Ai-based energy edge platforms, systems, and methods

Abstract

In some embodiments, a configured artificial intelligence system includes a plurality of intelligence models; a scoring system configured to generate know-your-model scores that quantify suitability for specific tasks of each model; a model execution system configured to provide standardized execution environment for the plurality of intelligence models; a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by the plurality of intelligence models and use outcome data as feedback to reinforce model performance; and a governance and analysis system configured to ensure model operations comply with governance standards. The intelligence controller may be configured to receive task requests, analyze task complexity, decompose tasks into manageable subtasks, and dynamically select appropriate models from the plurality of intelligence models to execute each subtask based on model suitability and performance characteristics.

Description

AI-BASED ENERGY EDGE PLATFORMS, SYSTEMS, AND METHODS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of: (i) U.S. Provisional Patent Application No. 63 / 674,493, filed 23 July 2024; (ii) U S Provisional Patent Application No 63 / 848,966, filed 22 July 2025; (iii) U S Patent Application No 19 / 277,308, filed 22 July 2025; and (iv) International Application No. PCT / US25 / 12983, filed 24 January 2025, which claims the benefit of U S. Patent Applications: Serial No. 63 / 625,613 filed 26 January 2024; Serial No. 63 / 638,601 filed 25 April 2024; and Serial No. 63 / 639,907 filed 29 April 2024.

[0002] The patent applications referenced above are hereby incorporated by reference as if fully set forth herein in their entirety.BACKGROUND

[0003] Energy remains a critical factor in the world economy and is undergoing an evolution and transformation, involving changes in energy generation, storage, planning, demand management, consumption and delivery systems and processes. These changes are enabled by the development and convergence of numerous diverse technologies, including more distributed, modular, mobile and / or portable energy generation and storage technologies that will make the energy market much more decentralized and localized, as well as a range of technologies that will facilitate management of energy in a more decentralized system, including edge and Internet of Things networking technologies, advanced computation and artificial intelligence technologies, transaction enablement technologies (such as blockchains, distributed ledgers and smart contracts) and others. The convergence of these more decentralized energy technologies with these networking, computation and intelligence technologies is referred to herein as the “energy edge ”

[0004] The energy market is expected to evolve and transform over the next few decades from a highly centralized model that relies on fossil fuels and a managed electrical grid to a much more distributed and decentralized model that involves many more localized generation, storage, and consumption systems During that transition, a hybrid system will likely persist for many years in which the conventional grid becomes more intelligent, and in which distributed systems will play a growing role A need exists for a platform that facilitates management and improvement of legacy infrastructure in coordination with distributed systems.SUMMARY

[0005] Some embodiments include a configured artificial intelligence system including: an intelligence system including an intelligence controller and a system of models architecture featuring a plurality of intelligence models; wherein the intelligence controller is configured to receive task requests, analyze task complexity, decompose tasks into manageable subtasks, and dynamically select appropriate models from the plurality of intelligence models to execute each subtask based on model suitability and performance characteristics; wherein the plurality of intelligence models encompasses diverse model architectures including one or more of large language models, audio models, visual models, classification models, and foundation or multimodal models; a scoring system configured to generate know-your-model scores that quantify suitability for specific tasks of each model; a model execution system configured to provide standardized execution environment for the plurality of intelligence models; a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by the plurality of intelligence models and use outcome data as feedback to reinforce model performance; and a governance and analysis system configured to ensure model operations comply with governance standards.

[0006] Some embodiments include a configured artificial intelligence system, wherein the intelligence controller implements orchestration algorithms that coordinate simultaneous execution of multiple models within the plurality of intelligence models for parallel processing of different aspects of complex tasks.

[0007] Some embodiments include a configured artificial intelligence system, wherein the system of models architecture enables hybrid model topologies including a set of competing model configurations, wherein multiple models process identical inputs simultaneously and outputs are evaluated through automated scoring mechanisms.

[0008] Some embodiments include a configured artificial intelligence system, wherein the set of competing model configurations includes ensemble voting mechanisms that combine outputs from multiple models through structured decision-making processes including majority voting and weighted consensus mechanisms.

[0009] Some embodiments include a configured artificial intelligence system, wherein the system of models architecture supports hierarchical hybrid topologies that process tasks through multiple organizational levels with different models operating at local subsystem levels and integrator models reasoning over combined outputs at higher system levels.

[0010] Some embodiments include a configured artificial intelligence system, wherein the system of models architecture enables federated hybrid topologies that coordinate collaborative model operation across distributed environments while maintaining data privacy and security boundaries.

[0011] Some embodiments include a configured artificial intelligence system, further including a hardware abstraction layer that provides unified interface for coordinating Al model execution across heterogeneous hardware platforms while abstracting underlying complexity of different chipset architectures

[0012] Some embodiments include a configured artificial intelligence system, wherein the hardware abstraction layer includes a model-to-hardware mapping module that analyzes model characteristics and computational requirements to determine optimal hardware assignments for different Al models.

[0013] Some embodiments include a configured artificial intelligence system, wherein the hardware abstraction layer includes a resource management module that implements dynamic resource allocation and performance optimization across hardware acceleration components and specialized processing units

[0014] Some embodiments include a configured artificial intelligence system, wherein the hardware acceleration components include at least one of neural processing units, tensor processing units, graphics processing units, FPGA- based adaptive accelerators, or physics simulation and co-processor chips.

[0015] Some embodiments include a configured artificial intelligence system, wherein the configured artificial intelligence system supports 3D chipset and chiplet architectures that incorporate vertically stacked processing layers to increase computational throughput per unit area while reducing interconnect distances between processing elements.

[0016] Some embodiments include a configured artificial intelligence system, further including a digital twin system configured to create and maintain digital twins, wherein the training and reinforcement system interfaces with the digital twin system to monitor outcomes from model decisions made within digital twin environments

[0017] Some embodiments include a configured artificial intelligence system, wherein the governance and analysis system implements longitudinal tracking capabilities that provide continuous visibility into model performance, decision-making processes, and operational effectiveness over extended time periods.

[0018] Some embodiments include a configured artificial intelligence system, wherein tire training and reinforcement system supports continuous model development through automated retraining capabilities thatincorporate optimization techniques including parameter adjustments, hyperparameter adjustments, and augmented data for new training.

[0019] Some embodiments include a configured artificial intelligence system, wherein the training and reinforcement system implements automation of evolutionary' model refinement through genetic programming techniques that automatically generate, test, and refine model architectures and parameters.

[0020] Some embodiments include a configured artificial intelligence system, configured as a modular system of models in a box that packages complete intelligence system components into a standardized enterprise solution capable of seamless integration with existing organizational technology infrastructure.

[0021] Some embodiments include a configured artificial intelligence system, wherein the modular system of models implements comprehensive enterprise integration capabilities that enable connectivity with at least one of customer relationship management systems, enterprise resource planning systems, or electronic medical record software through standardized APIs.

[0022] Some embodiments include a system-of-models system including: an intelligence controller configured to coordinate execution of multiple artificial intelligence models; a plurality of intelligence models including large language models, visual models, and classification models; a hardware abstraction layer including a model-to- hardware mapping module and a resource management module; and hardware acceleration components including at least one of neural processing units, tensor processing units, or graphics processing units; wherein the intelligence controller implements hybrid model topologies including competing model configurations that process identical inputs through multiple models simultaneously and evaluate outputs through automated scoring mechanisms; wherein the model-to-hardware mapping module analyzes model computational requirements to determine optimal hardware assignments; and wherein the system-of-models system enables dynamic switching between different hybrid topologies based on changing task requirements and performance optimization needs.

[0023] Some embodiments include a system-of-models system, wherein the hybrid model topologies include ensemble voting mechanisms that aggregate individual model contributions into collective decisions through weighted voting that considers each model's confidence scores and historical performance metrics.

[0024] Some embodiments include a method for coordinating artificial intelligence model execution including: receiving, at an intelligence controller, a request to perform a task; analyzing task complexity and decomposing the task into manageable subtasks; dynamically selecting appropriate models from a plurality of intelligence models based on model suitability scores; implementing hybrid model topologies including competing model configurations that process identical inputs through multiple models simultaneously; evaluating outputs from the multiple models through automated scoring mechanisms; mapping selected models to hardware acceleration components through a model-to-hardware mapping module; coordinating parallel execution of the selected models across the hardware acceleration components; and dynamically switching between different hybrid topologies based on real-time assessment of task complexity and performance requirements.

[0025] Some embodiments include an Al-based platform for enabling intelligent orchestration and management of distributed energy resources, including: an adaptive energy data pipeline configured to communicate data across a set of nodes in a network, each node being adapted to operate on an energy data set associated with at least one of energy generation, energy storage, energy delivery, or energy consumption; and a KYM system including a processor and memory and configured to execute KYM Al-based learning models for model lifecycle management of energy optimization models; wherein the adaptive energy data pipeline is configured to filter, compress, transform, errorcorrect and route energy data sets based on at least one of network conditions, data size, data granularity, or data content.

[0026] Some embodiments include an Al-based platform, wherein the adaptive energy data pipeline is configured to match communication value with quality-of-service needs of routes associated with energy-related communications.

[0027] Some embodiments include an Al-based platform, wherein the KYM system includes a model evaluation and risk assessment scoring system configured to generate quantitative scores for foundational properties, task performance, and safety management.

[0028] Some embodiments include an Al-based platform, wherein the adaptive energy data pipeline is configured to prioritize occurrence and frequency of communications based on matching value with quality-of-service needs

[0029] Some embodiments include an Al-based platform, further including intelligent data layers configured to produce at least one of energy generation data layers, energy storage data layers, energy delivery data layers, or energy consumption data layers.

[0030] Some embodiments include an Al-based platform, wherein the adaptive energy data pipeline is configured to identify and use least-cost routes for communications and switch to higher-cost routes to meet quality-of-service needs.

[0031] Some embodiments include an Al-based platform, wherein the KYM system is configured to perform at least one of model intake and registration actions, model evaluation and risk assessment actions, or model deployment actions for energy optimization models

[0032] Some embodiments include an Al-based platform, wherein the adaptive energy data pipeline includes quality-of-service needs for communicating reports of at least one of energy generation, energy storage, energy transport, or energy consumption occurrences

[0033] Some embodiments include an Al-based platform, wherein the adaptive energy data pipeline is configured to forecast energy demands and adjust data transmission processes accordingly.

[0034] Some embodiments include an Al-based platform, wherein the intelligent data layers are configured to perform at least one of extraction, transformation, loading, normalization, cleansing, compression, route selection, protocol selection, self-organization of storage, filtering, timing of transmission, encoding, or decoding.

[0035] Some embodiments include an Al-based platform for adaptive energy data management, including: an adaptive energy data pipeline configured to communicate data across a set of nodes in a network, each node being adapted to operate on an energy data set associated with at least one of energy generation, energy storage, energy delivery’, or energy consumption; a system of models architecture including an intelligence controller configured to coordinate energy optimization models; configurable data and intelligence modules configured to access data from various sources throughout the Al-based platform; and a cross-service resource optimization system including one or more Al agents trained to manage, configure, deploy, provision, and optimize subsystems operating within a linked system; wherein the system of models architecture includes at least one of model execution systems or model interface systems for coordinating multiple Al models.

[0036] Some embodiments include an Al-based platform, wherein the adaptive energy data pipeline is configured to prioritize data transmission from energy storage systems during predicted demand spikes.

[0037] Some embodiments include an Al-based platform, wherein the configurable data and intelligence modules provide at least one of batches, files, database reports, event logs, or data streams as configured outputs.

[0038] Some embodiments include an Al-based platform, wherein the intelligence controller is configured to receive task requests, analyze complexity, decompose into subtasks, and dynamically select appropriate energy optimization models.

[0039] Some embodiments include an Al-based platform, wherein the cross-service resource optimization system is configured to measure and allocate energy used across platforms including at least one of battery storage by devices, energy use by GPUs in cloud computing, or energy use by data centers for generative Al workloads.

[0040] Some embodiments include an Al-based platform, further including at least one of distributed ledger and smart contract systems configured to ensure secure and transparent transactions, or energy simulation systems configured to model potential energy scenarios.

[0041] Some embodiments include an Al-based platform, wherein the adaptive energy data pipeline includes network topology adaptation capabilities based on parameters of the network.

[0042] Some embodiments include an Al-based platform, wherein the system of models architecture includes training and reinforcement systems configured to monitor outcomes and provide feedback for model improvement.

[0043] Some embodiments include an Al-based platform, wherein the configurable data and intelligence modules are configured to be automatically generated and pushed to other systems or queried and pulled from distributed databases.

[0044] Some embodiments include a method for adaptive energy data pipeline management, including: communicating data across nodes in a network using an adaptive energy data pipeline, wherein each node operates on energy data sets; executing KYM Al-based learning models to manage lifecycle of energy optimization models; filtering, compressing, and routing energy data sets based on at least one of network conditions or data content; and coordinating energy optimization models using a system of models architecture.

[0045] Some embodiments include an Al-based platform for enabling intelligent orchestration and management of distributed energy resources, including: a set of intelligence enablement systems including at least one of intelligent data layers, distributed ledger and smart contract systems, adaptive energy digital twin systems, or energy simulation systems; and a plurality of Al-based energy orchestration, optimization, and automation systems including at least two of energy generation orchestration systems, energy consumption orchestration systems, energy marketplace orchestration systems, energy delivery orchestration systems, or energy storage orchestration systems; wherein the set of intelligence enablement systems is configured to utilize algorithms and computational tools to parse datasets, perform pattern recognition, and / or perform informed decision-making.

[0046] Some embodiments include an Al-based platform, wherein the intelligent data layers are configured to manage and process information for energy-relevant tasks including at least one of prediction, forecasting, optimization, automated discovery, configuration, execution of energy transactions, monitoring, tracking, or automated generation of energy-related content.

[0047] Some embodiments include an Al-based platform, wherein the distributed ledger and smart contract systems are configured to ensure secure and transparent transactions and data management for energy-related operations

[0048] Some embodiments include an Al-based platform, wherein the adaptive energy digital twin systems are configured to create virtual replicas of physical energy assets for at least one of monitoring or optimization.

[0049] Some embodiments include an Al-based platform, wherein the energy simulation systems are configured to model potential energy scenarios to aid in decision-making.

[0050] Some embodiments include an Al-based platform, further including a KYM system configured to manage Al model deployment and monitoring for energy applications.

[0051] Some embodiments include an Al-based platform, wherein the energy generation orchestration systems are configured to manage and coordinate energy production sources

[0052] Some embodiments include an Al-based platform, wherein the energy consumption orchestration systems are configured to oversee and optimize how energy is used.

[0053] Some embodiments include an Al-based platform, wherein the energy marketplace orchestration systems are configured to facilitate energy trading and transactions.

[0054] Some embodiments include an Al-based platform, wherein the energy delivery orchestration systems are configured to ensure efficient and reliable energy distribution.

[0055] Some embodiments include an Al-based intelligence enablement platform, including: a set of intelligence enablement systems including at least one of intelligent data layers, distributed ledger and smart contract systems, adaptive energy digital twin systems, or energy simulation systems; configurable data and intelligence modules including at least one of energy transaction enablement systems, stakeholder energy digital twins, or data integrated microservices; a cross-service resource optimization system including Al agents trained to manage, configure, deploy, provision, and optimize subsystems operating within a linked system; a governance and analysis system configured to ensure compliance with established governance standards; and a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by models; wherein the cross-service resource optimization system is configured to measure and allocate energy used across platforms including at least one of batter;' storage by devices, energy use by GPUs in cloud computing, or energy use by data centers for generative Al workloads.

[0056] Some embodiments include an Al-based intelligence enablement platform, wherein the energy transaction enablement systems are configured to facilitate and streamline energy-related transactions.

[0057] Some embodiments include an Al-based intelligence enablement platform, wherein the stakeholder energy digital twins provide virtual representations of stakeholder-specific energy assets for at least one of monitoring or management.

[0058] Some embodiments include an Al-based intelligence enablement platform, wherein the data integrated microservices enable configured stakeholder energy edge solutions ensuring integrated energy management approaches.

[0059] Some embodiments include an Al-based intelligence enablement platform, wherein the governance and analysis system implements monitoring and analytics frameworks that provide visibility into Al system behavior through at least one of real-time data collection, analysis, or reporting capabilities.

[0060] Some embodiments include an Al-based intelligence enablement platform, wherein the training and reinforcement system supports various types of learning models including at least one of supervised learning, unsupervised learning, semi-supervised learning, deep learning, regression, decision tree, or random forest models

[0061] Some embodiments include an Al-based intelligence enablement platform, further including a reporting system configured to provide comprehensive documentation and transparency of model outcomes over extended operational periods.

[0062] Some embodiments include an Al-based intelligence enablement platform, wherein the governance and analysis system is configured to monitor model behavior, validate outputs against policy requirements, and implement safeguards.

[0063] Some embodiments include an Al-based intelligence enablement platform, wherein the training and reinforcement system is configured to use feedback data to reinforce model performance through iterative improvement processes.

[0064] Some embodiments include a method for intelligence enablement in energy management, including: utilizing intelligence enablement systems as cognitive backbone with advanced algorithms for energy management; orchestrating energy operations using Al-based systems for at least two of energy generation, energy consumption, energy marketplace operations, energy delivery, or energy storage; optimizing cross-service resources using Al agents trained to manage and optimize subsystems; and ensuring compliance through governance and analysis systems.

[0065] Some embodiments include an Al-based platform for enabling intelligent orchestration and management of distributed energy resources, including: a digital twin platform configured to provide an environment for decision making with adaptive energy digital twins representing energy stakeholder entities; and a decision making framework for distributing authority among at least one of human beings, human- Al systems, or autonomous Al systems; wherein the decision making framework is selected from at least one of hierarchical, rules-based, simulation, enterprise planning, algorithmic, principles-based, collaborative, peer-to-peer, or competitive frameworks.

[0066] Some embodiments include an Al-based platform, wherein the digital twin platform includes an interface system for designating trainers for Al system creation and a system for displaying training metrics

[0067] Some embodiments include an Al-based platform, wherein the adaptive energy digital twins are configured to represent at least one of energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy usage priority conditions.

[0068] Some embodiments include an Al-based platform, further including a KYM system integrated with the digital twin platform to generate digital twin representations of Al models subject to model intake and registration processes.

[0069] Some embodiments include an Al-based platform, wherein the digital twin platform includes an embedded intelligent agent system for discovering available systems among at least one of human systems, combined human- AI systems, or standalone artificial intelligence systems.

[0070] Some embodiments include an Al-based platform, wherein the adaptive energy digital twins are configured to provide at least one of visual indicators of energy consumption by energy consumers, analytic indicators of energy consumption, filtering of energy data, highlighting of energy data, or adjustment of energy data.

[0071] Some embodiments include an Al-based platform, wherein the KYM system is configured to analyze intake and registration data and generate digital twin representations that model at least one of Al model behavior, performance characteristics, or operational parameters

[0072] Some embodiments include an Al-based platform, wherein the decision making framework is configured to distribute authority based on contextual factors including at least one of time of day, workforce availability, market data, or environmental data.

[0073] Some embodiments include an Al-based platform, wherein the adaptive energy digital twins are configured to generate visual and analytic indicators of energy consumption by at least one of machines, factories, or vehicles in vehicle fleets

[0074] Some embodiments include an Al-based platform, wherein the digital twin platform is configured to run simulations receiving parameters and executing simulations using libraries that model behaviors of different types of systems.

