensors in edge, IoT, and other devices, and the generation of massive, high-density data streams. All of this provides opportunities for increased intelligence, automation, optimization, and agility as information continuously flows between the physical and digital worlds. Such devices and systems require a great deal of energy. For example, data centers consume a lot of energy, and edge devices and IoT devices may be deployed in off-grid environments that require alternative forms of energy generation, storage, or transportation. In embodiments, a set of digitized resources may be integrated, accessed, or used for energy optimization for computing, storage, and other resources in data centers and at the edge, and elsewhere in particular. In some embodiments, as more devices incorporate sensors and control devices, machines will begin to "communicate" with each other, and information will continuously flow between the physical and digital worlds. Products can be tracked from supplier to customer, and even during use, enabling rapid responses to changes both inside and outside the company. Those in positions to manage and regulate such systems can obtain detailed data from these devices to optimize the operation of the entire process. This trend transforms big data into smart data, enabling significant cost and process efficiencies.

[0568] In this embodiment, advances in digital technology have enabled levels of monitoring and operational performance that were previously impossible. Thanks to sensors and other smart assets, service providers can collect a wide range of data across multiple parameters and monitor them in real time, 24 hours a day.

[0569] In the embodiment, DERs128 are integrated into computing networks and infrastructure devices and systems, playing a role in enhancing existing power grids, reducing costs, and improving reliability. For example, by integrating DERs128 such as local solar power plants and wind turbines into urban infrastructure, platform 102 can significantly enhance existing power grids. For instance, during peak demand, instead of relying solely on conventional power plants, platform 102 enables urban energy management systems to utilize localized energy sources. Mobile energy resources

[0570] In embodiments, DERs may be integrated with mobile energy resources 124 such as electric vehicles (EVs) and their charging networks / infrastructure, thereby augmenting existing power grids 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, increasing power demand and the development of EV infrastructure will require optimization using related technologies such as edge technologies and IoT. Charging of electric vehicles may be integrated into a distributed infrastructure, potentially being added to the grid as bidirectional charging stations, or even used as a DER 128 by powering another system locally. Vehicle power electronics systems and batteries can benefit the power grid by providing system and grid services. Excess energy can be stored in the vehicle as needed and discharged when needed. This flexibility option not only avoids expensive load peaks during short-term high energy demand but also increases the proportion of renewable energy used.

[0571] In some embodiments, universal integration of electric vehicles and charging infrastructure into the power grid requires coordination with various other standardized communication protocols. Platform 102 can include, integrate, and / or link a set of communication protocols that enable the management, provisioning, governance, and control of energy edge devices and systems using such protocols. Here, Platform 102 can act as a central hub integrating various protocols, ensuring that communication between vehicles, stations, and the grid is smooth, efficient, and coordinated when EVs dock at charging stations. Energy edge solutions for configured stakeholders

[0572] The configured set of stakeholder energy edge solutions 108 may include, in particular, 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 enable benefits for specific stakeholders such as private companies, non-governmental organizations, independent service organizations, and government organizations, using a variety of advanced energy resources and systems 104, and / or a variety of configurable data and intelligence modules and services 118. All such solutions can leverage edge intelligence, such as feeding data collected from onboard or integrated sensors, IoT systems, and edge devices located in close proximity to entities that generate, store, supply, and / or use energy, into models, expert systems, analytical systems, data services, intelligent agents, robotic process automation systems, and other artificial intelligence systems to facilitate solutions for the needs of specific stakeholders. For example, in the case of a city, the set of mobility demand solutions 152 can be used to predict peak travel times and adjust public transport schedules accordingly. Similarly, for large corporate campuses, a set of enterprise optimization solutions (154) can be used to manage energy consumption and ensure that office buildings are adequately powered during working hours, while conserving energy outside of working hours. Business Optimization Solutions

[0573] In embodiments, DER128 is integrated with enterprises and shared resources to augment existing power grids, helping to reduce costs and improve reliability. The increased level of digitalization helps to integrate activities across buildings / operations, campuses, and enterprises, and facilitates new ways of optimizing energy. For example, by integrating DER128, a campus can supplement its power demand with renewable energy. Digitalization of energy management allows campuses to monitor and adjust energy consumption in real time. In embodiments, this could enable increased operating revenue for for-profit enterprises by leveraging big data and plug load analysis to efficiently manage buildings. For example, a campus can efficiently manage buildings, ensure that energy is used where it is needed, and optimize operating costs.

[0574] In some embodiments, IoT sensors and building automation control systems may be configured to help optimize floor space, identify unused equipment, automate efficient energy consumption, improve safety, and reduce the building's environmental impact. For example, in a multi-story office building equipped with IoT sensors and building automation control systems, these systems can monitor energy consumption on each floor and ensure that lighting and HVAC systems are optimized according to the number of occupants. For instance, unused conference rooms can have their lights automatically switched off and their temperatures adjusted to reduce energy waste.

[0575] In one embodiment, platform 102 can manage the total energy consumption of systems and equipment connected to an electrical network or a set of DERs 128. Some systems may be running almost constantly, while other equipment and machines may only be connected occasionally. By understanding both the total daily electricity consumption of a building and the role that individual equipment plays in the overall energy use of a particular system, platform 102 can optionally predict, provide, manage, and control total consumption by AI or algorithms. For example, platform 102 can monitor and adjust energy consumption based on the specific needs of each building through AI and algorithms, thereby optimizing energy use.

[0576] In an embodiment, platform 102 can track and utilize an understanding of occupant behavior. Occupant 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, the energy generation, storage, and / or consumption of a building 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 occupants tend to stay home until the evening during the winter, it can adjust the heating accordingly. Over time, the system learns from these patterns and energy is used more efficiently.

[0577] In embodiments, platform 102 enables or can integrate with systems or platforms for autonomous operation. For example, industrial sites such as oil drilling rigs and power plants require extensive monitoring for efficiency and safe...

Claims

1. An AI-based platform that enables intelligent coordination and management of power and energy, It includes a graph neural network, and this graph neural network is A set of nodes, each representing at least one distributed energy resource (DER), and An AI-based platform comprising a set of edges that interconnect a set of nodes, each edge representing at least one energy-related property between at least two nodes within the set of nodes.

2. At least one node in the set of nodes contains information about at least one distributed energy resource, and this information is The power generation capacity of at least one distributed energy resource, The power storage capacity of at least one distributed energy resource, The transmission capacity of at least one distributed energy resource, The power exchange capacity of at least one distributed energy resource, The power conversion capacity of at least one distributed energy resource, The power supply capacity of at least one distributed energy resource, or The power consumption capacity of at least one distributed energy resource, The AI-based platform according to claim 1, comprising at least one of the following:

3. At least one node in the set of nodes provides information about at least one energy-related pattern associated with at least one distributed energy resource, and the at least one energy-related pattern is Energy demand patterns, Energy supply patterns, Energy storage capacity patterns, Energy market price patterns, Energy availability patterns, or Energy emission patterns, The AI-based platform according to claim 1, comprising at least one of the following:

4. At least one edge in a graph neural network representing energy-related features between at least two nodes represents at least one energy-related relationship between at least two nodes, and at least one energy-related relationship is At least two electrical connection relationships between nodes, Power transmission capability relationship between at least two nodes, Power transmission relationship between at least two nodes, Power exchange relationship between at least two nodes, Power conversion relationship between at least two nodes, Power generation dependencies between at least two nodes, Power supply dependency between at least two nodes, Power supply dependency between at least two nodes, Power storage dependency between at least two nodes, or Power consumption dependencies between at least two nodes, The AI-based platform according to claim 1, comprising at least one of the following:

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

6. At least one edge in the graph neural network represents an energy-related feature between at least two nodes and indicates at least one energy-related event, and at least one energy-related event is Energy generation events associated with at least two nodes, Energy storage events related to at least two nodes, Energy transmission events related to at least two nodes, Energy supply events related to at least two nodes, Energy demand events related to at least two nodes, Energy surplus events related to at least two nodes, Energy shortage events related to at least two nodes, Energy consumption events related to at least two nodes, Energy emission events related to at least two nodes, or Energy leakage events related to at least two nodes, The AI-based platform according to claim 1, comprising at least one of the following.