[0075] Some embodiments include an Al-based digital twin and simulation platform, including: a digital twin platform configured to provide an environment for decision making with adaptive energy digital twins representing energy stakeholder entities and configured to create, maintain, and interrogate digital twins with data visualization features; one or more energy simulation systems configured to model potential energy scenarios including simulation environments that simulate outcomes of algorithms governing at least one of generation, consumption, or storage; a training and reinforcement system configured to interface with the digital twin platform to monitor outcomes relating to decisions or predictions made by models within digital twin environments; a data services system configured to provide at least one of data lakes or data pools maintained for training and reinforcement of models; and a governance and analysis system configured to ensure actions comply with governance standards throughout feedback processes; wherein the one or more energy simulation systems are configured to simulate interaction of non-controllable loads and optimized charging processes.

[0076] Some embodiments include an Al-based digital twin and simulation platform, wherein the digital twin platform incorporates data from real-world entities that are twinned providing comprehensive comparison capabilities between simulated and actual performance results

[0077] Some embodiments include an Al-based digital twin and simulation platform, wherein the training and reinforcement system is configured to collect outcome data from model predictions and decisions, analyze at least one of success or failure, and use feedback to adjust model parameters or retrain models.

[0078] Some embodiments include an Al-based digital twin and simulation platform, wherein the digital twin platform provides at least one of output to, integration with, or data sharing with adaptive energy digital twin systems.

[0079] Some embodiments include an Al-based digital twin and simulation platform, wherein the one or more energy simulation systems enable simulation of various outcomes for renewable energy transitions including at least one of solar panel responses to weather conditions, wind turbine operations during seasons, or energy storage management during peak demand.

[0080] Some embodiments include an Al-based digital twin and simulation platform, wherein the data services system includes data augmentation sendees with capabilities for at least one of normalizing data sets, synthesizing data for inclusion in data sets, or anonymizing data while preserving longitudinal tracking capabilities.

[0081] Some embodiments include an Al-based digital twin and simulation platform, wherein the governance and analysis system implements at least one of intrinsic governance monitoring general operations or extrinsic governance monitoring proposed outputs

[0082] Some embodiments include an Al-based digital twin and simulation platform, further including an intelligence controller configured to coordinate execution of model decisions within digital twin environments while monitoring intermediate subtasks and aggregating results.

[0083] Some embodiments include an Al-based digital twin and simulation platform, wherein the governance and analysis system implements decision-making algorithms that consider multiple factors including at least one of severity levels, historical patterns, or potential impact assessments based on longitudinal performance data.

[0084] Some embodiments include a method for digital twin and simulation-based energy management, including: creating and maintaining digital twins with data visualization features for energy stakeholder entities; modeling potential energy scenarios using simulation environments that simulate algorithm outcomes: monitoring outcomes from digital twin environments and using feedback data to reinforce model performance: distributing decision-making authority among at least one of human beings, human-AI systems, or autonomous Al systems: and ensuring compliance with governance standards through at least one of intrinsic or extrinsic governance monitoring.

[0085] Some embodiments include a cross- service resource optimization system for energy management, including: a plurality of Al agents trained to manage, configure, deploy, provision, and / or optimize subsystems operating within a linked system; and a converged workflow orchestration system including Al algorithms for transaction monitoring and machine learning techniques for risk assessment; wherein the cross-service resource optimization system is configured to measure and allocate energy used across platforms including at least one of battery storage by devices, energy use by GPUs in cloud computing, or energy use by data centers for generative Al workloads.

[0086] Some embodiments include a cross-sendee resource optimization system, wherein the plurality of Al agents is configured to coordinate distributed energy assets and balance system-wide energy consumption.

[0087] Some embodiments include a cross-service resource optimization system, wherein the converged workflow orchestration system includes at least one of robotics and process automation for repetitive tasks or blockchain technology for transaction recording

[0088] Some embodiments include a cross-service resource optimization system, further including automated governance modules for policy automation and regulatory framework monitoring.

[0089] Some embodiments include a cross-service resource optimization system, wherein the cross-service resource optimization system implements at least one of deep neural networks for pattern recognition, predictive analytics, natural language processing for transaction documents, or cloud computing infrastructure.

[0090] Some embodiments include a cross-service resource optimization system, wherein the plurality of Al agents implement resource optimization frameworks to coordinate distributed energy assets.

[0091] Some embodiments include a cross-service resource optimization system, further including API integrations for system communication and real-time monitoring capabilities.

[0092] Some embodiments include a cross-service resource optimization system, wherein the converged workflow orchestration system provides capabilities for at least one of automated edge transaction orchestration, adjustment of transaction parameters, monitoring of marketplace conditions, or analysis of sensor data from energy entities.

[0093] Some embodiments include a cross-service resource optimization system, wherein the cross-service resource optimization system includes resource optimization modules that provide at least one of real-time monitoring capabilities, predictive analytics, automated control systems, resource allocation optimization, or automated execution of optimization strategies.

[0094] Some embodiments include a cross-service resource optimization system, wherein the plurality of Al agents are configured to optimize computational resource allocation and manage energy resources across subsystems.

[0095] Some embodiments include an Al-based enterprise transactional decision support system, including: a crossservice resource optimization system for energy management, including: Al agents trained to manage, configure,deploy, provision, and optimize subsystems operating within a linked system; and a converged workflow orchestration system including Al algorithms for transaction monitoring and machine learning techniques for risk assessment; one or more strategic energy resource planning and / or transaction planning simulation modules; one or more sets of digital twins and a set of intelligent dashboards configured to integrate operational data; and one or more automated governance systems configured to facilitate energy transactions and operations including distributed energy resource governance and energy grid governance capabilities; wherein the Al-based enterprise transactional decision support system implements energy edge convergence capabilities including automated governance of energy transactions and Al-based enterprise decision support.

[0096] Some embodiments include an Al-based enterprise transactional decision support system, wherein the energy edge convergence capabilities include simulation and modeling tools for complex energy scenarios.

[0097] Some embodiments include an Al-based enterprise transactional decision support system, wherein the set of intelligent dashboards provides real-time visualization of energy operations and performance metrics.

[0098] Some embodiments include an Al-based enterprise transactional decision support system, wherein the one or more sets of digital twins integrate operational data from multiple sources to provide comprehensive system representations.

[0099] Some embodiments include an Al-based enterprise transactional decision support system, wherein the one or more automated governance systems include at least one of policy automation systems, regulatory compliance monitoring, transaction orchestration, or workflow optimization.

[0100] Some embodiments include an Al-based enterprise transactional decision support system, wherein the AI- based enterprise transactional decision support system implements at least one of intelligent edge networking, context-aware sensor fusion, analytics integration, Al classification systems, or optimization systems.

[0101] Some embodiments include an Al-based enterprise transactional decision support system, wherein the one or more automated governance systems implement at least one of Al-based decision support, strategic planning capabilities, marketplace simulation, operational data integration, or digital twin modeling.

[0102] Some embodiments include an Al-based enterprise transactional decision support system, further including context-aware sensor fusion systems for energy management with data fusion capabilities for marketplace data and operational data integration.

[0103] Some embodiments include an Al-based enterprise transactional decision support system, wherein the AI- based enterprise transactional decision support system provides joint optimization of energy and computation with computation resource management and energy resource management.

[0104] Some embodiments include a method for cross-service resource optimization in energy systems, including: managing and optimizing subsystems using Al agents trained for linked system operations; orchestrating converged workflows with Al algorithms for transaction monitoring; measuring and allocating energy used across platforms including battery storage and cloud computing; implementing automated governance for energy transactions and operations; and providing enterprise decision support through strategic planning and intelligent dashboards

[0105] Some embodiments include a virtual power plant system for distributed energy resource management, including: one or more aggregation and management modules for heterogeneous energy resources including at least one of solar plants, battery storage systems, wind turbines, electric vehicle charging stations, demand response management centers, or smart meters; and a DER market orchestration system including at least one of market forming systems, market demand systems, market response systems, or market value analysis systems; wherein thevirtual power plant system is configured to manage small, isolated power generation points used for load-leveling and to absorb excess supply from intermittent renewables.

[0106] Some embodiments include a virtual power plant system, wherein the virtual power plant system is configured to deliver supply during shortages and manage load-leveling operations

[0107] Some embodiments include a virtual power plant system, wherein the DER market orchestration system includes energy marketplaces based on at least one of type of energy or location of energy.

[0108] Some embodiments include a virtual power plant system, further including transaction aggregation systems configured to automatically orchestrate energy -related transactions for at least one of energy generation, energy storage, energy delivery, energy consumption, renewable energy credits, carbon abatement credits, or pollution abatement credits.

[0109] Some embodiments include a virtual power plant system, wherein the market demand systems include transaction aggregation capabilities for automated energy transaction orchestration.

[0110] Some embodiments include a virtual power plant system, further including a DER market interface configured to broadcast information regarding current and forecasted energy capacity and pricing.[OHl] Some embodiments include a virtual power plant system, wherein the market forming systems are configured to establish and maintain energy trading environments.

[0112] Some embodiments include a virtual power plant system, wherein the market response systems are configured to coordinate responses to energy demand fluctuations.

[0113] Some embodiments include a virtual power plant system, wherein the market value analysis systems are configured to assess and optimize energy transaction values

[0114] Some embodiments include a virtual power plant system, wherein the virtual power plant system includes market orchestration assist layers configured to coordinate multiple DER providers and DER clients.

[0115] Some embodiments include a comprehensive energy marketplace orchestration platform, including : a virtual power plant system for distributed energy resource management, including: aggregation and management capabilities for multiple heterogeneous energy resources including at least one of solar plants, battery storage systems, wind turbines, electric vehicle charging stations, demand response management centers, or smart meters; and a DER market orchestration layer including at least one of market forming systems, market demand systems, market response systems, or market value analysis systems; a futures market optimization system configured to automatically orchestrate aggregation of futures markets contracts based on forecast of future energy needs; an energy trading systems intelligence platform including at least one of neural networks for automated energy trading, deep learning models for price prediction, natural language processing systems for market analysis, or reinforcement learning algorithms for trading optimization; and at least one set of distributed ledger and smart contract systems configured to enable energy-related transactions including at least one of purchases, sales, leases, futures contracts, renewable energy credits, carbon abatement credits, pollution abatement credits, leasing of assets, shared economy transactions, shared consumption contracts, bulk purchases, or provisioning of mobile resources; wherein the futures market optimization system generates forecasts using at least one of machine learning on outcomes, human output, or human- labeled data.

[0116] Some embodiments include a comprehensive energy marketplace orchestration platform, wherein the futures market optimization system is configured to design, configure, and execute series of futures market transactions across various jurisdictions.

[0117] Some embodiments include a comprehensive energy marketplace orchestration platform, wherein the energy trading systems intelligence platform implements automated trading algorithms with real-time market analysis.

[0118] Some embodiments include a comprehensive energy marketplace orchestration platform, wherein the at least one set of distributed ledger and smart contract systems ensures secure and transparent transaction processing

[0119] Some embodiments include a comprehensive energy marketplace orchestration platform, wherein the futures market optimization system bases forecasts on at least one of historical usage patterns, current operating conditions, current market conditions, or anticipated operational needs.

[0120] Some embodiments include a comprehensive energy marketplace orchestration platform, further including energy transaction intelligent agents configured to at least one of discover counterparties, discover arbitrage opportunities, design smart contracts, generate smart contracts, deploy smart contracts, optimize transaction parameters, recommend contract execution steps, or resolve contracts upon completion.

[0121] Some embodiments include a comprehensive energy marketplace orchestration platform, wherein the energy trading systems intelligence platform includes predictive models for market trend analysis and automated decisionmaking.

[0122] Some embodiments include a comprehensive energy marketplace orchestration platform, wherein the at least one set of distributed ledger and smart contract systems provides immutable transaction records and automated contract execution.

[0123] Some embodiments include a comprehensive energy marketplace orchestration platform, further including market broadcast and poll systems for providing information regarding current and forecasted energy capacity and pricing

[0124] Some embodiments include a method for virtual power plant and market orchestration, including: aggregating and managing multiple heterogeneous energy resources using virtual power plant systems; orchestrating energy markets using DER market orchestration layers with market forming, demand, response, and value analysis systems: optimizing futures market contracts based on forecasted energy needs; implementing automated energy trading using neural networks and deep learning models; and enabling secure energy transactions using distributed ledger and smart contract systems.

[0125] Some embodiments include a chipset system for systems of models enabling Al model execution, including: a hardware abstraction layer containing at least one of model-to-hardware mapping modules or resource management modules; a plurality of hardware acceleration components including at least one of a neural processing unit, a tensor processing unit, a graphics processing unit, a FPGA-based adaptive accelerator, or a physics simulation and coprocessor chip; and a specialized processing unit including at least one of an embedded microcontroller, a real-time control unit, a sensor fusion processor, a trusted platform module, a hardware security modules, or an edge Al systemon-chip implementation; wherein the hardware abstraction layer provides unified interface for coordinating Al model execution across heterogeneous hardware platforms.

[0126] Some embodiments include a chipset system, wherein the model-to-hardware mapping modules implement algorithms that analyze model characteristics and computational requirements to determine optimal hardware assignments.

[0127] Some embodiments include a chipset system, wherein the resource management modules implement dynamic resource allocation and performance optimization capabilities.

[0128] Some embodiments include a chipset system, wherein tire neural processing unit provides dedicated acceleration for neural network inference operations with specialized instruction sets.

[0129] Some embodiments include a chipset system, wherein the tensor processing unit offers optimized acceleration for tensor operations that form computational foundation of transformer models

[0130] Some embodiments include a chipset system, wherein the graphics processing unit leverages massively parallel architecture to accelerate training and inference operations for visual models and LLMs.

[0131] Some embodiments include a chipset system, wherein the FPGA-based adaptive accelerator enables reconfigurable hardware optimization for specific model requirements.

[0132] Some embodiments include a chipset system, wherein the embedded microcontroller enables deployment of Al models in resource-constrained environments with low power consumption requirements

[0133] Some embodiments include a chipset system, wherein the sensor fusion processor provides specialized capabilities for integrating and processing data from multiple sensor types simultaneously

[0134] Some embodiments include a chipset system, wherein the trusted platform module implements cryptographic acceleration and secure execution environments for Al models and sensitive data.

[0135] Some embodiments include an advanced 3D chipset system for transformer model optimization, including: a chipset architecture for systems of models enabling Al model execution, including: a hardware abstraction layer containing at least one of model-to-hardware mapping modules or resource management modules; hardware acceleration components including at least one of neural processing units, tensor processing units, graphics processing units, FPGA-based adaptive accelerators, or physics simulation and co-processor chips; and specialized processing units including at least one of embedded microcontrollers and real-time control units, sensor fusion processors, trusted platform modules and hardware security modules, or edge Al system-on-chip implementations; a 3D chipset implementation incorporating vertically stacked processing layers that increase computational throughput per unit area; a chiplet architecture that enables modular hardware designs where different processing capabilities are combined based on application requirements; a high-bandwidth memory integration providing direct access to large memory pools with minimal latency; and a chiplet interconnect module implementing high-speed, low-latency communication pathways between processing elements; wherein the advanced 3D chipset system implements advanced thermal management systems including at least one of integrated heat spreaders, thermal interface materials, or active cooling solutions.

[0136] Some embodiments include an advanced 3D chipset system, wherein the 3D chipset implementation reduces interconnect distances between processing elements resulting in improved performance and reduced power consumption.

[0137] Some embodiments include an advanced 3D chipset system, wherein the chiplet architecture provides flexibility to optimize hardware configurations for different model types and deployment scenarios.

[0138] Some embodiments include an advanced 3D chipset system, wherein the high-bandwidth memory integration enables efficient processing of large language models and memory-intensive Al applications

[0139] Some embodiments include an advanced 3D chipset system, wherein the chiplet interconnect module enables efficient coordination between specialized processing units during complex multi-model workflows.

[0140] Some embodiments include an advanced 3D chipset system, wherein the advanced thermal management systems enable reliable operation of high-density processing configurations under intensive computational workloads.

[0141] Some embodiments include an advanced 3D chipset system, wherein the advanced 3D chipset system facilitates higher density' and faster computation making transformer models more cost-effective.

[0142] Some embodiments include an advanced 3D chipset system, further including integration capabilities with data services systems, governance and analysis systems, and external systems including at least one of sensor systems, loT systems, or robotic systems

[0143] Some embodiments include an advanced 3D chipset system, wherein the advanced 3D chipset system supports comprehensive plug-and-play deployment through modular system of models in a box configuration.

[0144] Some embodiments include a method for chipset-optimized Al model execution, including: coordinating Al model execution across heterogeneous hardware platforms using hardware abstraction layers; mapping models to optimal hardware based on computational requirements and performance objectives; implementing dynamic resource allocation using resource management modules; accelerating model operations using specialized hardware acceleration components; optimizing performance using 3D chipset architectures with vertically stacked processing layers; and ensuring secure execution using trusted platform modules and hardware security modules.BRIEF DESCRIPTION OF THE DRAWINGS

[0145] The present disclosure will become more fully understood from the detailed description and the accompanying drawings.

[0146] FIG. 1 is a schematic diagram that presents examples of platforms and main elements according to some embodiments.

[0147] FIGS 2A and 2B are schematic diagrams that present an introduction of main subsystems of a major ecosystem, according to some embodiments

[0148] FIG. 3 is a schematic diagram that presents more detail on distributed energy generation systems, according to some embodiments.

[0149] FIG. 4 is a schematic diagram that presents more detail on data resources, according to some embodiments.

[0150] FIG. 5 is a schematic diagram that presents more detail on configured energy edge stakeholders, according to some embodiments.

[0151] FIG. 6 is a schematic diagram that presents more detail on intelligence enablement systems, according to some embodiments.

[0152] FIG. 7 is a schematic diagram that presents more detail on Al-based energy orchestration, according to some embodiments.

[0153] FIG. 8 is a schematic diagram that presents more detail on configurable data and intelligence, according to some embodiments.

[0154] FIG. 9 is a schematic diagram that presents a dual-process learning function of a dual-process artificial neural network, according to some embodiments.

[0155] FIG. 10 is a schematic view of an exemplary embodiment of a quantum computing sendee according to some embodiments of the present disclosure

[0156] FIG. 11 illustrates quantum computing service request handling according to some embodiments of the present disclosure.

[0157] FIG. 12 is a diagrammatic view of a thalamus service and how it coordinates within the modules in accordance with the present disclosure.

[0158] FIG. 13 is another diagrammatic view of a thalamus service and how it coordinates within the modules in accordance with the present disclosure.

[0159] FIG. 14 is a diagrammatic view of an energy edge converging technology stack in accordance with the present disclosure

[0160] FIG. 15 is a diagrammatic view of a set of capabilities of an energy edge convergence technology stack in accordance with the present disclosure.

[0161] FIG. 16 depicts a schematic of a Distributed Energy Resource (DER) platform.

[0162] FIG. 17 depicts a schematic of a configured DER provider.

[0163] FIG. 18 depicts a schematic of DER generator module.

[0164] FIG. 19 depicts a schematic of DER generator system.

[0165] FIG. 20 depicts a schematic of provider service layer

[0166] FIG. 21 depicts a schematic of a generic DER assist layer.

[0167] FIG. 22 depicts a schematic of a client load.

[0168] FIG. 23 depicts a schematic of a client orchestration layer

[0169] FIG. 24 depicts a schematic of details of the client assist library' of the client orchestration layer.

[0170] FIG. 25 depicts a schematic of DER market orchestration layer.

[0171] FIG. 26 depicts a schematic of a market value analysis system and a DER market assist layer.

[0172] FIG. 27 depicts a schematic of an automated resource orchestration and control system.

[0173] FIG 28 depicts a schematic of a power evaluation system

[0174] FIG 29 depicts a schematic of components and interactions of a data collection architecture involving application of cognitive and machine learning systems to data collection and processing in accordance with the present disclosure.