7. The AI-based platform according to claim 1, further comprising at least one artificial intelligence model configured to generate a graph neural network based on data relating to at least one distributed energy resource.

8. Data related to at least one distributed energy resource is, Energy generation data related to at least one distributed energy resource, Energy storage data related to at least one distributed energy resource, Energy transmission data related to at least one distributed energy resource Energy supply data related to at least one distributed energy resource, Energy demand data related to at least one distributed energy resource, Energy surplus data related to at least one distributed energy resource, Energy shortage data related to at least one distributed energy resource, Energy consumption data related to at least one distributed energy resource, Energy emissions data related to at least one distributed energy resource, or Energy leakage data related to at least one distributed energy resource, The AI-based platform according to claim 7, comprising at least one of the following.

9. Data related to at least one distributed energy resource is, Historical data showing at least one historical characteristic related to at least one distributed energy resource, Current data showing at least one current characteristic related to at least one distributed energy resource, or Predictive data showing at least one predictive characteristic related to at least one distributed energy resource, The AI-based platform according to claim 7, comprising at least one of the following.

10. At least one of the distributed energy resources represented by at least one node of a graph neural network is Wind turbines, Solar power generation (PV), Flexible solar power generation system, Floating solar power generation system, Solar power plant, fuel cell, Coal mine, oil wells, Natural gas well, Modular nuclear reactor, nuclear battery, Modular hydroelectric power generation system, microturbine, Turbine array, Reciprocating engine, Combustion turbine, Cogeneration plant, Biomass power generators, General waste incinerator, Battery storage system, Capacitive energy storage systems, Geothermal energy systems, Molten salt energy storage system, Electrical thermal energy storage (ETES) system, Gravity-type energy storage system, Compressible fluid energy storage, Pumped-storage hydroelectric energy storage (PHES) system, Liquid air energy storage (LAES) system, Coal storage facilities, Petroleum storage tanks, Natural gas storage tanks, Liquefied natural gas (LNG) storage tanks, Flywheel, gravity battery, Fuel transport vehicles, Fuel transport pipeline, Wired power transmission system, or Wireless power transmission system, An AI-based platform according to claim 1, which represents at least one of the following.

11. An AI-based platform that enables intelligent orchestration and management of power and energy. A supply graph neural network is a set of nodes, each representing a distributed energy resource (DER) configured to generate, store, convert, and / or transport energy, and An AI-based platform comprising a demand graph neural network, which is a collection of nodes, each representing a distributed energy resource (DER) configured to consume energy.

12. At least one node in the group of nodes included in the supply graph neural network represents information about at least one distributed energy resource, and this information is The power generation capacity of at least one distributed energy resource, The power storage capacity of at least one distributed energy resource, The transmission capacity of at least one distributed energy resource, The power exchange capacity of at least one distributed energy resource, The power conversion capacity of at least one distributed energy resource, The power supply capacity of at least one distributed energy resource, or The power consumption capacity of at least one distributed energy resource, The AI-based platform according to claim 11, comprising at least one of the following.

13. At least one node in the group of nodes included in the supply graph neural network represents information about at least one energy-related pattern associated with at least one distributed energy resource, and the at least one energy-related pattern is Energy demand patterns, Energy supply patterns, Energy storage capacity patterns, Energy market price patterns, Energy availability patterns, or Energy emission patterns, The AI-based platform according to claim 11, comprising at least one of the following.