[0175] FIG. 30 is a schematic view of an example Al convergence system of systems.

[0176] FIG. 31 is a schematic view of an example offering layer.

[0177] FIG. 32 is a schematic view of an example transactions layer.

[0178] FIG. 33 is a schematic view of an example operations layer.

[0179] FIG. 34 is a schematic view of an example network layer.

[0180] FIG. 35 is a schematic view of an example data layer.

[0181] FIG. 36 is a schematic view of an example data layer.

[0182] FIG. 37 is a schematic view of an example intelligent data layer architecture

[0183] FIG. 38 is a schematic view of an example network layer.

[0184] FIG. 39 is a schematic view of an example Al subsystem integrator system.

[0185] FIG. 40 is a schematic view of an example multiplatform attention management system.

[0186] FIG. 41 is a schematic view of an example configured artificial intelligence system.

[0187] FIG 42 is a schematic view of a KYX system

[0188] FIG. 43 is a schematic view of a system of models architecture within the intelligence system of the configured artificial intelligence system.

[0189] FIG. 44 is a schematic view of a chipset architectures for systems of models.

[0190] FIG. 45 is an illustration of a matrix for organizing and interconnecting various features of Al agent understanding.

[0191] FIG. 46 is a schematic diagram detailing an example artificial neural network with multiple layers.

[0192] FIG. 47 is a schematic diagram detailing an example of training and inference of an example artificial neural network.

[0193] FIG 48 is a schematic diagram detailing an example of a determination of attention by a machine learning model

[0194] FIG. 49 is a schematic diagram of a first transformer model.

[0195] FIG. 50 is a schematic diagram of a second transformer model

[0196] FIG. 51 is a schematic diagram detailing an example system in which a large language model including includes a retrieval component that provides a RAG capability.

[0197] FIG. 52 is a schematic diagram detailing an example of tool use by an example Al agent

[0198] FIG. 53 is a schematic diagram detailing an example Al agent featuring an agent loop.

[0199] FIG. 54 is a schematic diagram detailing a development of an artificial neural network by reinforcement learning.DETAILED DESCRIPTIONFIG. 1: INTRODUCTION OF PLATFORM AND MAIN ELEMENTS

[0200] In some embodiments, provided herein is an Al-based energy edge platform, referred to herein for convenience in some cases as simply the platform 102, including a set of systems, subsystems, applications, processes, methods, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent, and in some cases autonomous or semi-autonomous, orchestration and management of power and energy in a variety of ecosystems and environments that include distributed entities (referred to herein in some cases as “distributed energy resources” or “DERs”) and other energy resources and systems that generate, store, consume, and / or transport energy and that include loT, edge and other devices and systems that process data in connection with the DERs and other energy resources and that can be used to inform, analyze, control, optimize, forecast, and otherwise assist in the orchestration of the distributed energy resources and other energy resources.

[0201] By way of example, distributed energy resources (“DERs”) may include (without limitation): wind turbines (including wind turbine farms), solar photovoltaics (PV), flexible and / or floating solar energy systems (including solar energy farms), fuel cells (including natural-gas-fired fuel cells and biomass-fired fuel cells), coal mines, petroleum wells, natural gas wells, modular nuclear reactors, nuclear batteries, modular hydropower systems, microturbincs and turbine arrays, reciprocating engines, combustion turbines, cogeneration plants, biomass generators, municipal solid waste incinerators, battery' storage energy (including chemical batteries and others), capacitive energy storage, geothermal energy systems, molten salt energy storage, electro-thermal energy storage (ETES), gravity-based storage, compressed fluid energy storage, pumped hydroelectric energy storage (PHES), liquid air energy storage (LAES), coal storage facilities, petroleum storage tanks, natural gas storage tanks, liquefied natural gas (LNG) storage tanks, physical energy storage systems such as flywheels, gravity batteries (e.g., mass suspended in a gravity well), fuel transport vehicles, fuel transport pipelines, wired power transmission systems, wireless power transmission systems, or the like.

[0202] In some embodiments, the platform 102 enables a set of configured stakeholder energy edge solutions 108, with a wide range of functions, applications, capabilities, and uses that may be accomplished, without limitation, by using or orchestrating a set of advanced energy resources and systems 104, including DERs and others. The set ofconfigured stakeholder energy edge solutions 108 may integrate, for example, domain- specific stakeholder data, such as proprietary data sets that are generated in connection with enterprise operations, analysis and / or strategy, real-time data from stakeholder assets (such as collected by loT and edge devices located in proximity to the assets and operations of the stakeholder), stakeholder-specific energy resources and systems 104 (such as available energy generation, storage, or distribution systems that may be positioned at stakeholder locations to augment or substitute for an electrical grid), and the like into a solution that meets the energy needs and capabilities of the stakeholder, including baseline, period, and peak energy needs to conduct operations such as large-scale data processing, transportation, production of goods and materials, resource extraction and processing, heating and cooling, and many others

[0203] In some embodiments, the platform 102 (and / or elements thereof) and / or the set of configured stakeholder energy edge solutions 108 may take data from, provide data to and / or exchange data with a set of data resources for energy edge orchestration 110. The platform 102 obtains information from the set of data resources for the energy edge orchestration 110. These data resources may include datasets, ranging from real-time energy consumption metrics to predictive analytics on future energy demands. By using these resources, the platform 102 is able to make decisions that are both timely and informed. The platform 102 is also equipped to provide data back to the set of data resources for the energy edge orchestration 110. Such data may include feedback on energy optimization strategies, insights derived from Al analyses, and / or even raw data collected from various sensors and nodes within the energy infrastructure. This feedback loop ensures that the data resources remain updated, facilitating more accurate and dynamic energy management Further, the set of configured stakeholder energy edge solutions 108, tailored to meet the unique needs of various stakeholders, can contribute data to and derive insights from the platform 102 By way of example, a stakeholder solution designed for a solar energy farm may provide real-time data on solar panel efficiency, which the platform 102 can then use to optimize energy distribution. Such data exchange between the platform 102, the set of configured stakeholder energy edge solutions 108, and the set of data resources for energy edge orchestration 110 ensures that optimizations are based on the most updated available data.

[0204] The platform 102 may include, integrate with, exchange data with and / or otherwise link to a set of intelligence enablement systems 112, a set of Al-based energy orchestration, optimization, and automation systems 114 and a set of configurable data and intelligence modules and services 118. The set of intelligence enablement systems 112 serves as the cognitive backbone of the platform 102. The set of intelligence enablement systems 112, utilizing advanced algorithms and computational tools, enable the platform 102 with the requisite intelligence to parse vast datasets, recognize patterns, and make informed decisions. The set of Al-based energy orchestration, optimization, and automation systems 114 ensures that the platform 102 achieves efficiency and adaptability. By orchestrating energy sources, optimizing energy flows, and automating processes, the set of Al-based energy orchestration, optimization, and automation systems 114 transform the platform 102 into a dynamic entity, responsive to real-time changes and proactive in its strategies. The set of configurable data and intelligence modules and services 118 provides the platform 102 with flexibility of modularity and customization Depending on specific use-cases, stakeholders can configure these modules to cater to their unique requirements.

[0205] The set of intelligence enablement systems 112 may include a set of intelligent data layers 130 that manage and process information, a set of distributed ledger and smart contract systems 132 that ensure secure and transparent transactions and data management, a set of adaptive energy digital twin systems 134 that create virtual replicas of physical energy assets for better monitoring and optimization, and / or a set of energy simulation systems 136 thatmodel potential energy scenarios to aid in decision-making. These integrated systems work collectively within the set of intelligence enablement systems 112 to provide a comprehensive solution for advanced energy management

[0206] The set of Al-based energy orchestration, optimization, and automation systems 114 may include a set of energy generation orchestration systems 138 that manage and coordinate energy production sources, a set of energy consumption orchestration systems 140 that oversee and optimize how energy is used, a set of energy marketplace orchestration systems 146 that facilitate energy trading and transactions, a set of energy delivery orchestration systems 147 that ensure efficient and reliable energy distribution, and a set of energy storage orchestration systems 142 that manage the storage of energy Together, these systems provide a holistic approach to orchestrating the entire energy lifecycle

[0207] The set of configurable data and intelligence modules and services 118 may include a set of energy transaction enablement 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 data integrated microservices 150 that may enable or contribute to enablement of the set of configured stakeholder energy edge solutions 108, ensuring an integrated approach to energy management.

[0208] The platform 102 may include, integrate with, link to, exchange data with, be governed by, take inputs from, and / or provide outputs to one or more artificial intelligence (Al) systems, which may include models, rule-based systems, expert systems, neural networks, deep learning systems, supervised learning systems, robotic process automation systems, natural language processing systems, intelligent agent systems, self-optimizing and selforganizing systems, and others as described throughout this disclosure and in the documents incorporated by reference herein Except where context specifically indicates otherwise, references to Al, or to one or more examples of Al, should be understood to encompass these various alternative methods and systems; for example, without limitation, an Al system described for enabling any of a wide variety of functions, capabilities and solutions described herein (such as optimization, autonomous operation, prediction, control, orchestration, or the like) should be understood to be capable of implementation by operation on a model or rule set; by training on a training data set of human tag, labels, or the like; by training on a training data set of human interactions (e.g., human interactions with software interfaces or hardware systems); by training on a training data set of outcomes; by training on an Al-generated training data set (e.g. , where a full training data set is generated by Al from a seed training data set); by supervised learning; by semi-supervised learning; by deep learning; or the like For any given function or capability that is described herein, neural networks of various types may be used, including any of the types described herein or in the documents incorporated by reference, and, In some embodiments, a hybrid set of neural networks may be selected such that within the set a neural network type that is more favorable for performing each element of a multi-function or multicapability system or method is implemented. As one example among many, a deep learning, or black box, system may use a gated recurrent neural network for a function like language translation for an intelligent agent, where the underlying mechanisms of Al operation need not be understood as long as outcomes are favorably perceived by users, while a more transparent model or system and a simpler neural network may be used for a system for automated governance, where a greater understanding of how inputs are translated to outputs may be needed to comply with regulations or policies.SUBSYSTEMS AND MODULES OF AI-BASED ENERGY ORCHESTRATION, OPTIMIZATION, A D AUTOMATION SYSTEMS

[0209] The set of Al-based energy orchestration, optimization, and automation systems 114 may include the set of energy generation orchestration systems 138, the set of energy consumption orchestration systems 140, the set of energy storage orchestration systems 142, the set of energy marketplace orchestration systems 146 and the set of energy delivery orchestration systems 147, among others For example, the set of energy delivery' orchestration systems 147 may enable orchestration of the delivery of energy to a point of consumption, such as by fixed transmission lines, wireless energy transmission, delivery of fuel, delivery of stored energy (e.g., chemical or nuclear batteries), or the like, and may involve autonomously optimizing the mix of energy types among the foregoing available resources based on various factors, such as location (e.g., based on distance from the grid), purpose or type of consumption (e.g., whether there is a need for very high peak energy delivery, such as for power- intensive production processes), and the like. Consider a remote industrial unit located far from the main grid, requiring power for its production processes. The set of energy generation orchestration systems 138 may analyze the location and determine that connecting such unit to the main grid may not be feasible. Instead, the set of energy generation orchestration systems 138 may suggest that a combination of wireless energy transmission and delivery of chemical batteries may be most suitable in this case.

[0210] In some embodiments, the platform 102 may employ demand forecasting, including automated forecasting by artificial intelligence or by taking a data stream of forecast information from a third party. Among other tilings, forecasting demand helps inform site selection and intelligently planned network expansion In some embodiments, machine learning algorithms may generate multiple forecasts, such as about weather, prices, solar generation, energy demand, and other factors, and analyze how energy assets can best capture or generate value at different times and / or locations

[0211] In some embodiments, the Al-based energy orchestration, optimization, and automation systems 114 may enable energy pattern optimization, such as by analyzing building or other operational energy usage and seeking to reshape patterns for optimization (e.g., by modeling demand response to various stimuli). By analyzing energy consumption trends, the Al-based energy orchestration, optimization, and automation systems 114 can identify areas of wastage or inefficiency. By way of example, they can evaluate how energy consumption of a building varies during different times of the day or in different seasons. Using this knowledge, the automation systems 114 can then reshape these patterns to achieve optimal energy usage. This may be applied in a commercial office building where the AI- based energy orchestration, optimization, and automation systems 114 may notice that energy consumption spikes during the early afternoon due to the simultaneous use of lighting, heating, and cooling systems By modeling how the building may respond to certain stimuli, such as optimizing Heating, Ventilation, and Air Conditioning (IIVAC) system based on real-time occupancy data, the Al-based energy orchestration, optimization, and automation systems 114 can suggest measures to distribute energy consumption more evenly throughout the day, thereby reducing peak demand and associated costs.

[0212] The Al-based energy orchestration, optimization, and automation systems 114 may be enabled by the set of intelligence enablement systems 112 that provide functions and capabilities that support a range of applications and use cases.

[0213] In some embodiments, the platform 102 may be configured to integrate data from an at least one internal edge device located within an environment (e.g. sensors within a building, vehicle, machine, utility) and an at least one external edge device located outside the environment (e.g. sensors on weather monitoring stations broadcasting real-time data, vehicles, etc.). The platform 102 may collect real-time energy intelligence data and provide the real-time energy intelligence data to an intelligence circuit that is trained on the data and outcomes and automatically executes an action 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. . cloud cover, temperature, wind, precipitation, etc ) may be correlated with energy output from the DER, and a machine learning model may be trained to utilize variables from the second edge device to anticipate actions related to the environment of the first edge device. By way of further example, a radar signature output by the weather station edge device may be used to action a ramping up or down of energy from the DER

[0214] In some embodiments, data output from one or more edge devices may be vectorized and / or stored in a distributed database. Capturing energy data from devices may be optimized further through use of vector-based updating of the data in which only changes that impact a model of the consumption information are communicated. The vector may be developed based on the analysis of data from consuming devices described above. A vector for a composite energy consuming system may be a multi-dimensional vector that represents consumption type, purpose, device, and the like to form a highly efficient way of communicating complex energy usage environments. By way of example, consider a smart grid system where thousands of home appliances, EIVAC systems, and lighting solutions are continuously sending energy consumption data. Instead of sending eve ' minute detail, the system analyzes this data, and based on the consumption patterns, develops a vector. This vector, especially for a composite energy consuming system, may include various parameters like consumption type, the purpose of consumption, the specific device consuming energy, among others.

[0215] In some embodiments, patterns of energy usage may include localized patterns, such as based on work-a- day schedules of consumers However, patterns of energy usage may be based on a wider range of data, including weather forecast data; energy consumption in areas being currently affected by a weather system for preparing an area predicted to receive the weather system; and the like Pattern analysis may include not only raw usage, but may include information about consumers (e. ., devices being operated that consume energy) that may impact learnings. By way of example, work-a-day schedules of consumers, which may involve turning off all home appliances during working hours and increasing energy consumption in the evenings, may be a localized pattern which may be recognized and adapted to by the system.

[0216] Demographics and other human-based activity may play a role in energy pattern analysis. In an example, demographics of an area that suggest consumers replace older vehicles with new vehicles more frequently than in other areas may suggest that local energy demand for electric vehicle charging might increase sooner in such areas. When demographics and / or consumer behaviors suggest that consumers in a region tend to replace vehicles with used vehicles, then maintenance of legacy energy sourcing may be indicated as preferred for those areas.Energy Simulation Systems

[0217] In some embodiments, a set of energy simulation systems 136 is provided, such as to develop and evaluate detailed simulations of energy generation, demand response and charge management, including a simulation environment that simulates the outcomes of use of various algorithms that may govern generation across various generations assets, consumption by devices and systems that demand energy, and storage of energy. Data can be used to simulate the interaction of non-controllable loads and optimized charging processes, among other use cases. The simulation environment may provide output to, integrate with, or share data with the set of adaptive energy digital twin systems 1 4. By way of example, if a city plans to 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 mayrespond to varying weather conditions, how wind turbines may operate during different seasons, or how energy storage solutions may need to be managed during peak demand periods

[0218] In some embodiments, as more enterprises embrace hybrid infrastructure, uptime is becoming more complex, requiring backup and failover strategies that span cloud, colocation, on-premises facilities, and edge infrastructure. This may include Al-based algorithms for automatically managing energy for devices and systems in such devices. For example, artificial intelligence may enable autonomous data center cooling and industrial control. In some embodiments, distributed energy resources, or DERs 128, may be integrated into or with, for example, AI- driven computing infrastructure, smart Power Distribution Units (PDUs), Uninterrupted Power Supply (UPS) systems, energy-enabled air How management systems, and HVAC systems, among others. By simulating energy scenarios, the set of energy simulation systems 136 ensures that enterprises, irrespective of their infrastructure model, operate seamlessly and sustainably.SUBSYSTEMS AND MODULES OF INTELLIGENCE ENABLEMENT SYSTEMSIntelligent Data Layers

[0219] The set of intelligence enablement systems 112 may include a set of intelligent data layers 130, such as a set of services (including microservices). APIs, interfaces, modules, applications, programs, and the like which may 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, calculation, transformation, loading, batch processing, streaming, filtering, routing, parsing, converting, pattern recognition, content recognition, object recognition, and others. Through a set of interfaces, a user of the platform 102 may configure tire set of intelligent data layers 130 or outputs thereof to meet internal platform needs and / or to enable further configuration, such as for the set of configured stakeholder energy edge solutions 108. The set of intelligent data layers 130, the set of intelligence enablement systems 112 more generally, and / or the configurable data and intelligence modules and services 118 may access data from various sources throughout the platform 102 and, In some embodiments, may operate from the set of shared data resources, which may be contained in a centralized database and / or in a set of distributed databases, or which may consist of a set of distributed or decentralized data sources, such as loT or edge devices that produce energy-relevant event logs or streams. The set of intelligent data layers 130 may be configured for a wide range of energy -relevant tasks, such as prediction / forecasting of energy consumption, generation, storage or distribution parameters (eg., at the level of individual devices, subsystems, systems, machines, or fleets); optimization of energy generation, storage, distribution or consumption (also at various levels of optimization); automated discovery', configuration and / or execution of energy transactions (including microtransactions and / or larger transactions in spot and futures markets as well as in peer-to-peer groups or single counterparty transactions); monitoring and tracking of parameters and attributes of energy consumption, generation, distribution and / or storage (eg., baseline levels, volatility, periodic patterns, episodic events, peak levels, and the like); monitoring and tracking of energy-related parameters and attributes (eg. , pollution, carbon production, renewable energy credits, production of waste heat, and others); automated generation of energy-related alerts, recommendations and other content (eg. , messaging to prompt or promote favorable user behavior); and many others.

[0220] In some embodiments, the platform 102 may be configured to analyze a monitored energy data set and generate configuration recommendations for a distributed system to produce and consume energy. The platform 102 may be configured to analyze streams from one or more local power consumption entities and generate recommendations. For example, a manufacturing plant may have a set of needs that differ greatly from a hospitalcampus. As such, the Al-based platform may perform analysis of each of a plurality of energy consumption scenarios and related devices and demands, and recommend types of DERs for providing energy and conditioning energy corresponding to the needs and demands of the local power consumption entities. A hospital may have an ER that has a specific set of demands, such as times when an operating theater is open, or contingent demands based on emergencies. Examples of a monitored energy data set may include one or more of grid-based energy resources and mobile energy resources. Grid-based energy resources may include, for example, fossil fuel-based energy production facilities (coal, oil, natural gas, etc.), renewable energy-based production facilities (solar farms, wind farms, geothermal generators, tidal generators, hydroelectric power facilities, etc.) Mobile energy resources may include, for example, mobile battery installations, mobile fossil fuel-based generators, mobile renewable energy producers, mobile transfonners and power conditioning systems, drone-based power delivery / storage systems, vehicle-based power delivery / storage systems, etc.Distributed Ledger and Smart Contract Systems

[0221] The set of intelligence enablement systems 112 may include a smart contract system 132 for handling a set of smart contracts, each of which may optionally operate on a set of blockchain-based distributed ledgers. Each of the smart contracts may operate on data stored in the set of distributed ledgers or blockchains, such as to record energy-related transactional events, such as energy purchases and sales (in spot, forward and peer-to-peer markets, as well as direct counterparty transactions), relevant service charges and the like; transaction relevant energy events, such as consumption, generation, distribution and / or storage events, and other transaction-relevant events often associated with energy, such as carbon production or abatement events, renewable energy credit events, pollution production or abatement events, and the like. The set of smart contracts handled by the smart contract system 132 may consume as a set of inputs any of the data types and entities described throughout this disclosure, undertake a set of calculations (optionally configured in a flow that takes inputs from disparate systems in a multi-step transaction), and provide a set of outputs that enable completion of a transaction, reporting (optionally recorded on a set of distributed ledgers), and the like The set of energy transaction enablement systems 144 may be enabled or augmented by artificial intelligence, including to autonomously discover, configure, and execute transactions according to a strategy and / or to provide automation or semi-automation of transactions based on training and / or supervision by a set of transaction experts.

[0222] In some embodiments, tire smart contract systems 132 may be used by the set of energy transaction enablement systems 144 (described elsewhere in this disclosure) to configure transactional solutions. Each smart contract within the smart contract systems 132 is intricately designed to process data stored within these distributed ledgers or blockchains. The functionality of the smart contracts extends to documenting a variety of energy-associated transactional events. This includes, but is not limited to, recording peer-to-peer energy transactions and even direct transactions between parties. Furthermore, they capture data related to service charges and other transaction-relevant energy events, including information on energy consumption, generation, distribution, and storage For example, an energy grid of a city having integrated renewable energy sources, such as solar and wind, the smart contract systems 132 can autonomously execute contracts that purchase solar energy during peak sunlight hours and wind energy during windy periods. Simultaneously, it records each transaction, the associated service charges, and even the carbon offset achieved by using renewable sourcesAdaptive Energy Digital Twin Systems

[0223] Any entity, analytic results, output of artificial intelligence, state, operating condition, or other feature noted throughout this disclosure may, In some embodiments, be presented in a digital twin, such as the set of adaptive energy digital twin systems 134, which is widely applicable, and / or the set of stakeholder energy digital twins 148, which is configured for the needs of a particular stakeholder or stakeholder solution The set of adaptive energy digital twin systems 134 may, for example, provide a visual or analytic indicator of energy consumption by a set of machines, a group of factories, a fleet of vehicles, or the like: a subset of the same (e.g., to compare energy parameters by each of a set of similar machines to identify out-of-range behavior); and many other aspects. A digital twin may be adaptive, such as to filter, highlight, or otherwise adjust data presented based on real-time conditions, such as changes in energy costs, changes in operating behavior, or the like.

[0224] In some embodiments, the platform 102 may be configured to create, manage, and / or otherwise provide a dynamic digital twin of historical, current, and forecast distributed energy demand for both mobile and fixed entities within a domain based. For example, relatively large companies or organization settings may be modeled via digital twins, such as industrial environments, factory environments, distribution centers, hospital settings, university / college environments, office building settings, mining operations, etc. In a specific example, for a manufacturing facility with numerous machines, assembly lines, and automated systems, the platform 102 can create a digital twin of this environment, capturing every' detail of its energy consumption patterns. Such digital twin can provide real-time information about the energy demands of the facility', from the historical energy usage data of each machine to the present consumption rates, and even predictions about future energy needs based on forecasted production schedules. Larger environments may be modeled where the costs can be shifted significantly based on energy adjustments across entire environment By way of example, in larger environments, where energy consumption is high, even minor adjustments can lead to substantial financial implications. By having a dynamic digital twin, stakeholders can simulate various energy adjustments and analyze their impact. By way of example, in an office building setting, adjusting the operation of the HVAC system based on real-time occupancy data or optimizing lighting based on natural daylight availability can shift the energy costs considerably.

[0225] In some embodiments, the platform 102 may be configured to model government entities via one or more digital twins, such as states, counties, cities, towns, developmental areas, communities, and the like. In an example, for a city, having thousands or hundreds of thousands of residents, businesses, public transport systems, and numerous amenities, the platform 102 can create a digital twin of such city, capturing every aspect of its energy consumption. Tills digital representation may include everything from the lighting in public parks, the HVAC systems in government buildings, to the energy demands of public transport systems. By doing so, the platform 102 offers city administrators a holistic view of the energy footprint of the city, facilitating informed decisions on energy management. The platform 102 can even model larger entities like states or counties, capturing the diverse energy demands of various regions, from urban hubs to rural areas. On the other end, the platform 102 can also represent smaller entities, like towns. By way of example, in a new' town w'hich is being developed for industrial use, the platform 102 can model the expected energy demands based on planned industries, ensuring that the energy infrastructure is adequately prepared to meet the demand. In another example, a county planning to transition to renewable energy sources can utilize its digital twin to simulate the impact of integrating solar farms or wind turbines. This simulation can provide insights into potential energy savings, grid stability, and even the environmental benefits of such a transition.

[0226] In some embodiments, the platform 102 may include an Al-based system for updating a digital twin based on set of energy parameters which may include adapting energy consumption data from a physical device for the digital twin based on the set of energy parameters, such as by adjusting a cost incurred for energy consumed based on a dynamic energy marketplace from which the device sources energy By way of example, consider a device that sources its energy from a dynamic energy marketplace, where the cost of energy fluctuates based on demand, supply, and other market factors. If the device consumes energy at a time when costs are high, the Al-based system can adjust the digital twin to reflect this, ensuring that the virtual representation accurately mirrors the financial implications of real-world energy consumption. The Al-based system may also incorporate energy sourcing preferences of user(s) of the device (optionally as expressed in the device digital twin) when updating the device. By way of example, if a user, through the digital twin of their device, has expressed a preference for green energy, the Al system ensures that this preference is factored into the energy consumption data updates. For a shared device (e.g., e-bike), energy consumed during and / or associated with a user share of the device (while tire e-bike is checked out in the account of the user) may be assigned to / across specific energy source(s) based on the user profile. For example, when a user checks out the e-bike on their user account, the energy consumed during their usage can be specifically sourced from their preferred energy source, as detailed in their user profile associated with the user account. Additionally or alternatively, an owner of the device and / or digital twin may identify an allocation of consumed energy to be assigned to each of a plurality of energy sources. By way of example, there may be scenarios where the owner of the device has specific allocations for consumed energy across multiple energy sources. In such cases, the Al system ensures that the digital twin reflects this allocation accurately For example, an owner may specify that 50% of the energy consumed by a device should be sourced from wind energy and the remaining 50% from hydro energy The Al system, when updating the digital twin, may ensure that this allocation is accurately represented. Thus, the platform 102, with its Al-based system, provides digital twins which are not just static representations but are dynamic, responsive, and tailored to individual preferences and real-world scenarios.

[0227] In some embodiments, the Al-based system for updating a digital twin based on a set of energy parameters may include adapting energy production and / or allocation control for an upcoming time period (e.g., during an upcoming high-demand event and the like) based on the set of energy parameters. This may include relying on an AI- based forecast of energy demand for a future period of time to adjust how an energy sourcing system operates, such as energy parameters that determine how much energy to store versus generate and deliver, for example. By way of example, in a scenario where there is an anticipated high-demand event, perhaps due to a festival, the Al-based system, by analyzing the energy parameters, can predict this surge in demand and adapt the energy production and / or allocation controls accordingly. In another example, based on past data and current trends, the Al-based system may anticipate increased energy demand during the summer months. In addition to Al-based energy demand forecasts, an Al-based system may evaluate macro trends / activity based on the energy parameters. In an example, an Al-based system that updates an energy consumption system may detect pricing patterns that suggest energy costs may sharply increase (e.g., due to a major weather event, or the like), the set of energy parameters may guide the Al-based system to adapt energy consumption and / or storage guidance for at least select consumers (e.g., public systems (e.g., taxbased systems) so as to avoid unnecessary burden on taxpayers). By way of example, if the Al-based system detects patterns suggesting that energy costs may increase due to an upcoming major weather event, it can take preemptive measures. By analyzing the set of energy parameters, the Al-based system may guide certain consumers to adapt theirenergy consumption or storage patterns, or guide public systems to reduce consumption or increase storage. Thus, the platform 102, with its Al-based system, ensures that energy management is proactive and efficient.

[0228] In some embodiments, the platform 102 may be configured to provide and / or facilitate digital twins of common device types (e. ., same model of e-bike) The digital twins may exchange consumption data across a range of instances of use to develop an understanding of how this common device type consumes energy in different environments, during different times of day, different geographies, demographics of users (including demographics local to a point of use). For example, an e-bike used predominantly in a hilly terrain may exhibit different energy consumption patterns compared to one used in a flat urban setting. The platform 102, by aggregating this data from various digital twins, can identify these patterns and make informed predictions. This can allow digital twins of specific devices (a specific e-bike) to better forecast energy demand leading to, among other things, dynamic recharging profiles. Some devices may be located in an area of high demand that suggests a need for more frequent charging, whereas others may be permitted to sustain a lower average energy charge due to, for example, shorter and less frequent utilization. For example, an e-bike stationed in a busy urban center may be identified to require frequent recharging due to high demand; on tire other hand, another e-bike, perhaps stationed in a less frequented area, may operate optimally even without frequent recharging. This can also allow aggregation of demand profiles for a range of geographic areas to identify demand, such as recharging needs, available energy and the like. By way of example, in a locality with a high concentration of e-bikes (for example), the platform 102 may suggest staggered recharging schedules to balance the demand and prevent grid overloads. This can lead to management of charging activities for e-bikes, including demand balance of other rechargeable devices in an area

[0229] In some embodiments, the platform 102 may be configured such that not every physical instance of a device (e.g. , a specific model e-bike) needs to have its own permanent digital twin. Most of these types of devices are dormant for significantly longer durations than they are in use (duty cycle is very sparse), so even energy demand for processing to support digital twins of these types of devices can be managed based on a demand profile. An instance of a physical device (or a configured genetic instance) can be activated (can be allocated energy resources) based on predictions of demand. Consider the scenario of a specific model of an e-bike. While these e-bikes may be scattered across various locations and be available for use all the time, their actual usage or “duty cycle” may be infrequent, with the devices lying dormant for extended periods. Understanding this unique characteristic, the platform 102 is configured in a way that instead of maintaining a continuous digital twin for each e-bike, the platform 102 can activate digital twins for these devices based on predicted demand. By way of example, in an urban setting, if the platform 102 predicts a surge in demand for e-bikes during, say, the morning rush hours, it can activate the digital twins for the e-bikes during such time. These digital twins can then facilitate energy management, ensuring that the e-bikes are charged and ready for use. Post the rush hour, these digital twins can be deactivated to conserve processing energy. This demand-driven approach ensures that energy resources for processing the digital twins are optimally utilized.

[0230] In some embodiments, the platform 102 may provide and / or facilitate sharing, exchange, and / or aggregation of energy consumption data provided to digital twins by physical device instances that can be harvested to establish a set of energy demand parameters for predictive energy demand models, and the like. For example, the platform 102 is designed to facilitate the exchange and aggregation of energy consumption data from various physical device instances and channeled to their respective digital twins. By way of example, consider a neighborhood with multiple smart homes, each equipped with multiple smart devices. While each home may have its unique energy consumption patterns, the collective data from all these homes can reveal broader trends. The platform 102, by aggregating thisdata, may identify patterns like increased energy consumption during holiday seasons or reduced demand during vacation periods. These insights can then inform predictive models, ensuring that energy providers are well-prepared to meet the anticipated demands.

[0231] In some embodiments, the platform 102 may be configured such that energy consumption data provided to digital twins can also facilitate prediction of energy-related demands, such as maintenance of energy providing infrastructure, and the like. For example, a need for addressing waste from energy production can be better predicted based on not only consumption, but supply sourcing that can be available to digital twins. In other words, not only does a physical device consume energy, but it must also be supplied with (or must generate its own) energy. Energy supply and / or sourcing can be used by digital twins to indicate times / regions / specific sources of energy production for support (waste removal, refurbishment, etc.). By way of example, if a local energy production facility predominantly relies on non-renewable sources, the associated waste generation would be higher. The digital twin, by predicting this, can ensure that adequate waste management measures are in place. Further, a digital twin of a local energy production facility can utilize predicted demand from energy consumption digital twins to address not only production, but up-the-chain sourcing. For example, if a predicted demand for (again using e-bikes as the example) e-bike utilization for upcoming event(s) (graduation, new student day, etc.) can be forecasted along with, for example, availability of solar produced energy expectations, local energy supply depots can source up-chain energy only if needed and / or as needed. By way of example, if the solar energy predictions are favorable, the depots can rely predominantly on solar energy, otherwise the depots can source energy from up-the-chain energy providers to meet the demand

[0232] In some embodiments, the platform 102 may include a set of configurable data and intelligence modules and sendees 118. These may include a set of energy transaction enablement systems 144, a set of stakeholder energy digital twins 148, a set of data integrated microservices 150, and others. Each module or service (optionally configured in a microservices architecture) may exchange data with the various data resources in order to provide a relevant output, such as to support a set of internal functions or capabilities of the platform 102 and / or to support a set of functions or capabilities of one or more of the set of configured stakeholder energy edge solutions 108. As one example among many, a service may be configured to take event data from an loT device that has cameras or sensors that monitor a generator and integrate it with weather data from public data resources 162 to provide a weather- correlated timeline of energy generation data for the generator, which in turn may be consumed by a set of configured stakeholder energy edge solutions 108, such as to assist with forecasting day-ahead energy generation by the generator based on a day-ahead weather forecast. A wide range of such configured data and intelligence modules and services 118 may be enabled by the platform 102, representing, for example, various outputs that consist of the fusion or combination of the wide range of energy edge data sources handled by the platform, higher-level analytic outputs resulting from expert analysis of data, forecasts and predictions based on patterns of data, automation and control outputs, and many others.

[0233] In some embodiments, the platform 102 may be configured such that energy consumption devices and / or systems (e.g., a set of energy consuming devices in a household) may arbitrate locally for access to energy sources, such as main line energy, first level stored energy (e.g., at a device), local stored energy (e.g., a local battery that can source energy to a plurality of devices), and the like. Also, devices may consume energy for a range of purposes, consumption, storage, balancing sourcing, acting as a proxy for other devices, and the like. Yet further, energy consuming devices may be configured / configurable to use a plurality of energy types, such as electric grid, solar,geothermal, fossil fuel (combustion engine), hydrogen, and the like. Also, within an energy consumption system (set of devices as noted above) energy consumption may span a range of energy sources (e.g. , hydrogen for cooking, solar for energy storage, waste energy recovery, and the like). By way of example, consider a household equipped with multiple energy-consuming devices, each with its unique energy demands and preferences The platform 102 can facilitate a dynamic environment where these devices can locally arbitrate for access to various energy sources based on their immediate needs and available resources. By way of example, on a sunny day, solar panels in a house may be generating excess energy, in such case, the platform 102 may utilize energy primarily from the solar panels, reducing energy consumption from the grid

[0234] In some embodiments, the platform 102 may capture the energy consumption information from / via the edge devices and develop a data set that represents a plurality of perspectives regarding consumed energy. Edge devices that may communicate (e.g., locally or in close proximity) with a range of energy consuming devices and device types may collect data about tire devices, including, for example, what sources can the devices consume, what source have the devices consumed, purpose / use of the consumed energy, and the like. Further examples may include whether it appear as if the devices performing any sort of optimization, such as utilizing local storage during high energy cost periods (including high transmission costs which might be measured based on efficiencies of the delivery' and the like), consuming energy for replenishing storage during off-peak times, and / or utilizing low cost sources (e. ., solar) when readily available. A wide range of analytics may be generated, captured, used in an energy management system, and the like. By way of example, consider a smart plug connected to a refrigerator which can provide insights into energy consumption patterns thereof, revealing details like its preference for utilizing local storage during high energy cost periods By aggregating this data from various edge devices, the platform 102 can identify patterns, predict future energy demands, and optimize energy consumption across devicesEnergy Transaction Enablement Systems

[0235] Configurable data and intelligence modules and services 118 may include a set of energy transaction enablement systems 144. The set of energy transaction enablement systems 144 may include a set of smart contracts, which may operate on data stored in a set of distributed ledgers or blockchains, such as to record energy-related transactional events, such as energy purchases and sales (in spot, forward and peer-to-peer markets, as well as direct counterparty transactions) and relevant service charges; transaction relevant energy events, such as consumption, generation, distribution and / or storage events, and other transaction-relevant events often associated with energy, such as carbon production or abatement events, renewable energy credit events, pollution production or abatement events, and the like. The set of smart contracts may consume as a set of inputs any of the data types and entities described throughout this disclosure, undertake a set of calculations (optionally configured in a flow that takes inputs from disparate systems in a multi-step transaction), and provide a set of outputs that enable completion of a transaction, reporting (optionally recorded on a set of distributed ledgers), and the like. The set of energy transaction enablement systems 144 may be enabled or augmented by artificial intelligence, including to autonomously discover configure, and execute transactions according to a strategy and / or to provide automation or semi-automation of transactions based on training and / or supervision by a set of transaction experts. Autonomy and / or automation (supervised or semi-supervised) may be enabled by robotic process automation, such as by training a set of intelligent agents on transactional discovery, configuration, or execution interactions of a set of transactional experts with transaction-enabling systems (such as software systems used to configure and execute energy trading activities).

[0236] As energy is increasingly produced and consumed in local, decentralized markets, the energy market is likely to follow patterns of other peer-to-peer or shared economy markets, such as ride sharing, apartment sharing and used goods markets. Technology enables the bypassing of top-down or centralized energy supply and enables operators to create platforms that can manage and monetize spare capacity, such as through the leasing and trading of assets and outputs.

[0237] As more distributed or peer-to-peer transactive energy markets develop, the platform 102 may include systems or link to, integrate with, or enable other platforms that facilitate P2P trading, wholesale contracts, renewable energy certificate (REC) tracking, and broader distributed energy provisioning, payment management and other transaction elements. In some embodiments, the foregoing may use blockchain, distributed ledger and / or smart contract systems 132. By way of example, a homeowner with excess solar energy may decide to sell this surplus energy. This transaction gets securely recorded on the blockchain.

[0238] In some embodiments, with increased transparency, choice, and flexibility, consumers will be able to participate actively in energy markets, by generating, storing, and selling, as well as consuming electricity. By way of example, a local community may decide to capitalize on its collective solar energy generation. The platform 102 enables homes with solar panels to trade their excess energy with those without, ensuring that the entire community benefits.