14. A supply graph neural network includes at least one edge representing at least one energy relation between at least two nodes of the supply graph neural network, and at least one energy relation is At least two electrical connection relationships between nodes, Power transmission capability relationship between at least two nodes, Power transfer relationship between at least two nodes, Power exchange relationship between at least two nodes, Power conversion relationship between at least two nodes, Power generation dependency between at least two nodes, Power supply dependency between at least two nodes, Power supply dependency between at least two nodes, Power storage dependency between at least two nodes, or Power consumption dependencies between at least two nodes, The AI-based platform according to claim 11, comprising at least one of the following.

15. The supply graph neural network includes at least one edge representing an energy relation between at least two nodes of the supply graph neural network, At least one edge in the supply graph neural network is a directed edge, The AI-based platform according to claim 11, wherein the direction of a directed edge represents a directional dependency between at least two nodes in the node set of the supply graph neural network.

16. The supply graph neural network includes at least one edge representing at least one energy-related event between at least two nodes of the supply graph neural network, At least one energy-related event, Energy generation events associated with at least two nodes, Energy Stock events associated with at least two nodes, Energy transmission events related to at least two nodes, Energy supply events related to at least two nodes, Energy demand events related to at least two nodes, Energy excess events related to at least two nodes, Energy shortage events related to at least two nodes, Energy consumption events related to at least two nodes, Energy emission events related to at least two nodes, or Energy leakage events related to at least two nodes, The AI-based platform according to claim 11, comprising at least one of the following.

17. The AI-based platform according to claim 11, further comprising at least one artificial intelligence model configured to generate either a supply graph neural network or a demand graph neural network based on data relating to at least one distributed energy resource.

18. Data related to at least one distributed energy resource is, Energy generation data related to at least one distributed energy resource, Energy storage data related to at least one distributed energy resource, Energy transmission data related to at least one distributed energy resource, Energy supply data related to at least one distributed energy resource, Energy demand data related to at least one distributed energy resource, Energy surplus data related to at least one distributed energy resource, Energy shortage data related to at least one distributed energy resource, Energy consumption data related to at least one distributed energy resource, Energy emissions data related to at least one distributed energy resource, or Energy leakage data related to at least one distributed energy resource, The AI-based platform according to claim 17, comprising at least one of the following.

19. Data related to at least one distributed energy resource is, Historical data showing at least one historical characteristic related to at least one distributed energy resource, Current data showing at least one current characteristic related to at least one distributed energy resource, or Predictive data showing at least one predictive characteristic related to at least one distributed energy resource, The AI-based platform according to claim 17, comprising at least one of the following.

20. A distributed energy resource represented by at least one node of a supply graph neural network is, Wind turbines, Solar power generation (PV), Flexible solar power generation system, Floating solar power generation system, Solar power plant, fuel cell, Coal mine, oil wells, Natural gas well, Modular nuclear reactor, nuclear battery, Modular hydroelectric power generation system, microturbine, Turbine array, Reciprocating engine, Combustion turbine, Cogeneration plant, Biomass power generators, General waste incinerator, Battery storage system, Capacitive energy storage systems, Geothermal energy systems, Molten salt energy storage system, Electrical thermal energy storage (ETES) system, Gravity-type energy storage system, Compressible fluid energy storage, Pumped-storage hydroelectric energy storage (PHES) system, Liquid air energy storage (LAES) system, Coal storage facilities, Petroleum storage tanks, Natural gas storage tanks, Liquefied natural gas (LNG) storage tanks, Flywheel, gravity battery, Fuel transport vehicles, Fuel transport pipeline, Wired power transmission system, or Wireless power transmission system, The AI-based platform according to claim 11, representing at least one of the following.

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

22. The AI-based platform according to claim 11, further comprising an AI-based energy adjustment model configured to adjust and manage power and energy by meshing supply graph neural networks and demand graph neural networks.

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

24. The AI-based energy adjustment model meshes supply graph neural networks and demand graph neural networks based on an energy-related event graph neural network. The aforementioned energy-related event graph neural network is, A set of nodes each representing at least one energy-related event, involving at least one node of the supply graph neural network and / or at least one node of the demand graph neural network, and A set of edges associated with at least two nodes of an energy-related event graph neural network, The AI-based platform according to claim 22, including the following:

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

26. An AI-based platform that enables intelligent orchestration and management of power and energy. At least one digital twin representing at least one distributed energy resource (DER), and It includes a graph neural network, and the graph neural network is A collection of nodes associated with at least one digital twin, and An AI-based platform that includes a set of edges that interconnect a set of nodes.

27. At least one node in the set of nodes contains information about at least one distributed energy resource, and this information is The power generation capacity of at least one distributed energy resource, The power storage capacity of at least one distributed energy resource, The transmission capacity of at least one distributed energy resource, The power exchange capacity of at least one distributed energy resource, The power conversion capacity of at least one distributed energy resource, The power supply capacity of at least one distributed energy resource, or The power consumption capacity of at least one distributed energy resource, The AI-based platform according to claim 26, comprising at least one of the following.

28. At least one node in the set of nodes represents information about at least one energy-related pattern associated with at least one distributed energy resource, and the at least one energy-related pattern is Energy demand patterns, Energy supply patterns, Energy storage capacity patterns, Energy market price patterns, Energy availability patterns, or Energy emission patterns, The AI-based platform according to claim 26, comprising at least one of the following.

29. At least one edge in a graph neural network representing energy-related features between at least two nodes indicates at least one energy-related relationship between at least two nodes, and at least one energy-related relationship is At least two electrical connection relationships between nodes, Power transmission capability relationship between at least two nodes, Power transfer relationship between at least two nodes, Power exchange relationship between at least two nodes, Power conversion relationship between at least two nodes, Power generation dependency between at least two nodes, Power supply dependency between at least two nodes, Power supply dependency between at least two nodes, Power storage dependency between at least two nodes, or Power consumption dependencies between at least two nodes, The AI-based platform according to claim 26, comprising at least one of the following.

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

31. At least one edge in a graph neural network representing energy-related features between at least two nodes indicates at least one energy-related event, and at least one energy-related event is, Energy generation events associated with at least two nodes, Energy storage events related to at least two nodes, Energy transmission events related to at least two nodes, Energy supply events related to at least two nodes, Energy demand events related to at least two nodes, Energy surplus events related to at least two nodes, Energy shortage events related to at least two nodes, Energy consumption events related to at least two nodes, Energy emission events related to at least two nodes, or Energy leakage events related to at least two nodes, The AI-based platform according to claim 26, comprising at least one of the following.

32. The AI-based platform according to claim 26, further comprising at least one artificial intelligence model configured to generate a graph neural network based on data represented by at least one digital twin.

33. The data shown by at least one digital twin is Energy generation data related to at least one distributed energy resource, Energy storage data related to at least one distributed energy resource, Energy transmission data related to at least one distributed energy resource, Energy supply data related to at least one distributed energy resource, Energy demand data related to at least one distributed energy resource, Energy surplus data related to at least one distributed energy resource, Energy shortage data related to at least one distributed energy resource, Energy consumption data related to at least one distributed energy resource, Energy emissions data related to at least one distributed energy resource, or Energy leakage data related to at least one distributed energy resource, The AI-based platform according to claim 32, comprising at least one of the following.

34. Data related to at least one distributed energy resource is, Historical data showing at least one historical feature related to at least one distributed energy resource, Current data showing at least one current characteristic related to at least one distributed energy resource, or Predictive data showing at least one predictive characteristic related to at least one distributed energy resource, The AI-based platform according to claim 32, comprising at least one of the following.