[0239] In some embodiments, transactional elements may be configured by a set of energy transaction enablement systems 144 to optimize energy generation, storage, or consumption, such as utility time of use charges. Shifting energy demand away from high-priced time periods with loT-based platforms that can identify periods where energy costs are the least expensive By way of example, in regions where utility charges vary based on the time of use, the platform 102 can shift energy demand to periods when energy is cheaper. In an example, smart home devices, linked to the platform 102, can identify periods when energy costs are lowest and adjust operations, ensuring efficient and cost-effective energy consumption.Stakeholder Energy Digital Twins

[0240] The configurable data and intelligence modules and services 118 may include a set of stakeholder energy digital twins 148, which may, In some embodiments, include set of digital twins that are configured to represent a set of stakeholder entities that are relevant to energy, including stakeholder-owned and stakeholder-operated energy generation resour ces, energy distribution resources, and / or energy distribution resources (including representing them by type, such as indicating renewable energy systems, carbon-producing systems, and others); stakeholder information technology and networking infrastructure entities (e. ., edge and loT devices and systems, networking systems, data centers, cloud data systems, on premises information technology systems, and the like); energy- intensive stakeholder production facilities, such as machines and systems used in manufacturing; stakeholder transportation systems; market conditions (e.g. , relating to current and forward market pricing for energy, for the supply chain of the stakeholder, for the stakeholders product and services, and the like), and others The set of stakeholder energy digital twins 148 may provide real-time information, such as provided sensor data from loT and edge devices, event logs, and other information streams, about status, operating conditions, and the like, particularly relating to energy consumption, generation, storage, and or distribution.

[0241] The set of stakeholder energy digital twins 148 may provide a visual, real-time view of the impact of energy on all aspects of an enterprise A digital twin may be role-based, such as providing visual and analytic indicators that are suitable for the role of the user, such as financial reporting information for a Chief Financial Officer (CFO);operating parameter information for a power plant manager; and energy market information for an energy trader A CFO, by way of example, may need a visual representation highlighting the financial cost of energy consumption, like how shifting operations to off-peak hours impacts the energy cost In contrast, a power plant manager may be more interested in operational parameters, like the efficiency of the energy generation resources An energy trader, on the other hand, may want insights into the energy market, like tracking prices. Thus, by offering insights tailored to individual roles, the set of stakeholder energy digital twins 148 ensures that different stakeholders have the relevant information they need to make informed decisionsData Integrated Microservices

[0242] The configurable data and intelligence modules and services 118 may include a set of data integrated microservices 150, such as organized in a service-oriented architecture, such that various microservices can be grouped in series, in parallel, or in more complex flows to create higher-level, more complex services that each provide a defined set of outputs by processing a defined set of outputs, such as to enable a set of configured stakeholder energy edge solutions 108 or to facilitate Al-based orchestration, optimization and / or automation systems 114. The configurable data and intelligence modules and services 118 may, without limitation, be configured from various functions and capabilities of the set of intelligent data layers 130, which in turn operate on various data resources for energy edge orchestration 110 and / or internal event logs, outputs, data streams and the like of the platform 102FIGS. 2-8: ENERGY EDGE ECOSYSTEMDATA RESOURCES FOR ENERGY EDGE ORCHESTRATION

[0243] Referring to FIG. 2A, the data resources for energy edge orchestration 110 may include a set of edge and loT 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 handling capabilities involved in the transport of data over a network or communication system. This may include adapting one or more of these aspects of data handling based on data content (e.g., by packet inspection or other mechanisms for understanding the same), based on network conditions (e.g., congestion, delay s / latency, packet loss, error rates, cost of transport, quality of service (QoS), or the like), based on context of usage (e.g. , based on user, system, use case, application, or the like, including based on prioritization of the same), based on market factors (e.g. , price or cost factors), based on user configuration, or other factors, as well as based on various combinations of the same. For example, among many others, a least-cost route may be automatically selected for data that relates to management of a low-priority use of energy, such as heating a swimming pool, while a fastest or highest-QoS route may be selected for data that supports a prioritized use or energy, such as support of critical healthcare infrastructure.

[0244] Referring to FIG 2B, the platform 102 and orchestration may include, integrate, link to, integrate with, use, create, or otherwise handle, a wide range of data resources for the advanced energy resources and systems 104, the set of configured stakeholder energy edge solutions 108, and / or the energy edge orchestration 110. In some embodiments, 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 corresponding elements shown in Figure 1. The data resources may include separate databases, distributed databases, and / or federated data resources, among many others.Edge and loT Networking Systems

[0245] A wide range of energy-related data may be collected and processed (including by artificial intelligence services and other capabilities), and control instructions may be handled, by a set of edge and loT networking systems 160, such as ones integrated into devices, components or systems, ones located in loT devices and systems, ones located in edge devices and systems, or the like, such as where the foregoing are located in or around energy-related entities, such as ones used by consumers or enterprises, such as ones involv ed in energy generation, storage, delivery or use. These include any of the wide range of software, data and networking systems described herein.Public Data Resources

[0246] In some embodiments, the platform 102 may track public data resources 162, such as weather data. Weather conditions can impact energy use, particularly as they relate to HVAC systems Collecting, compiling, and analyzing weather data in connection with other building information allows building managers to be proactive about HVAC energy consumption. The public data resources 162 may include satellite data, demographic and psychographic data, population data, census data, market data, website data, ecommerce data, and many other types.Enterprise Data Resources

[0247] A set of enterprise data resources 168 may 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, operating data, and many others.ADVANCED ENERGY RESOURCES AND SYSTEMS

[0248] In some embodiments, the advanced energy resources and systems 104 may include distributed energy resources, or DERs 128. More decentralized energy resources will mean that more individuals, networked groups, and energy communities will be capable of generating and sharing their own energy and coordinating systems to achieve ultimate efficacy. The DER 128 may be a small- or medium-scale unit of power generation and / or storage that operates locally and may be connected to a larger power grid at the distribution level. For example, the DERs 128 may be either connected to the local electric power grid or isolated from the grid in stand-alone applications.Transformed Energy Infrastructure

[0249] The advanced energy resources and systems 104 orchestrated by the platform 102 may include a set of transformed energy infrastructure systems 120. The energy edge will involve increasing digitalization of generation, transmission, substation, and distribution assets, which in turn will shape the operations, maintenance, and expansion of legacy grid infrastructure. In some embodiments, a set of transformed energy infrastructure systems 120 may be integrated with or linked to the platform 102. The transition to improved infrastructure may include moving from SCADA systems and other existing control, automation, and monitoring systems to loT platforms with advanced capabilities

[0250] In some embodiments, new assets added to or coordinated with the grid (e.g. , DERs 128) may be compatible with existing infrastructure to maintain voltage, frequency, and phase synchronization. By way of example, consider a city that is incorporating renewable energy sources like wind turbines and solar panels (DERs 128) into its existing power grid These new assets need to integrate with tire older infrastructure to ensure consistent power delivery. This compatibility ensures that even as the city transitions to greener energy sources, residents experience no fluctuations in voltage, frequency, or phase synchronization, ensuring a stable power supply.

[0251] Any improvements to legacy grid assets, new grid-connected equipment, and supporting systems may. In some embodiments, comply with regulatory standards from NERC, FERC, NIST, and other relevant authorities;positively impact the reliability of the grid; reduce the susceptibility of the grid to cyberattacks and other security threats; increase the ability of the grid to adapt to extensive bi-directional flow of energy ( / .«., DER proliferation); and offer interoperability with technologies that improve the efficiency of the grid ( / .e., by providing and promoting demand response, reducing grid congestion, etc )

[0252] Digitalization of legacy grid assets may relate to assets used for generation, transmission, storage, distribution or the like, including power stations, substations, transmission wires, and others.

[0253] In some embodiments, in order to maintain and improve existing energy infrastructure, the platform 102 may include various capabilities, including fully integrated predictive maintenance across utility-owned assets (z'.e., generation, transmission, substations, and distribution); smart (Al / ML-based) outage detection and response; and / or smart (Al / ML-based) load forecasting, including optional integration of the DERs 128 with the existing grid. By way of example, consider a scenario where a utility company has a network of power generation and distribution assets, some of which are decades old. To ensure the longevity and efficiency of these assets, the platform 102 can offer predictive maintenance, alerting the utility company about potential issues before they become critical.

[0254] In some embodiments, power grid maintenance may be provided. With proactive maintenance, utilities can accurately detect defects and reduce unplanned outages to better serve customers. Al systems, deployed with loT and / or edge computing, can help monitor energy assets and reduce maintenance costs. By way of example, if a transmission line shows signs of wear and tear, the platform 102 can alert the utility company for timely repair. This proactive approach not only reduces unplanned outages but also reduce maintenance costs, leading to a more efficient and cost-effective power gridDigitized Resources

[0255] In some embodiments, the platform 102 may take advantage of the digital transformation of a wide range of digitized resources. Machines are becoming smarter, and software intelligence is being embedded into every aspect of a business, helping drive new levels of operational efficiency and innovation. Also, digital transformation is ongoing, involving increasing presence of smart devices and systems that are capable of data processing and communication, nearly ubiquitous sensors in edge, loT and other devices, and generation of large, dense streams of data, all of which provide opportunities for increased intelligence, automation, optimization, and agility, as information flows continuously between the physical and digital world. Such devices and systems demand large amounts of energy. Data centers, for example, consume massive amounts of energy, and edge and loT devices may be deployed in off-grid environments that require alternative forms of generation, storage, or mobility of energy. In some embodiments, a set of digitized resources may be integrated, accessed, or used for optimization of energy for compute, storage, and other resources in data centers and at the edge, among other places. In some embodiments, as more and more devices are embedded with sensors and controls, information can flow continuously between the physical and digital worlds as machines ‘talk’ to each other. Products can be tracked from source to customer, or while they are in use, enabling fast responses to internal and external changes Those tasked with managing or regulating such systems can gain detailed data from these devices to optimize the operation of the entire process This trend turns big data into smart data, enabling significant cost- and process efficiencies.

[0256] In some embodiments, advances in digital technologies enable a level of monitoring and operational performance that was not previously possible. Thanks to sensors and other smart assets, a service provider can collect a wide range of data across multiple parameters, monitoring in real-time, 24 hours a day.

[0257] In some embodiments, the DERs 128 will be integrated into computational networks and infrastructure devices and systems, augmenting the existing power grid and serving to decrease costs and improve reliability. For example, the platform 102 by integrating DERs 128, such as localized solar farms or wind turbines, into a city infrastructure can significantly augment the existing power grid By way of example, during peak demand times, rather than solely relying on traditional power plants, the platform 102 can enable energy management system of the city to utilize localized energy sources, which may, in turn, reduce the strain on the main grid and can also lead to substantial cost savings.Mobile Energy Resources

[0258] In some embodiments, DERs may be integrated into mobile energy resources 124, such as electric vehicles (EVs) and their charging networks / infrastructure, thereby augmenting the existing power grid and serving to decrease costs and improve reliability. Given the rise of EVs (of all types) charging infrastructure and vehicle charging plans will need to be optimized to match supply and demand. Also, growing electricity demand and development of EV infrastructure will require optimization using edge and other related technologies such as loT. Electric vehicle charging may be integrated into decentralized infrastructure and may even be used as the DER 128 by adding to the grid, such as through two-way charging stations, or by powering another system locally. Vehicle power electronic systems and batteries can benefit the power grid by providing system and grid services. Excess energy can be stored in the vehicles as needed and discharged when required. This flexibility option not only avoids expensive load peaks during times of short-term, high-energy demand but also increases the share of renewable energy use.

[0259] In some embodiments, in order to universally integrate electric vehicles and charging infrastructure into a distribution network, coordination with various other standardized communication protocols is needed. The platform 102 may include, integrate and / or link to a set of communication protocols that enable management, provisioning, governance, control or the like of energy edge devices and systems using such protocols Herein, the platform 102 can serve as a central hub, integrating various protocols, ensuring that when an EV docks at a charging station, the communication between the vehicle, the station, and the grid is smooth, efficient, and coordinated.DISTRIBUTED ENERGY GENERATION SYSTEMS

[0260] Referring to FIG 3, a distributed energy generation systems 302 may include wind turbines, solar photovoltaics (PV), flexible and / or floating solar systems, fuel cells, modular nuclear reactors, nuclear batteries, modular hydropower systems, microturbines and turbine arrays, reciprocating engines, combustion turbines, and cogeneration plants, among others. The distributed energy storage systems 304 may include battery storage energy (including chemical batteries and others), molten salt energy storage, electro-thermal energy storage (ETES), gravitybased storage, compressed fluid energy storage, pumped hydroelectric energy storage (PHES), and liquid air energy storage (LAES), among others. The distributed energy storage systems 304 may be managed by the platform 102. In some embodiments, the distributed energy storage systems 304 may be portable, such that units of energy may be transported to points of use, including points of use that are not connected to the conventional grid or ones where the conventional grid does not fully satisfy demand (e.g., where greater peak power, more reliable continuous power, or other capabilities are needed). Management may include the integration, coordination, and maximizing of retum-on- investment (ROI) on distributed energy resources (DERs), while providing reliability and flexibility for energy needs.

[0261] In some embodiments, the DERs 128 may use various distributed energy delivery methods and systems 308 having various energy delivery capabilities, including transmission lines (e. ., conventional grid and building infrastructure), wireless energy transmission (including by coupled, resonant transfer between high-Q resonators,near-field energy transfer and other methods), transportation of fluids, batteries, fuel cells, small nuclear systems, and the like), and others.

[0262] The mobile energy resources 124 include a wide range of resources for generation, storage, or delivery of energy at various scales; accordingly, the mobile energy resources 124 may comprise a subcategory of the DERs 128 that have attributes of mobility, such as where the mobile energy resources 124 are integrated into a vehicle 310 (e.g. , an electric vehicle, hybrid electric vehicle, hydrogen fuel cell vehicle, or the like, and in embodiments including a set of autonomous vehicles, which may be unmanned autonomous vehicles (UAV s), drones, or the like); where resources are integrated into or used by a mobile electronic device 312, or other mobile system; where the mobile energy resources 124 are portable resources 314 (including where they are removable and replaceable from a vehicle or other system), and the like. As the mobile energy resources 124 and supporting infrastructure (e.g., charging stations) scale in capacity and availability, orchestration of the mobile energy resources 124 and other DERs 128, optionally in coordination with available grid resources, takes on increased importance.

[0263] Resources involved in generation, storage, and transmission of energy are increasingly undergoing digital transformation. These digitized resources 122 may include smart resources 318 (such as smart devices (e.g., thermostats), smart home devices (e.g. , speakers), smart buildings, smart wearable devices and many others that are enabled with processors, network connectivity, intelligent agents, and other onboard intelligence features) where intelligence features of the smart resources 318 can be used for energy orchestration, optimization, autonomy, control or the like and / or used to supply data for artificial intelligence and analytics in connection with the foregoing. The digitized resources 122 may also include loT- and edge-digitized resources 320, where sensors or other data collectors (such as data collectors that monitor event logs, network packets, network traffic patterns, networked device location patterns, or other available data) provide additional energy-related intelligence, such as in connection with energy generation, storage, transmission or consumption by legacy infrastructure systems and devices ranging from large scale generators and transformers to consumer or business devices, appliances, and other systems that are in proximity to a set of loT or edge devices that can monitor the same. Thus, loT and edge device can provide digital information about energy states and flows for such devices and systems whether or not the devices and systems have onboard intelligence features; for example, among many others, an loT device can deploy a current sensor on a power line to an appliance to detect utilization patterns, or an edge networking device can detect whether another device or system connected to the device is in use (and in what state) by monitoring network traffic from the other device. The digitized resources 122 may also include cloud-aggregated resources 322 about energy generation, storage, transmission, or use, such as by aggregating data across a fleet of similar resources that are owned or operated by an enterprise, that are used in connection with a defined workflow or activity, or the like. The cloud-aggregated resources 322 may consume data from the various data resources, from crowdsourcing, from sensor data collection, from edge device data collection, and many other sources.

[0264] In some embodiments, the digitized resources 122 may be used for a wide range of uses that involve or benefit from real time information about the attributes, states, or flows of energy generation, storage, transmission, or consumption, including to enable digital twins, such as a set of adaptive energy digital twin systems 134 and / or the set of stakeholder energy digital twins 148 and for the set of configured stakeholder energy edge solutions 108. By way of example, a digital twin of public transport system in a city can predict energy needs based on commuter patterns, adjusting the operation of electric buses accordingly. Similarly, digital twins can be employed in various sectors, such as manufacturing units monitoring machinery energy consumption. Integration of the platform 102 withthese digital twins ensures that energy is always used optimally, adjusting to the real-time needs of the corresponding system.

[0265] Energy generation, storage, and consumption, particularly involving green or renewable energy, have been the subject of intensive research and development in recent decades, yielding higher peak power generation capacity, increases in storage capacity, reductions in size and weight, improvements in intelligence and autonomy, and many others The advanced energy resources and systems 104 may include a wide range of advanced energy infrastructure systems and devices that result from combinations of features and capabilities. In some embodiments, flexible hybrid energy systems 324 may be provided that is adaptable to meet varying energy consumption requirements, such as ones that can provide more than one kind of energy (e.g., solar or wind power) to meet baseline requirements of an off-grid operation, along with a nuclear battery to satisfy much higher peak power requirements, such as for temporary, resource intensive activities, such as operating a drill in a mine or running a large factory machine on a periodic basis. A wide variety of flexible hybrid energy systems 324 are contemplated herein, including ones that are configured for modular interconnection with various types of localized production infrastructure as described elsewhere herein. In some embodiments, the advanced energy resources and systems 104 may include advanced energy generation systems that draw power from fluid flows, such as portable turbine arrays 328 that can be transported to points of consumption that are in proximity to wind or water flows to substitute for or augment grid resources. The advanced energy resources and systems 104 may also include modular nuclear systems 330, including ones that are configured to use a nuclear battery and ones that are configured with mechanical, electrical and data interfaces to work with various consumption systems, including vehicles, localized production systems (as described elsewhere herein), smart buildings, and many others The modular nuclear systems 330 may include SMRs and other reactor types. The advanced energy resources and systems 104 may include advanced storage systems 332, including advanced batteries and fuel cells, including batteries with onboard intelligence for autonomous management, batteries with network connectivity for remote management, batteries with alternative chemistry (including green chemistry, such as nickel zinc), batteries made from alternative materials or structures (e.g., diamond batteries), batteries that incorporate generation capacity (e.g., nuclear batteries), advanced fuel cells (e.g., cathode layer fuels cells, alkaline fuel cells, polymer electrolyte fuel cells, solid oxide fuel cells, and many others).ENERGY EDGE DATA RESOURCES

[0266] Referring to FIG. 4, the data resources for energy edge orchestration 110 may include a wide range of public data sets, as well as private or proprietary data sets of an enterprise or individual. This may include data sets generated by or passed through the edge and loT networking systems 160, such as sensor data 402 (e.g. , from sensors integrated into or placed on machines or devices, sensors in wearable devices, and others); network data 404 (such as data on network traffic volume, latency, congestion, quality of sendee (QoS), packet loss, error rate, and the like); event data 408 (such as data from event logs of edge and loT devices, data from event logs of operating assets of an enterprise, event logs of wearable devices, event data detected by inspection of traffic on application programming interfaces, event streams published by devices and systems, user interface interaction events (such as captured by tracking clicks, eye tracking and the like), user behavioral events, transaction events (including financial transaction, database transactions and others), events within workflows (including directed, acyclic flows, iterative and / or looping flows, and the like), and others); state data 410 (such as data indicating historical, current or predicted / anticipated states of entities (such as machines, systems, devices, users, objects, individuals, and many others) and including a wide rangeof attributes and parameters relevant to energy generation, storage, delivery' or utilization of such entities); and / or combinations of tire foregoing (e.g., data indicating the state of an entity and of a workflow involving the entity).