35. At least one digital twin associated with at least one node of a graph neural network is, Wind turbines, Solar power generation (PV), Flexible solar power generation system, Floating solar power generation system, Solar power plant, fuel cell, Coal mine, oil wells, Natural gas well, Modular nuclear reactor, nuclear battery, Modular hydroelectric power generation system, microturbine, Turbine array, Reciprocating engine, Combustion turbine, Cogeneration plant, Biomass power generators, General waste incinerator, Battery storage system, Capacitive energy storage systems, Geothermal energy systems, Molten salt energy storage system, Electrical thermal energy storage (ETES) system, Gravity-type storage system, Compressible fluid energy storage, Pumped-storage hydroelectric energy storage (PHES) system, Liquid air energy storage (LAES) system, Coal storage facilities, Petroleum storage tanks, Natural gas storage tanks, Liquefied natural gas (LNG) storage tanks, Flywheel, gravity battery, Fuel transport vehicles, Fuel transport pipeline, Wired power transmission system, or Wireless power transmission system, An AI-based platform according to claim 26, representing at least one of the following.

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

37. An AI-based platform that enables intelligent orchestration and management of power and energy. A set of nodes, each representing at least one distributed energy resource (DER), and A set of edges that interconnect a set of nodes. A graph neural network including, An attention model that shows at least one attention relationship between at least two nodes in a set of nodes of a graph neural network, An AI-based platform that includes this.

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

39. The AI-based platform according to claim 38, further comprising an AI-based energy adjustment model configured to adjust and manage power and energy between at least one distributed energy resource based on the dependency model.

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

41. The AI-based platform according to claim 40, further comprising an AI-based energy adjustment model configured to adjust and manage power and energy between at least one distributed energy resource based on an energy flow model.

42. At least one node in the set of nodes contains information about at least one distributed energy resource, and this information is The power generation capacity of at least one distributed energy resource, The power storage capacity of at least one distributed energy resource, The transmission capacity of at least one distributed energy resource, The power exchange capacity of at least one distributed energy resource, The power conversion capacity of at least one distributed energy resource, The power supply capacity of at least one distributed energy resource, or The power consumption capacity of at least one distributed energy resource, The AI-based platform according to claim 37, comprising at least one of the following.

43. At least one node in the node group provides information about at least one energy-related pattern associated with at least one distributed energy resource, and the at least one energy-related pattern is Energy demand patterns, Energy supply patterns, Energy storage capacity patterns, Energy market price patterns, Energy availability patterns, or Energy emission patterns, The AI-based platform according to claim 37, comprising at least one of the following.

44. At least one edge in a graph neural network representing energy-related features between at least two nodes indicates at least one energy-related relationship between at least two nodes, and at least one energy-related relationship is At least two electrical connection relationships between nodes, Power transmission capability relationship between at least two nodes, Power transfer relationship between at least two nodes, Power exchange relationship between at least two nodes, Power conversion relationship between at least two nodes, Power generation dependency between at least two nodes, Power supply dependency between at least two nodes, Power supply dependency between at least two nodes, Power storage dependency between at least two nodes, or Power consumption dependencies between at least two nodes, The AI-based platform according to claim 37, comprising at least one of the following.

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

46. At least one edge in a graph neural network representing energy-related features between at least two nodes indicates at least one energy-related event, and at least one energy-related event is, Energy generation events associated with at least two nodes, Energy storage events related to at least two nodes, Energy transmission events related to at least two nodes, Energy supply events related to at least two nodes, Energy demand events related to at least two nodes, Energy surplus events related to at least two nodes, Energy shortage events related to at least two nodes, Energy consumption events related to at least two nodes, Energy emission events related to at least two nodes, or Energy leakage events related to at least two nodes, The AI-based platform according to claim 37, comprising at least one of the following.

47. The AI-based platform according to claim 37, further comprising at least one artificial intelligence model configured to generate a graph neural network based on data relating to at least one distributed energy resource.

48. Data related to at least one distributed energy resource is, Energy generation data related to at least one distributed energy resource, Energy storage data related to at least one distributed energy resource, Energy transmission data related to at least one distributed energy resource, Energy supply data related to at least one distributed energy resource, Energy demand data related to at least one distributed energy resource, Energy surplus data related to at least one distributed energy resource, Energy shortage data related to at least one distributed energy resource, Energy consumption data related to at least one distributed energy resource, Energy emissions data related to at least one distributed energy resource, or Energy leakage data related to at least one distributed energy resource, The AI-based platform according to claim 47, comprising at least one of the following.

49. Data related to at least one distributed energy resource is, Historical data showing at least one historical characteristic related to at least one distributed energy resource, Current data showing at least one current characteristic related to at least one distributed energy resource, or Predictive data showing at least one predictive characteristic related to at least one distributed energy resource, The AI-based platform according to claim 47, comprising at least one of the following.

50. A distributed energy resource represented by at least one node of a graph neural network is, Wind turbines, Solar power generation (PV), Flexible solar power generation system, Floating solar power generation system, Solar power plant, fuel cell, Coal mine, oil wells, Natural gas well, Modular nuclear reactor, nuclear battery, Modular hydroelectric power generation system, microturbine, Turbine array, Reciprocating engine, Combustion turbine, Cogeneration plant, Biomass power generators, General waste incinerator, Battery storage system, Capacitive energy storage systems, Geothermal energy systems, Molten salt energy storage system, Electrical thermal energy storage (ETES) system, Gravity-type energy storage system, Compressible fluid energy storage, Pumped-storage hydroelectric energy storage (PHES) system, Liquid air energy storage (LAES) system, Coal storage facilities, Petroleum storage tanks, Natural gas storage tanks, Liquefied natural gas (LNG) storage tanks, Flywheel, gravity battery, Fuel transport vehicles, Fuel transport pipeline, Wired power transmission system, or Wireless power transmission system, The AI-based platform according to claim 37, comprising at least one of the following.

51. An AI-based platform that enables intelligent orchestration and management of power and energy. A set of nodes, each representing at least one distributed energy resource (DER), and A group of edges that interconnects a group of nodes, A graph neural network including, A large-scale language model configured to generate at least one description of the set of nodes and the set of edges included in the aforementioned graph neural network, An AI-based platform that includes this.

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

53. At least one description generated by the aforementioned large-scale language model is, A description of at least one energy-related capacity of a distributed energy resource associated with at least one node in a node set. Description of aggregated energy-related capabilities based on at least a portion of the node group, Description of energy-related costs of distributed energy resources associated with at least one node in a cluster of nodes. Description of aggregated energy-related costs based on at least a portion of the node group, Explanation of energy-related shortages for at least one decentralized energy resource, An aggregated explanation of energy-related shortages based on at least a portion of the node group, A description of an energy-related event associated with at least one node in the node group, or A summary of energy-related events based on at least a portion of the node group, The AI-based platform according to claim 51, comprising at least one of the following.

54. The at least one description generated by the aforementioned large-scale language model is based on at least one prompt, and the at least one prompt is At least one distributed energy resource associated with a graph neural network, At least one of the distributed energy capabilities associated with a graph neural network, At least one of the distributed energy properties associated with a graph neural network, or At least one energy-related event associated with a graph neural network, An AI-based platform according to claim 51, relating to at least one of the following.

55. The AI-based platform according to claim 51, further comprising an AI-based energy adjustment model configured to adjust and manage power and energy among at least one distributed energy resource based on at least one description generated by the large-scale language model.

56. The AI-based platform according to claim 51, further comprising an AI-based energy presentation model that presents at least one description relating to at least one distributed energy resource based on at least one description generated by the large-scale language model.

57. At least one node in the set of nodes contains information about at least one distributed energy resource, and this information is The power generation capacity of at least one distributed energy resource, The power storage capacity of at least one distributed energy resource, The transmission capacity of at least one distributed energy resource, The power exchange capacity of at least one distributed energy resource, The power conversion capacity of at least one distributed energy resource, The power supply capacity of at least one distributed energy resource, or The power consumption capacity of at least one distributed energy resource, The AI-based platform according to claim 51, comprising at least one of the following.