[0267] In some embodiments, data resources may include, among many others, public data resources 162 that are relevant to energy, such as energy grid data 422 (such as historical, current and anticipated / predicted maintenance status, operating status, energy production status, capacity, efficiency, or other attribute of energy grid assets involved in generation, storage or transmission of energy); energy market data 424 (such as historical, current and anticipated / predicted pricing data for energy or energy-related entities, including spot market prices of energy based on location, type of consumption, type of generation and the like, day-ahead or other futures market pricing for the same, costs of fuel, cost of raw materials involved e.g., costs of materials used in battery production), costs of energy- related activities, such as mineral extraction, and many others); location and mobility data 428 (such as data indicating historical, current and / or anticipated / predicted locations or movements of groups of individuals (e.g., crowds attending large events, such as concerts, festivals, sporting events, conventions, and the like), data indicating historical, current and / or anticipated / predicted locations or movements of vehicles (such as used in transportation of people, goods, fuel, materials, and the like), data indicating historical, current and / or anticipated / predicted locations or movements of points of production and / or demand for resources, and others); and weather and climate data 430 (such as indicating historical, current and / or anticipated / predicted energy-relevant weather patterns, including temperature data, precipitation data, cloud cover data, humidity data, wind velocity data, wind direction data, storm data, barometric pressure data, and others).

[0268] In some embodiments, the data resources for energy edge orchestration 110 may include a set of enterprise data resources 168, which may include, among many others, energy-relevant financial and transactional data 432 (such as indicating historical, current and / or anticipated / predicted state, event, or workflow data involving financial entities, assets, and the like, such as data relating to prices and / or costs of energy and / or of goods and services, data related to transactions, data relating to valuation of assets, balance sheet data, accounting data, data relating to profits or losses, data relating to investments, interest rate data, data relating to debt and equity financing, capitalization data, and many others); operational data 434 (such as indicating historical, current and / or anticipated / predicted states or flows of operating entities, such as relating to operation of assets and systems used in production of goods and performance of services, relating to movement of individuals, devices, vehicles, machines and systems, relating to maintenance and repair operations, and many others); human resources data 438 (such as indicating historical, current and / or anticipated / predicted states, activities, locations or movements of enterprise personnel); and sales and marketing data 440 (such as indicating historical, current and / or anticipated / predicted states or activities of customers, advertising data, promotional data, loyalty program data, customer behavioral data, demand planning data, pricing data, and many others); and others.

[0269] In some embodiments, the data resources for energy edge orchestration 110 may be handled by an adaptive energy data pipeline 164, which may leverage artificial intelligence capabilities of the platform 102 in order to optimize the handling of the various data resources Increases in processing power and storage capacity of devices are combining with wider deployment of edge and loT devices to produce massive increases in the scale and granularity of data of available data of the many types described herein Accordingly, even more powerful networks like 5G, and anticipated 6G, are likely to have difficulty transmitting available volumes of data without problems of congestion, latency, errors, and reduced QoS The adaptive energy data pipeline 164 can include a set of artificial intelligence capabilities for adapting the pipeline of the data resources to enable more effective orchestration ofenergy-related activities, such as by optimizing various elements of data transmission in coordination with energy orchestration needs. In some embodiments, the adaptive energy data pipeline 164 may include self-organizing data storage 412 (such as storing data on a device or system (e.g., an edge, loT, or other networking device, cloud or data center system, on-premises system, or the like) based on the patterns or attributes of the data (e.g. , patterns in volume of data over time, or other metrics), the content of the data, the context of the data e.g., whether the data relates high- stakes enterprise activities), and the like) In some embodiments, the adaptive energy data pipeline 164 may include automated, adaptive networking 414 (such as adaptive routing based on network route conditions (including packet loss, error rates, QoS, congestion, cost / pricing and the like)), adaptive protocol selection (such as selecting among transport layer protocols (e.g., TCP or UDP) and others), adaptive routing based on RF conditions (e.g., adaptive selection among available RF networks (e.g., Bluetooth, Zigbee, NFC, and others)), adaptive filtering of data (e.g., DSP-based filtering of data based on recognition of whether a device is permitted to use RF capability), adaptive slicing of network bandwidth, adaptive use of cognitive and / or peer-to-peer network capacity, and others. In some embodiments, the adaptive energy data pipeline 164 may include enterprise contextual adaptation 418, such as where data is automatically processed based on context (such as operating context of an enterprise (e.g., distinguishing between mission-critical and less critical operations, distinguishing between time- sensitive and other operations, distinguishing between context required for compliance with policy or law, and the like), transactional or financial context (e.g., based on whether the data is required based on contractual requirements, based on whether the data is useful or necessary for real-time transactional or financial benefits (e.g., time-sensitive arbitrage opportunities or damage-mitigation needs)), and many others) In some embodiments, the adaptive energy data pipeline 164 may include market-based adaptation 420, such as where storage, networking, or other adaptation is based on historical, current and / or anticipated / predicted market factors (such as based on the cost of storage, transmission and / or processing of the data (including the cost of energy used for the same), the price, cost, and / or marginal profit of goods or sendees that are produced based on the data, and many others).

[0270] In some embodiments, the adaptive energy data pipeline 164 may adapt any and all aspects of data handling, including storage, routing, transmission, error correction, timing, security, extraction, transformation, loading, cleansing, normalization, filtering, compression, protocol selection (including physical layer, media access control layer and application layer protocol selection), encoding, decoding, and othersCONFIGURED ENERGY EDGE STAKEHOLDER SOLUTIONS

[0271] The set of configured stakeholder energy edge solutions 108 may include a set of mobility demand solutions 152, a set of enterprise optimization solutions 154, a set of energy provisioning and governance solutions 156, and / or a set of localized production solutions 158, among others, that use various advanced energy resources and systems 104 and / or various configurable data and intelligence modules and sendees 118 to enable benefits to particular stakeholders, such as private enterprises, non-governmental organizations, independent service organizations, governmental organizations, and others All such solutions may leverage edge intelligence, such as using data collected from onboard or integrated sensors, loT systems, and edge devices that are located in proximity to entities that generate, store, deliver and / or use energy to feed models, expert systems, analytic systems, data services, intelligent agents, robotic process automation systems, and other artificial intelligence systems into order to facilitate a solution for a particular stakeholder needs. By way of example, in the case of a city, the set of mobility demand solutions 152 can be utilized to predict peak travel times and adjust public transport schedules accordingly. Similarly, in case of a large corporate campus, the set of enterprise optimization solutions 154 can be utilized to manage itsenergy consumption, ensuring that office buildings are adequately powered during work hours while conserving energy during off-hours.Localized Production Solutions

[0272] In some embodiments, a set of localized production solutions 158 may be integrated with, linked to, or managed by the platform 102, such that localized production demand can be met, particularly for goods that are very costly to transport (e.g., food) or services where the cost of energy distribution has a large adverse impact on product or service margins (e.g., where there is a need for intensive computation in places where the electrical grid is absent, lacks capacity, is unreliable, or is too expensive ). The platform 102 can manage tire energy consumption of the set of localized production solutions 158, optimizing usage based on available resources, especially in places where the conventional electrical grid may be absent or unreliable.

[0273] In some embodiments, power management systems may converge with other systems, such as building management systems, operational management systems, production systems, services systems, data centers, and others to allow for enterprise- wide energy management. The platform 102 by converging power management with the building management systems, the operational management systems, the production systems, the services systems, the data centers, and the like, can ensure that energy is used optimally across the board in the enterprise. For example, during off-hours, while the building management system reduces lighting, the data center can shift its heavy' computations, balancing the overall energy load

[0274] Referring to FIG. 5, the platform 102 may orchestrate the various services and capabilities described in order to configure the set of configured stakeholder energy edge solutions 108, including the set of mobility demand solutions 152, the set of enterprise optimization solutions 154, energy provisioning and governance solutions 156, and a set of localized production solutions 158.

[0275] The set of localized production solutions 158 may include a set of computation intensive solutions 522 where the demand for energy involved in computation activities in a location is operationally significant, either in terms of overall energy usage or peak demand (particularly ones where location is a relevant factor in operations, but energy availability may not be assured in adequate capacity, at acceptable prices), such as data center operations (e.g, to support high-frequency trading operations that require low-latency and benefit from close proximity to the computational systems of marketplaces and exchanges), operations using quantum computation, operations using very large neural networks or computation- intensive artificial intelligence solutions (e.g., encoding and decoding systems used in cryptography), operations involving complex optimization solutions (e.g., high-dimensionality database operations, analytics and the like, such as route optimization in computer networks, behavioral targeting in marketing, route optimization in transportation), operations supporting cryptocurrencies (such as mining operations in cryptocurrencies that use proof-of-work or other computationally intensive approaches), operations where energy is sourced from local energy sources (e.g., hydropower dams, wind farms, and the like), and many others.

[0276] The set of localized production solutions 158 may include a set of transport cost mitigation solutions 524, such as ones where the cost of energy required to transport raw materials or finished goods to a point of sale or to a point of use is a significant component in overall cost of goods. The set of transport cost mitigation solutions 524 may configure a set of DERs 128 or other advanced energy resources to provide energy that either supplements or substitutes for conventional grid energy in order to allow localized production of goods that are conventionally produced remotely and transported by transportation and logistics networks (e.g. , long-haul trucking) to points of sale or use. For example, crops that have high water content can be produced locally, such as in containers that areequipped with lighting systems, hydration systems, and the like in order to shift the energy mix toward production of the crops, rather than transportation of the finished goods. The platform 102 may be used to optimize, at a fleet level, the mix of a set of localized, modular energy generation systems or storage systems to support a set of localized production systems for heavy goods, such as by rotating the energy generation or storage systems among the localized production systems to meet demand {e.g. , seasonal demand, demand based on crop cycles, demand based on market cycles and the like).

[0277] The set of localized production solutions 158 may include a set of remote production operation solutions 528, such as to orchestrate DERs 128 or other advanced energy resources to provide energy in a more optimal way to remote operations, such as mineral mining operations, energy exploration operations, drilling operations, military operations, firefighting and other disaster response operations, forestry operations, and others where localized energy demand at given points of time periodically exceeds what can be provided by the energy grid, or where the energy grid is not available. This may include orchestration of the routing and provisioning of a fleet of portable energy storage systems {e.g. , vehicles, batteries, and others), tire routing and provisioning of a fleet of portable renewable energy generation systems (wind, solar, nuclear, hydropower and others), and the routing and provisioning of fuels {e.g., fuel cells).

[0278] The set of localized production solutions 158 may include a set of flexible and variable production solutions 530, such as where a set of production assets {e.g., 3D printers, CNC machines, reactors, fabrication systems, conveyors and other components) are configured to interface with a set of modular energy production systems, such as to accept a combination of energy from the grid and from a localized energy generation or storage source, and where the energy storage and generation systems are configured to be modular, removable, and portable among the production assets in order to provide grid augmentation or substitution at a fleet level, without requiring a dedicated energy asset for each production asset. The platform 102 may be used to configure and orchestrate the set of energy assets and the set of production assets in order to optimize localized production, including based on various factors noted herein, such as marketplace conditions in the energy market and in the market for the goods and services of an enterprise.Enterprise Optimization Solutions

[0279] The set of configured stakeholder energy edge solutions 108 may also include a set of enterprise optimization solutions 154, such as to provide an enterprise with greater visibility into the role that energy plays in enterprise operations (such as to enable targeted, strategic investment in energy-relevant assets); greater agility in configuring operations and transactions to meet operational and financial objectives that arc driven at least in part by energy availability energy market prices or the like; improved governance and control over energy-related factors, such as carbon production, waste heat and pollution emissions; and improved efficiency in use of energy at any and all scales of use, ranging from electronic devices and smart buildings to factories and energy extraction activities. The term “enterprise,” as used herein, may, except where context requires otherwise, include private and public enterprises, including corporations, limited liability companies, partnerships, proprietorships and the like, non-governmental organizations, for-profit organizations, non-profit organizations, public-private partnerships, military organizations, first responder organizations (police, fire departments, emergency medical services and the like), priv ti te and public educational entities (schools, colleges, universities and others), governmental entities (municipal, county, state, provincial, regional, federal, national and international), agencies (local, state, federal, national and international, cooperative {e.g., treaty-based agencies), regulatory, environmental, energy, defense, civil rights, educational, andmany others), and others Examples provided in connection with a for-profit business should be understood to apply to other enterprises, and vice versa, except where context precludes such applicability.

[0280] The set of enterprise optimization solutions 154 may include a set of smart building solutions 512, where the platform 102 may be used to orchestrate energy generation, transmission, storage and / or consumption across a set of buildings owned or operated by the enterprise, such as by aggregating energy purchasing transactions across a fleet of smart buildings, providing a set of shared mobile or portable energy units across a fleet of smart buildings that are provisioned based on contextual factors, such as utilization requirements, weather, market prices and the like at each of the buildings, and many others.

[0281] The set of enterprise optimization solutions 154 may include a set of smart energy delivery solutions 514, where the platform 102 may be used to orchestrate delivery or energy at a favorable cost and at a favorable time to a point of operational use. In some embodiments, the platform 102 may, for example, be used to time the routing of liquid fuel through elements of a pipeline by automatically controlling switching points of the pipeline based on contextual factors, such as operational utilization requirements, regulatory requirements, market prices, and the like. In other embodiments, the platform 102 may be used to orchestrate routing of portable energy storage units or portable energy generation units in order to deliver energy to augment or substitute for grid energy capacity at a point and time of operational use. In some embodiments, the platform 102 may be used to orchestrate routing and delivery' of wireless power to deliver energy to a point and time of use. Energy delivery optimization may be based on market prices (historical, current, futures market, and / or predicted), based on operational conditions (current and predicted), based on policies (e.g., dictating priority for certain uses) and many other factors

[0282] The set of enterprise optimization solutions 154 may include a set of smart energy transaction solutions 518, where the platform 102 may be used to orchestrate transactions in energy or energy-related entities (e.g., renewable energy credits (RECs), pollution abatement credits, carbon-reduction credits, or the like) across a fleet of enterprise assets and / or operations, such as to optimize energy purchases and sales in coordination with energy-relevant operations at any and all scales of energy usage. This may include, In some embodiments, aggregating and timing current and futures market energy purchases across assets and operations, automatically configuring purchases of shared generation, storage or delivery capacity for enterprise operational usage and the like The platform 102 may leverage blockchain, smart contract, and artificial intelligence capabilities, trained as described throughout this disclosure, to undertake such activities based on the operational needs, strategic objectives, and contextual factors of an enterprise, as well as external contextual factors, such as market needs. For example, an anticipated need for energy by an enterprise machine may be provided as an event stream to a smart contract, which may automatically secure a future energy delivery contract to meet the need, either by purchasing grid-based energy from a provider or by ordering a portable energy storage unit, among other possibilities. The smart contract may be configured with intelligence, such as to time the purchase based on a predicted market price, which may be predicated, such as by an intelligent agent, based on historical market prices and current contextual factors

[0283] The set of enterprise optimization solutions 154 may include a set of enterprise energy digital twin solutions 520, where the platform 102 may be used to collect, monitor, store, process and represent in a digital twin a wide range of data representing states, conditions, operating parameters, events, workflows and other attributes of energyrelevant entities, such as assets of the enterprise involved in operations, assets of external entities that are relevant to the energy utilization or transactions of the enterprise (e.g., energy grid entities, pipelines, charging locations, and the like), energy market entities e.g., counterparties, smart contracts, blockchains, prices and the like). A user of the setof enterprise energy digital twin solutions 520 may, for example, view a set of factories that are consuming energy and be presented with a view that indicates the relative efficiency of each factory', of individual machines within the factory, or of components of the machines, such as to identify inefficient assets or components that should be replaced because the cost of replacement would be rapidly recouped by reduced energy usage The digital twin, in such example, may provide a visual indicator of inefficient assets, such as a red flag, may provide an ordered list of the assets most benefiting from replacement, may provide a recommendation that can be accepted by the user (e.g., triggering an order for replacement), or the like. Digital twins may be role-based, adaptive based on context or market conditions, personalized, augmented by artificial intelligence, and the like, in the many ways described herein and in the documents incorporated by reference herein.

[0284] In some embodiments, the DERs 128 will be integrated with or into enterprises and shared resources, augmenting the existing power grid and serving to decrease costs and improve reliability. Increasing levels of digitalization will help integrate activities and facilitate new ways of optimizing energy in buildings / operations, and across campuses and enterprises. By way of example, by integrating the DERs 128, the campus can supplement its power needs with renewable sources. Digitalization of energy management can help the campus monitor and adjust its energy consumption in real-time. In some embodiments, this may enable increasing the operational bottom line of a for-profit enterprise by leveraging big data and plug load analytics to efficiently manage buildings. For example, the campus can manage its buildings efficiently, ensuring that energy is used where needed, optimizing operational costs.

[0285] In some embodiments, loT sensors and building automation control systems may be configured to assist in optimizing floor space, identifying unused equipment, automating efficient energy consumption, improving safety, and reducing environmental impact of buildings. By way of example, in a multi-storied office building equipped with loT sensors and building automation control systems, these systems can monitor energy consumption of each floor, ensuring that lighting and HVAC systems are optimized for the number of occupants. In an example, unused conference rooms can automatically switch off lights and adjust temperatures, reducing energy wastage.

[0286] In some embodiments, the platform 102 may manage total energy consumption of systems and equipment connected to the electrical network or to a set of DERs 128. Some systems are almost always operational, while other pieces of equipment and machinery may be connected only occasionally. By maintaining an understanding of both the total daily electrical consumption of a building and the role individual devices play in Hie overall energy use of a specific system, the platform 102 may forecast, provision, manage and control, optionally by Al or algorithm, the total consumption. For example, the platform 102, through Al and algorithms, can monitor and adjust energy consumption based on the specific needs of each building, optimizing energy use.

[0287] In some embodiments, the platform 102 may track and leverage an understanding of occupants’ behavior. Activity levels, behavior patterns, and comfort preferences of occupants may be a consideration for energy efficiency measures. This may include tracking various cyclical or seasonal factors. Over time, energy generation, storage and / or consumption of a building may follow predictable patterns that an loT-based analytics platform can take into consideration when generating proposed solutions. By way of example, during winter, if the platform notices residents tend to stay in during evenings, it can adjust heating accordingly. Over time, the system learns from these patterns, ensuring energy is used efficiently.

[0288] In some embodiments, the platform 102 may enable or integrate with systems or platforms for autonomous operations. For example, industrial sites, such as oil rigs and power plants, require extensive monitoring for efficiencyand safety because liquid, steam, or oil leakages can be catastrophic, costly, and wasteful. Al and machine learning may provide autonomous capabilities for power plants, such as those served by edge devices, loT devices, and onsite cameras and sensors. Models may be deployed at the edge in power plants or on DERs 128, such as to use real-time inferencing and pattern detection to identify faults, such as leaks, shaking, stress, or the like Operators may use computer vision, deep learning, and intelligent video analytics (IVA) to monitor heavy machinery, detect potential hazards, and alert workers in real-time to protect their health and safety, prevent accidents, and assign repair technicians for maintenance. By way of example, in a factory with multiple machines, the platform 102, through Al and machine learning, can monitor the health of the machines in real-time, predicting potential weak points, and suggesting timely maintenance and repair.

[0289] In some embodiments, the platform 102 may enable or integrate with systems or platforms for pipeline optimization. For example, oil and gas enterprises may rely on finding the best-fit routes to transfer oil to refineries and eventually to fuel stations. Edge Al can calculate the optimal flow of oil to ensure reliability of production and protect long-term pipeline health. In some embodiments, enterprises can inspect pipelines for defects that can lead to dangerous failures and automatically alert pipeline operators.Mobility Demand Solutions

[0290] Referring still to FIG. 5, the set of configured stakeholder energy edge solutions 108 may include a set of mobility' demand solutions 152, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of mobile entities, such as a fleet of vehicles, a set of individuals, a set of mobile event production units, or a set of mobile factory units, among many others

[0291] The set of mobility demand solutions 510 may include a set of transportation solutions 502, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of vehicles, such as used to transport goods, passengers, or the like. The platform 102 may handle relevant operational and contextual data, such as indicating needs, priorities, and the like for transportation, as well as relevant energy data, such as the cost of energy used to transport entities using different modes of transportation at different points in time, and may provide a set of recommendations, or automated provisioning, of transportation in order to optimize transportation operations while accounting fully for energy costs and prices. For example, among many others, an electric or hybrid passenger tour bus may be automatically routed to a scenic location that is in proximity to a low cost, renewable energy charging station, so that the bus can be recharged while tire tourists experience the location, thus satisfying an energy-related objective (cost reduction) and an operational objective (customer satisfaction). An intelligent agent may be trained, using techniques described herein and in the documents incorporated by reference (such as by training robotic process automation on a training set of expert interactions), to provide a set of recommendations for optimizing energy-related objectives and other operational objectives.