58. At least one node in the set of nodes provides information about at least one energy-related pattern associated with at least one distributed energy resource, and the at least one energy-related pattern is Energy demand patterns, Energy supply patterns, Energy storage capacity patterns, Energy market price patterns, Energy availability patterns, or Energy emission patterns, The AI-based platform according to claim 51, comprising at least one of the following.

59. At least one edge in a graph neural network representing energy-related features between at least two nodes indicates at least one energy-related relationship between at least two nodes, and at least one energy-related relationship is At least two electrical connection relationships between nodes, Power transmission capability relationship between at least two nodes, Power transfer relationship between at least two nodes, Power exchange relationship between at least two nodes, Power conversion relationship between at least two nodes, Power generation dependency between at least two nodes, Power supply dependency between at least two nodes, Power supply dependency between at least two nodes, Power storage dependency between at least two nodes, or Power consumption dependencies between at least two nodes, The AI-based platform according to claim 51, comprising at least one of the following.

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

61. At least one edge in a graph neural network representing energy-related features between at least two nodes indicates at least one energy-related event, and at least one energy-related event is, Energy generation events associated with at least two nodes, Energy storage events related to at least two nodes, Energy transmission events related to at least two nodes, Energy supply events related to at least two nodes, Energy demand events related to at least two nodes, Energy surplus events related to at least two nodes, Energy shortage events related to at least two nodes, Energy consumption events related to at least two nodes, Energy emission events related to at least two nodes, or Energy leakage events related to at least two nodes, The AI-based platform according to claim 51, comprising at least one of the following.

62. The AI-based platform according to claim 51, further comprising at least one artificial intelligence model configured to generate a graph neural network based on data relating to at least one distributed energy resource.

63. Data related to at least one distributed energy resource is, Energy generation data related to at least one distributed energy resource, Energy storage data related to at least one distributed energy resource, Energy transmission data related to at least one distributed energy resource, Energy supply data related to at least one distributed energy resource, Energy demand data related to at least one distributed energy resource, Energy surplus data related to at least one distributed energy resource, Energy shortage data related to at least one distributed energy resource, Energy consumption data related to at least one distributed energy resource, Energy emissions data related to at least one distributed energy resource, or Energy leakage data related to at least one distributed energy resource, The AI-based platform according to claim 62, comprising at least one of the following.

64. Data related to at least one distributed energy resource is, Historical data showing at least one historical characteristic related to at least one distributed energy resource, Current data showing at least one current characteristic related to at least one distributed energy resource, or Predictive data showing at least one predictive characteristic related to at least one distributed energy resource, The AI-based platform according to claim 62, comprising at least one of the following.

65. A distributed energy resource represented by at least one node of a graph neural network is, Wind turbines, Solar power generation (PV), Flexible solar power generation system, Floating solar power generation system, Solar power plant, fuel cell, Coal mine, oil wells, Natural gas well, Modular nuclear reactor, nuclear battery, Modular hydroelectric power generation system, microturbine, Turbine array, Reciprocating engine, Combustion turbine, Cogeneration plant, Biomass power generators, General waste incinerator, Battery storage system, Capacitive energy storage systems, Geothermal energy systems, Molten salt energy storage system, Electrical thermal energy storage (ETES) system, Gravity-type energy storage system, Compressible fluid energy storage, Pumped-storage hydroelectric energy storage (PHES) system, Liquid air energy storage (LAES) system, Coal storage facilities, Petroleum storage tanks, Natural gas storage tanks, Liquefied natural gas (LNG) storage tanks, Flywheel, gravity battery, Fuel transport vehicles, Fuel transport pipeline, Wired power transmission system, or Wireless power transmission system, The AI-based platform according to claim 51, comprising at least one of the following.

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