[0292] The set of mobility demand solutions 510 may include a set of mobile user solutions 504, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of mobile users, such as users of mobile devices. For example, in anticipation of a large, temporary increase in the number of people at a location (such as in a small city hosting a major sporting event), the platform 102 may provide a set of recommendations for, or automatically configure a set of orders for a set of portable recharging units to support charging of consumer devices.

[0293] The set of mobility demand solutions 510 may include a set of mobile event production solutions 508, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by orfor a set of mobile entities involved in production of an event, such as a concert, sporting event, convention, circus, fair, revival, graduation ceremony, college reunion, festival, or the like. This may include automatically configuring a set of energy generation, storage or delivery units based on the operational configuration of the event e.g., to meet needs for lighting, food service, transportation, loudspeakers and other audio-visual elements, machines (e.g., 3D printers, video gaming machines, and the like), rides and others), automatically configuring such operational configuration based on energy capabilities, configuring one or more of energy or operational factors based on contextual factors e.g., market prices, demographic factors of attendees, or the like), and the like.

[0294] The set of mobility demand solutions 510 may include a set of mobile factory solutions, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of mobile factory entities. These may include container-based factories, such as where a 3D printer, CNC machine, closed-environment agriculture system, semiconductor fabricator, gene editing machine, biological or chemical reactor, furnace, or other factory machine is integrated into or otherwise contained in a shipping container or other mobile factory housing, wherein the platform 102 may, based on a set of operational needs of the set of factory machines, configure a set of recommendations or instructions to provision energy generation, storage, or delivery to meet the operational needs of the set of factory' machine at a set of times and places The configuration may be based on energy factors, operational factors, and / or contextual factors, such as market prices of goods and energy, needs of a population (such as disaster recovery needs), and many other factors.Energy Provisioning and Governance Solutions

[0295] The energy provisioning and governance solutions 156 may include solutions for governance of mining operations. Cobalt, nickel, and other metals are fundamental components of the batteries that will be needed for the green EV revolution. Amounts required to support the growing market will create economic pressure on mining operations, many of which take place in regions like the DRC where there is a long history of corruption, child labor, and violence. Companies are exploring areas like Greenland for cobalt, in part on the basis that it can offer reliable labor law enforcement, taxation compliance, and the like. Such promises can be made there and in other jurisdictions with greater reliability through a set of mining governance solutions 542. The set of mining governance solutions 542 may include mine-level loT sensing of the mine environment, ground-penetrating sensing of unmined portions, mass spectrometry and computer vision-based sensing of mined materials, asset tagging of smart containers (e.g. , detecting and recording opening and closing events to ensure that the material placed in a container is the same material delivered at the end point), wearable devices for detecting physiological status of miners, secure (e. ., blockchain- and DLT-bascd) recording and resolution of transactions and transaction-related events, smart contracts for automatically allocating proceeds (e.g., to tax authorities, to workers, and the like), and an automated system for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements. All of the above, from base sensors to compliance reports can be optionally represented in a digital twin that represents each mine owner or operated by an enterprise

[0296] The energy provisioning and governance solutions 156 may also include a set of carbon-aware energy solutions, where controls for operating entities that generate (or capture) carbon are managed by data collection through edge and loT devices about current carbon generation or emission status and by automated generation of a set of recommendations and or control instructions to govern the operating entities to satisfy policies, such as by keeping operations within a range that is offset by available carbon offset credits, or the like.

[0297] Referring still to FIG. 5, the set of configured stakeholder energy edge solutions 108 may include a set of energy provisioning and governance solutions 156, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of entities based on a set of policies, regulations, laws, or the like, such as to facilitate compliance with company financial control policies, government or company policies on carbon reduction, and many others.

[0298] The set of energy provisioning and governance solutions 156 may include a set of carbon-aware energy edge solutions 532, such as where a set of policies regarding carbon generation may be explored, configured, and implemented in the platform 102, such as to require energy production by one or more assets or operations to be monitored in order to track carbon generation or emissions, to require offsetting of such generation or emissions, or the like. In some embodiments, energy generation control instructions (such as for a machine or set of machines! may be configured with embedded policy instructions, such as required confirmation of available offsets before a machine is permitted to generate energy (and carbon), or before a machine can exceed a given amount of production in a given period. In some embodiments, the embedded policy instructions may include a set of override provisions that enable the policy to be overridden (such as by a user, or based on contextual factors, such as a declared state of emergency) for mission critical or emergency operations. Carbon generation, reduction and offsets may be optimized across operations and assets of an enterprise, such as by an intelligent agent trained in various ways as described elsewhere in this disclosure.

[0299] The set of energy provisioning and governance solutions 156 may include a set of automated energy policy deployment solutions 534, such as where a user may interact with a user interface to design, develop or configure (such as by entering rules or parameters) a set of policies relating to energy generation, storage, deliver;' and / or utilization, which may be handled by the platform, such as by presenting the policies to users who interact with entities that are subject to the policies (such as interfaces of such entities and / or digital twins of such entities, such as to provide alerts as to actions that risk noncompliance, to log noncompliant events, to recommend alternative, compliance options, and the like), by embedding the policies in control systems of entities that generate, store, deliver or use energy (such that operations of such entities are controlled in a manner that is compliant with the policies), by embedding the policies in smart contracts that enable energy-related transactions (such that transactions are automatically executed in compliance with the policies, such that warnings or alerts are provided in the case of non- compliance, or the like), by setting policies that are automatically reconfigured based on contextual factors (such as operational and / or market factors) and others. In some embodiments, an intelligent agent may be trained, such as on a training data set of historical data, on feedback from outcomes, and / or on a training data set of human policy-setting interactions, to generate policies, to configure or modify policies, and / or to undertake actions based on policies A wide range of policies and configurations may be implemented, such as setting maximum energy usage for an entity for a time period, setting maximum energy cost for an entity for a time period, setting maximum carbon production for an entity for a time period, setting maximum pollution emissions for an entity for a time period, setting carbon offset requirements, setting renewable energy credit requirements, setting energy mix requirements (e.g., requiring a minimum fraction of renewable energy), setting profit margin minimums based on energy and other marginal costs for a production entity, setting minimum storage baselines for energy storage entities (such as to provide a margin of safety for disaster recovery), and many others

[0300] The set of energy provisioning and governance solutions 156 may include a set of energy governance smart contract solutions 538, such as to allow a user of the platform 102 to design, generate, configure and / or deploy a smartcontract that automatically provides a degree of governance of a set of energy transactions, such as where the smart contract takes a set of operational, market or other contextual inputs (such as energy utilization information collected by edge devices about operating assets) as inputs and automatically configures a set of contracts that are compliance with a set of policies for the purchase, sale, reservation, sharing, or other transaction for energy, energy-related credits, and the like. For example, a smart contract may automatically aggregate carbon offset credits needed to balance carbon generation detected across a set of machines used in enterprise operations.

[0301] The set of energy provisioning and governance solutions 156 may include a set of automated energy financial control solutions 540, such as to allow a user of the platform 102 and / or an intelligent agent to design, generate, configure, or deploy a policy related to control of financial factors related to energy generation, storage, delivery and / or utilization. For example, a user may set a policy requiring minimum marginal profit for a machine to continue operation, and the policy may be presented to an operator of the machine, to a manager, or the like. As another example, the policy may be embedded in a control system for the machine that takes a set of inputs needed to determine marginal profitability (e.g., cost of inputs and other non-energy resources used in production, cost of energy, predicted energy required to produce outputs, and market price of outputs) and automatically determines whether to continue production, and at what level, in order to maintain marginal profitability. Such a policy may take further inputs, such as relating to anticipated market and customer behavior, such as based on elasticity of demand for relevant outputs.

[0302] In some embodiments, an automated energy and governance policy may refer to a policy that to which an underlying system must adhere In other words, the automated energy and governance policy is like a rulebook that a system strictly follows In some embodiments, a set of edge devices may enforce the energy policies for a set of “downstream devices” (which is any device that uses power in the edge devices covered area). By way of example, in a smart city grid, the automated energy and governance policy may be utilized to ensure that streetlights operate within certain energy constraints. Edge devices, as part of the platform 102, which may include energy-efficient controllers, may be tasked with ensuring these energy policies for other devices connected to them. In an example, during festive seasons when there are additional decorative lights in use, these edge devices can enforce energy policies, ensuring that the overall energy consumption of all lights (including the additional decorative lights, i. e. , the "downstream devices") does not cross a predefined limit.

[0303] In some embodiments, an energy policy may define an upper limit of “carbon creation”, meaning that individual devices or the collection of downstream devices may not exceed a total carbon footprint over a given time. By way of example, for a corporation aiming for carbon neutrality, an energy policy may be set to ensure that their buildings or factories don't exceed a certain carbon footprint. In an example, a company may have a policy stating that its operations do not create more than a specific tonnage of carbon emissions in a year. This ensures that the activities of the company remain environmentally sustainable, even as it scales up its operations.

[0304] In some embodiments, energy delivery mechanisms may include “energy source” metadata indicating how the energy being delivered was generated and a measure of carbon output per “unit-of-usage” Thereby, if energy was generated by wind, solar, nuclear, etc., the carbon footprint per unit of usage would be zero or close to zero, but if it was coal, natural gas, gas, etc., it would have a non-zero factor. Consider an industrial plant powered by a mix of renewable and non-renewable energy sources. The energy delivered to the plant may come with metadata indicating its origin. If the energy was predominantly generated through green sources like wind or solar, the associated carbon footprint would be low. However, if a significant portion was from coal or natural gas, the footprint would be higher.In these cases, the power may be delivered in portable storage or wired storage. If wired storage with mixed grid, the energy source metadata may indicate the overall percentage of energy from each power source feeding into the grid (e.g. , 20% renewable, 50% nuclear, 10% coal), such that the carbon output per unit of usage parameter may be derived from the respective percentages Overall, this metadata can be especially useful for businesses operating in regions with mixed energy grids, helping them calculate their actual carbon impact.

[0305] In some embodiments, the edge device may monitor the amount of power being used by the set of downstream devices and may determine the carbon output based on the energy source metadata and carbon output rate associated with the energy source metadata. When the edge device determines that the set of downstream devices is approaching the policy limit, the energy and governance engine may take a set of preventative actions to avoid hitting the upper limit. Examples of preventative actions may include switching to a different energy delivery mechanism (which may be more expensive or less optimal in other ways), shutting down certain devices to reduce the energy spend, toggling energy usage between different devices, sending alerts to human users, or the like. When the edge device determines the set of downstream devices exceeded the upper limit, the energy and governance engine may take a set of corrective actions to avoid hitting the upper limit. The corrective actions may include one or more of buying carbon offset credits, turning off the system, and switching to a carbon neutral operating mode. By way of example, in a residential community powered by multiple energy sources, an edge device may monitor energy consumption of households and determine their carbon output. If the residential community is approaching its carbon limit due to excessive use of non-renewable energy, the platform 102 may shift more households in the residential community to solar power, despite potential added costs

[0306] In some embodiments, management of the reliability and uptime from energy edge components may be critical parts of overall operation of a distributed edge environment. Like for any business, ensuring that its operations are uninterrupted is crucial. This is especially true for sectors like healthcare or data centers, where energy reliability directly impacts human lives or vital data. Therefore, maintaining the reliability and uptime of energy edge components becomes a non-negotiable aspect of their operations. The platform 102 is configured to ensure to identify such operations, and ensure that their operations are uninterrupted, such as, by diverting energy from other sources if needed.

[0307] In some embodiments, the platform 102 may be configured to provide and / or facilitate artificial general intelligence ( AGI (-based governance of energy resources. The platform 102 may include one or more AGI agents configured to make decisions and interact with one or more of humans, other AGI agents, and components of the platform 102. The one or more AGI agents may be configured to make decisions based on an internal state of the one or more AGI agents. The platform 102 may be configured to create snapshots of the internal state of the one or more AGI agents, the snapshot being associated with decisions made by the one or more AGI agents. The platform 102 may be configured to analyze and / or monitor the snapshots to improve management and / or governance of energy resources.

[0308] In some embodiments, the platform 102 may be configured to monitor decisions of components of the platform 102 to perform, provide, and / or facilitate continuous and / or near-continuous correction and / or microadjustment of the components to align with strategic goals of the platform 102. By way of example, in large-scale energy projects, it is essential to ensure that all components work towards the strategic goals of the project. By continuously monitoring decisions of these components, the platform 102 can realign any deviations, ensuring that the entire system works in harmony.

[0309] In some embodiments, the platform 102 may be configured to detect bad actors. With increasing cyber threats, the ability of the platform 102 to detect bad actors becomes important. The platform 102 may be configured to perform one or more actions in response to detection of a bad actor. By way of example, if someone tries to manipulate the energy consumption data of a smart grid to gain undue advantages, the platform 102 can detect such anomalies and take corrective actions, like blocking of such manipulating agents, raising flags, etc.

[0310] In some embodiments, the platform 102 may be configured to track and monitor human interaction with components of the platform 102 and related edge devices and / or energy devices. The platform 102 may track and monitor human interaction to evaluate consistency of decisions of distributed agents, thereby encouraging that decisions made by the platform 102 and components thereof are consistent across a plurality of distributed energy resources. The platform 102 may be configured to additionally, or alternatively, track and monitor one or more of decision- making about resource allocation by components of the platform 102, management of supply and demand of energy resources, and responses to changes in an environment and / or market. By way of example, in a scenario where a human operator regularly interacts with an energy management system in a factory, by tracking these interactions, the platform 102 can determine the consistency of decisions made by different agents, ensuring a harmonized approach across the factory'. Such tracking may, particularly, be useful for factories with multiple shifts, ensuring that energy decisions are consistent, regardless of the operating personnel.

[0311] In some embodiments, the platform 102 may be configured to provide and / or facilitate detection and prevention of harm to wildlife by energy infrastructure. Infrastructure development often comes at an environmental cost For energy projects located near forests or water bodies, there is a risk of harming wildlife The platform 102 may be configured to detect any potential threats to wildlife due to the infrastructure, like birds flying into wind turbines or aquatic life being affected by hydropower plants, and take preventive actions.

[0312] In some embodiments, the platform 102 may be configured to gather data related to patterns of wildlife and use the wildlife pattern data to perform optimization of energy generation and distribution. By way of example, in wind farms located near habitats of migratory birds, the platform 102 can analyze data related to birds’ movement patterns. By understanding these patterns, it can optimize energy generation schedules, reducing the risk of bird collisions with the blades of the wind turbine, which ultimately may also reduce infrastructure damage. By way of example, in extreme cases, during peak migration periods, the platform 102 can stop the operations of wind turbines directly in path of movement of birds to minimize bird impacts.

[0313] In some embodiments, the platform 102 may be configured to determine and / or manage energy needs related to space travel. The platform 102 may perform and / or provide improvements to power generation, storage, and distribution during space missions based on the determined energy needs Space missions, like the Mars rovers, require precise energy management. The platform 102 can monitor solar panel efficiencies, battery' storage levels, and energy consumption rates in such rovers. By way of example, during periods when there is no sunlight, the platform 102 can help optimize energy consumption ensuring essential systems remain functional.

[0314] In some embodiments, the platform 102 may be configured to receive data from and / or transmit energy- related data to one or more satellites. The platform 102 may improve operation of one or more systems of components based on data received from the one or more satellites. By way of example, weather satellites provide crucial data that impacts energy generation, especially for renewables (like cloud cover over an area which can impact solar energy generation). By receiving data from these satellites, the platform 102 can forecast cloud cover, aiding solar farms to predict energy generation dips and adjust their distribution strategies accordingly.

[0315] In some embodiments, the platform 102 may be configured to use data received from the one or more satellites to perform and / or improve one or more of monitoring energy usage, predicting energy demand, and allocating energy resources. For example, satellite data can also be invaluable for energy management. By way of example, by analyzing cloud movement patterns from satellites, the platform 102 can anticipate when solar farms in a region may experience reduced sunlight and adjust energy distribution from other sources.

[0316] In some embodiments, the platform 102 may be configured to plan and / or manage energy needs and resources related to asteroid mining operations. Asteroid mining is being explored as a future method to extract rare minerals The platform 102 may consider energy requirements of extraction and / or transportation operations of the asteroid mining operations. In such operations, the platform 102 can manage energy for mineral extraction (like operating various tools for mining operation) and transportation (like propulsion). By way of example, when extracting minerals from an asteroid bound for Earth, the platform 102 can optimize energy use for both the extraction process and subsequent transportation of the extracted minerals back.

[0317] In some embodiments, the platform 102 may be configured to manage and / or track disposal of radioactive waste generated by nuclear power plants. The platform 102 may ensure safety and compliance with international standards and regulations. Herein, the platform 102 can track waste quantities, monitor storage conditions, and ensure that disposal methods are compliant with international standards By way of example, after fuel of a reactor is spent, the platform 102 can monitor the cooling process to ensure safety of such cooling operation, and subsequent safe storage of the spent fuel.

[0318] In some embodiments, the platform 102 may be configured to optimize solar power generation via advanced analytics The platform 102 may ensure maximum efficiency and reliability of solar power plants and distributed solar energy resources For example, in case of solar energy, solar power plants and solar installations have become increasingly complex. To ensure their peak performance, the platform 102 can utilize advanced analytics to analyze the operational data of these systems. By doing so, the platform 102 can provide insights into panel efficiency, dirt accumulation, etc. By way of example, using the platform 102, operators can predict which panels may need maintenance, determine optimal panel angles based on the position of the sun, and even predict energy generation based on weather forecasts.

[0319] In some embodiments, the platform 102 may be configured to anticipate and respond to threats from hostile nation states, such as cyberattacks targeting energy grids and / or sabotage of energy resources. In an era of increasing cyber warfare, energy grids are potential targets. The platform 102 can monitor for unusual patterns for detecting cyber intrusions, ensuring that energy resources remain secure. By way of example, during a sudden grid shutdown, the platform 102 can identify if it is a technical failure or a cyberattack.

[0320] In some embodiments, the platform 102 may be configured to plan and / or manage energy needs related to land mine cleanup operations. The platform 102 may consider energy required for detection, extraction, and / or safe disposal of land mines. Land mine cleanup is a dangerous and energy-intensive operation. The platform 102 can manage energy needs for detection robots, ensuring they operate efficiently By way of example, during a land mine detection operation in a large field, the platform 102 can optimize robot paths to minimize energy consumption.

[0321] In some embodiments, the platform 102 may be configured to address legal and / or ethical implications of decisions made by the platform 102. The platform 102 may ensure compliance with laws and regulations, and / or may implement safeguards to prevent harm related to operation of the platform 102. The platform 102, withits Al systems, can make decisions impacting human lives. The platform 102 is configured to cross-check every decision with legaland ethical guidelines, ensuring that it does not even inadvertently cause harm. By way of example, in case of power shortage, before shutting off power to a critical facility, the platform 102 can assess the human impact, and may accordingly decide not to take such step and may trv to divert power from other sources, and the like.

[0322] In some embodiments, the platform 102 may be configured to manage data storage in compliance with regulatory requirements, thereby ensuring data privacy and security Data storage, especially in the energy sector, involves a plethora of user-specific information that can be both sensitive and crucial for operations. Particularly, in regions with strict data regulations, like the EU with its GDPR, the platform 102 ensures that all stored energy consumption data complies with local regulations, safeguarding user privacy. Using the platform 102, this data can be stored with advanced encryption standards, and only be accessed when necessary.

[0323] In some embodiments, the platform 102 may be configured to manage and respect requests from individual and / or groups of individuals for data anonymity in accordance with data privacy and protection laws. As energy consumption data becomes more granular, and with smart home devices, it may become increasingly possible to understand behaviors of humans by analyzing his / her energy usage patterns. Considering that, individuals may demand that their data be anonymized. The platform 102 can ensure that individual energy consumption patterns aren't traceable back to specific users, adhering to privacy norms.

[0324] In some embodiments, the platform 102 may be configured to manage and / or address scenarios in which Al entities and / or robotic entities may request anonymity. By way of example, a business employing Al entities for providing energy management support (like a chatbot) for its users may wish not to let their user know about the use of Al; in such case, the Al entities may send an anonymity request to the platform 102 in its interactions, and the platform 102 may be configured to ensure that its identity remains protected

[0325] In some embodiments, the platform 102 may store data related to DNA and perform handling of the DNA data in accordance with laws and regulations. By way of example, the platform 102 can store DNA data related to bio-energy projects, ensuring that this sensitive data is handled ethically and legally In an example, with the platform 102, research institutions can store DNA sequences of algae species being used for biofuel production. This data can then be accessed and analyzed to determine which species produced the most biofuel under specific conditions, all while ensuring the sensitive genetic data remains protected.

[0326] In some embodiments, the platform 102 may be configured to interact with bank systems to manage financial transactions related to energy trading. The platform 102 may ensure secure and / or efficient energy trading operations. With the growth of energy trading, the platform 102 can act as a bridge between energy producers, traders, and consumers. The platform 102 can integrate with banking systems to streamline financial transactions By way of example, during an energy trade between two businesses, the platform 102 can manage the financial aspects, ensuring swift and secure payments.

[0327] In some embodiments, the platform 102 may be configured to perform automated marketing operations. The platform 102 may provide and / or facilitate one or more of personalized customer engagement, predictive analytics related to marketing operations, and optimization of marketing campaigns For example, the platform 102 can use energy consumption data to tailor marketing campaigns. In an example, if a region has high solar energy potential (say, for example, due to all-seasons sunlight availability), the platform 102 can target consumers in such region with solar panels product ads. In another example, if a region already has high solar energy adoption, the platform 102 can target consumers in such region with solar accessory product ads.

[0328] In some embodiments, the platform 102 may be configured to provide and / or facilitate secure and compliant use of text messaging communications with one or both of customers and stakeholders. The platform 102 may adhere to regulations related to privacy and / or consent. For example, the platform 102 can manage text-based communications with stakeholders, ensuring every message sent complies with privacy and consent regulations By way of example, before sending a promotional message to a user, the platform 102 can check if the said user has consented to such communications.

[0329] In some embodiments, the platform 102 may be configured such that edge devices may monitor movement of energy production, storage, and consumption devices throughout an area served by an energy grid. Movement and / or dispositioning of devices may be based on monitoring network traffic passing through / by the edge devices, such as network equipment and the like. Movement and / or dispositioning may also be based on changes in network activity, such as increases in localized network activity associated with energy production / storage / consumption devices.

[0330] In some embodiments, tire platform 102 may be configured to detect movement of energy production devices. When energy producing devices are moved within a networked environment, such as by being detected in a new locale (different / new segment) of a networked environment, edge devices may use this information to adjust guidance / instructions for local energy systems regarding energy production, pricing, and the like. Depending on the nature of the newly positioned energy producing resources (e.g. , temporal or permanent) the rules or policies to govern energy production, storage, and utilization may be impacted. As an example, new energy production resources that are dedicated to a temporal event such as construction, a high attendance local event (e.g., a sports event), festival, and the like may suggest that demand on a local energy infrastructure may be mitigated for / during the event Although demand for energy locally may increase substantially, due to the dedicated energy sourcing resources being disposed locally, energy policies may suggest taking some portion of the local energy grid and / or energy producing resources off-line for maintenance. If it appears that newly disposed energy producing resources have a more generalized local supply approach (including a long-term presence), such as when responding to an increase in demand and / or reduction in unreliable sourcing, edge devices that detect these new energy supply resources may act as moderator to temper an impact on local energy supply providers, such as by limiting access to the new source of supply, alerting local energy authorities of the new sourcing presence, and the like.

[0331] In some embodiments, the platform 102 may be configured to detect movement of energy consumption devices or of energy consumers based on movement of, for example, consumer mobile devices. This may be achieved through detecting an unusual increase in device presence in a localized network, such as in proximity to one or more cellular antennas, and the like. Increasing presence of potential energy consumers, (e. ., such as at a social event, concert, sporting event, political event, and the like) in a localized network environment, once detected, may be responded to by the edge devices adjusting energy delivery infrastructure to make a corresponding amount of energy available in the impacted region. Another role that edge devices may play’ in such a scenario, is to increase radio transmit power and / or receive power across the affected region to accommodate the increase in device traffic This may extend to signaling to energy providers that networked edge devices (within a region and / or as identified by specific identifier) will be increasing energy consumption in the near term.

[0332] In some embodiments, the platform 102 may be configured such that edge devices may also detect and / or react to detecting an influx of energy storage systems, including without limitation, whole-home energy storage systems. When new energy storage device(s) are detected by edge devices, an energy management plan for a regionmay be adjusted to take into consideration new energy storage capabilities. This may involve managing energy grid utilization to better take advantage of the increased storage capacity. Local storage of energy, particularly consumer- direct energy, can be leveraged to off-load an energy grid during certain times, such as when demand is high, by directing the local energy storage systems to give up their energy to the grid at high demand times Likewise, edge devices may configure communication channels between sourcing and storage to facilitate coordination among these resources.AI-BASED ENERGY ORCHESTRATION, OPTIMIZATION, A D AUTOMATION SYSTEMS

[0333] Referring to FIG. 6, further detail is provided as to embodiments of the set of intelligence enablement systems 112, including the set of intelligent data layers 130, the distributed ledger and smart contract systems 132, the set of adaptive energy digital twin systems 134 and the set of energy simulation systems 136.

[0334] The set of intelligent data layers 130 may undertake any of the wide range of data processing capabilities noted throughout this disclosure and the documents incorporated by reference herein, optionally autonomously, under user supervision, or with semi-supervision, including extraction, transformation, loading, normalization, cleansing, compression, route selection, protocol selection, self-organization of storage, filtering, timing of transmission, encoding, decoding, and many others. The set of intelligent data layers 130 may include energy generation data layers 602 (such as producing and automatically configuring and routing streams or batches of data relating to energy generation by a set of entities, such as operating assets of an enterprise), energy storage data layers 604 (such as producing and automatically configuring and routing streams or batches of data relating to energy storage by a set of entities, such as operating assets of an enterprise or assets of a set of customers), energy delivery data layers 608 (such as producing and automatically configuring and routing streams or batches of data relating to energy delivery by a set of entities, such as delivery by transmission line, by pipeline, by portable energy storage, or others), and energy consumption data layers 610 (such as producing and automatically configuring and routing streams or batches of data relating to energy consumption by a set of entities, such as operating assets of an enterprise, a set of customers, a set of vehicles, or the like).

[0335] The distributed ledger and smart contract systems 132 may provide a set of underlying capabilities to enable energy-related transactions, such as purchases, sales, leases, futures contracts, and the like for energy generation, storage, delivery, or consumption, as well as for related types of transactions, such as in renewable energy credits, carbon abatement credits, pollution abatement credits, leasing of assets, shared economy transactions for asset usage, shared consumption contracts, bulk purchases, provisioning of mobile resources, and many others. This may include a set of energy transaction blockchains 612 or distributed ledgers to record energy transactions, including generation, storage, delivery, and consumption transactions. A set of energy transaction smart contracts 614 may operate on blockchain events and other input data to enable, configure, and execute the aforementioned types of transactions and others In some embodiments, a set of energy transaction intelligent agents 618 may be configured to design, generate, and deploy the set of energy transaction smart contracts 614, to optimize transaction parameters, to automatically discover counterparties, arbitrage opportunities, and the like, to recommend and / or automatically initiate steps to contract offers or execution, to resolve contracts upon completion based on blockchain data, and many other functions.

[0336] The set of adaptive energy digital twin systems 134 may include digital twins of energy-related entities, such as operating assets of an enterprise that generate, store, deliver, or consume energy, and may include may include energy generation digital twins 622 (such as displaying content from event logs, or from streams or batches of data relating to energy generation by a set of entities, such as operating assets of an enterprise), energy storage digitaltwins 624 (such as displaying energy storage status information, usage patterns, or the like for a set of entities, such as operating assets of an enterprise or assets of a set of customers), energy delivery digital twins 628 (such as displaying status data, events, workflows, and the like relating to energy delivery by a set of entities, such as delivery by transmission line, by pipeline, by portable energy storage, or others), and energy consumption digital twins 630 (such as displaying data relating to energy consumption by a set of entities, such as operating assets of an enterprise, a set of customers, a set of vehicles, or the like). The set of adaptive energy digital twin systems 134 may include various types of digital twin described throughout this disclosure and / or the documents incorporated herein by reference, such as ones fed by data streams from edge and loT devices, ones that adapt based on user role or context, ones that adapt based on market context, ones that adapt based on operating context, and many others.

[0337] The set of energy simulation systems 136 may include a wide range of systems for the simulation of energy- related behavior based on historical patterns, current states (including contextual, operating, market and other information), and anticipated / predicted states of entities involved in generation, storage, delivery and / or consumption of energy. This may include an energy generation simulation 632, energy storage simulation 634, energy delivery simulation 638 and energy consumption simulation 640, among others. The set of energy simulation systems 136 may employ a wide range of simulation capabilities, such as 3D visualization simulation of behavior of physical, presentation of simulation outputs in a digital twin, generation of simulated financial outcomes for a set of different operational scenarios, generation of simulated operational outcomes, and many others. Simulation may be based on a set of models, such as models of the energy generation, storage, delivery and / or consumption behavior of a machine or system, or a fleet of machines or systems (which may be aggregated based on underlying models and / or based on projection to a larger set from a subset of models) Models may be iteratively improved, such as by feedback of outcomes from operations and / or by feedback comparing model-based predictions to actual outcomes and / or predictions by other models or human experts. Simulations may be undertaken using probabilistic techniques, by random walk or random forest algorithms, by projections of trends from past data on current conditions, or the like. Simulations may be based on behavioral models, such as models of enterprise or individual behavior based on various factors, including past behavior, economic factors (e.g., elasticity of demand or supply in response to price changes), energy utilization models, and others. Simulations may use predictions from artificial intelligence, including artificial intelligence trained by machine learning (including deep learning, supervised learning, semi-supervised learning, or the like). Simulations may be configured for presentation in augmented reality, virtual reality and / or mixed reality interfaces and systems (collectively referred to as “XR”), such as to enable a user to interact with aspects of a simulation in order to be trained to control a machine, to set policies, to govern a factory or other entity that includes multiple machines, to handle a fleet of machines or factories, or the like. As one example among many, a simulation of a factory may simulate tire energy consumption of all machines in the factory' while presenting other data, such as operational data, input costs, production costs, computation costs, market pricing data, and other content in the simulation. In the simulation, a user may configure the factory', such as by setting output levels for each machine, and the simulation may simulate profitability of the factory' based on a variety of simulated market conditions Thus, the user may be trained to configure the factory' under a variety of different market conditions

[0338] Referring to FIG. 7 more detail is provided with respect to the set of Al-based energy orchestration, optimization, and automation systems 114, each of which may use various other capabilities, services, functions, modules, components, or other elements of the platform 102 in order to orchestrate energy-related entities, workflows, or the like on behalf of an enterprise or other user Orchestration may, for example, use robotic process automationto facilitate automated orchestration of energy-related entities and resources based on training data sets and / or human supervision based on historical human interaction data. As another example, orchestration may involve design, configuration, and deployment of a set of intelligent agents, which may automatically orchestrate a set of energy- related workflows based on operational, market, contextual and other inputs Orchestration may involve design, configuration, and deployment of autonomous control systems, such as systems that control energy-related activities based on operational data collected by or from onboard sensors, edge devices, loT devices and the like Orchestration may involve optimization, such as optimization of multivariate decisions based on simulation, optimization based on real-time inputs, and others Orchestration may involve use of artificial intelligence for pattern recognition, forecasting and prediction, such as based on historical data sets and current conditions.

[0339] The set of Al-based energy orchestration, optimization, and automation systems 114 may include the set of energy generation orchestration systems 138, the set of energy consumption orchestration systems 140, the set of energy storage orchestration systems 142, the set of energy marketplace orchestration systems 146 and the set of energy delivery orchestration systems 147, among others.

[0340] The set of energy generation orchestration systems 138 may include a set of generation timing orchestration sy stems 702 and a set of location orchestration systems 704, among others. The set of timing orchestration systems 702 may orchestrate the timing of energy generation, such as to ensure that timing of generation meets mission critical or operational needs, complies with policies and plans, is optimized to improve financial or operational metrics and / or (in the case of energy generated for sale) is well-timed based on fluctuations of energy market prices Generation timing orchestration can be based on models, simulations, or machine learning on historical data sets Generation timing orchestration can be based on current conditions (operating, market, and others)

[0341] The set of location orchestration systems 704 may orchestrate location of generation assets, including mobile or portable generation assets, such as portable generators, solar systems, wind systems, modular nuclear systems and others, as well as selection of locations for larger-scale, fixed infrastructure generation assets, such as power plants, generators, turbines, and others, such as to ensure that for any given operational location, available generation capacity (baseline and peak capacity) meets mission critical or operational needs, complies with policies and plans, is optimized to improve financial or operational metrics and / or (in the case of energy generated for sale) is well-located based on local variations in energy market prices. Generation location orchestration can be based on models, simulations, or machine learning on historical data sets. Generation location orchestration can be based on current conditions (operating, market, and others).

[0342] The set of energy consumption orchestration systems 140 may include a set of consumption timing optimization systems 718 and a set of operational prioritization systems 720, among others. The set of consumption timing optimization systems 718 may orchestrate timing consumption, such as to shift consumption for non-critical activities to lower-cost energy resources (e.g., by shifting to off-peak times to obtain lower electricity pricing for grid energy consumption, shifting to lower cost resources (e.g., renewable energy systems in lieu of the grid), to shift consumption to activities that are more profitable (e.g. , to shift consumption to a machine that has a high marginal profit per time period based on current market and operating conditions (such as detected by a combination of edge and loT devices and market data sources), and the like).

[0343] The set of operational prioritization systems 720 may enable a user, intelligent agent, or the like to set operational priorities, such as by rule or policy, by setting target metrics (e.g., for efficiency, marginal profit production, or the like), by declaring mission-critical operations (e.g., for safety, disaster recovery and emergencysystems), by declaring priority among a set of operating assets or activities, or the like In some embodiments, energy consumption orchestration may take inputs from operational prioritization to provide a set of recommendations or control instructions to optimize energy consumption by a machine, components, a set of machines, a factory, or a fleet of assets

[0344] The set of energy storage orchestration systems 142 may include a set of storage location orchestration systems 708 and a set of margin of safety orchestration systems 710. The set of storage location orchestration systems 708 may orchestrate location of storage assets, including mobile or portable generation assets, such as portable batteries, fuel cells, nuclear storage systems and others, as well as selection of locations for larger-scale, fixed infrastructure storage assets, such as large-scale arrays of batteries, fuel storage systems, thermal energy storage systems te.g., using molten salt), gravity-based storage systems, storage systems using fluid compression, and others, such as to ensure that for any given operational location, available storage capacity meets mission critical or operational needs, complies with policies and plans, is optimized to improve financial or operational metrics and / or (in the case of energy stored and provide for sale) is well-located based on local variations in energy market prices. Storage location orchestration can be based on models, simulations, or machine learning on historical data sets, such as behavioral models that indicate usage patterns by individuals or enterprises. Storage location orchestration can be based on current conditions (operating, market, and others) and many other factors; for example, storage capacity can be brought to locations where grid capacity is offline or unusually constrained (e. ., for disaster recovery).

[0345] The set of margin of safety orchestration systems 710 may be used to orchestrate storage capacity to preserve a margin of safety, such as a minimum amount of stored energy to power mission critical systems (e.g., life support systems, perimeter security systems, or the like) or high priority systems (e.g, high-margin manufacturing) for a defined period in case of loss of baseline energy capacity (e.g. , due to an outage or brownout of the grid) or inadequate renewable energy production (e.g., when there is inadequate wind, water or solar power due to weather conditions, drought, or the like). The minimum amount may be set by rule or policy, or may be learned adaptively, such as by an intelligent agent, based on a training data set of outcomes and / or based on historical, current, and anticipated conditions (e.g., climate and weather forecasts). The set of margin of safety orchestration systems 710 may, In some embodiments, take inputs from the energy provisioning and governance solutions 156.

[0346] The set of energy marketplace orchestration systems 146 may include a set of transaction aggregation systems 722 and a set of futures market optimization systems 724.

[0347] The set of transaction aggregation systems 722 systems may automatically orchestrate a set of energy-related transactions, such as purchases, sales, orders, futures contracts, hedging contracts, limit orders, stop loss orders, and others for energy generation, storage, delivery' or consumption, for renewable energy credits, for carbon abatement credits, for pollution abatement credits, or the like, such as to aggregate a set of smaller transactions into a bulk transaction, such as to take advantage of volume discounts, to ensure current or day-ahead pricing when favorable, to enable fractional ownership by a set of owners, operators, or consumers of a block of energy generation, storage, or delivery capacity, or the like For example, an enterprise may aggregate energy purchases across a set of assets in different jurisdictions by use of an intelligent agent that aggregates a set of futures market energy purchases across the jurisdiction and represents the aggregated purchases in a centralized location, such as an operating digital twin of the enterprise.

[0348] The set of futures market optimization systems 724 may automatically orchestrate aggregation of a set of futures markets contracts for energy, renewable energy credits, for carbon offsets or abatement credits, for pollutionabatement credits, or the like based on a forecast of future energy needs for an individual or enterprise. The forecast may be based on historical usage patterns, current operating conditions, current market conditions, anticipated operational needs, and the like. The forecast may be generated using a predictive model and / or by an intelligent agent, such as one based on machine learning on outcomes, on human output, on human-labeled data, or the like The forecast may be generated by deep learning, supervised learning, semi-supervised learning, or the like. Based on the forecast, an intelligent agent may design, configure, and execute a series of futures market transactions across various jurisdictions to meet anticipated timing, location, and type of needs.

[0349] The set of energy delivery orchestration systems 147 may include a set of delivery routing orchestration systems 712 and a set of energy delivery type orchestration systems 714.

[0350] The set of energy delivery routing orchestration systems 712 may use various components, modules, facilities, services, functions and other elements of the platform 102 to orchestrate routing of energy delivery, such as based on location, timing and type of needs, available generation and storage capacity at places of energy need, available energy sources for routing (e.g, liquid fuel, portable energy generation systems, portable energy storage systems, and the like), available routes ( c.g.. main pipelines, pipeline branches, transmission lines, wireless power transfer systems, and transportation infrastructure (roads, railways and waterways, among others)), market factors (price of energy, price of goods, profit margins for production activities, timing of events that require energy, and others), environmental factors (e.g. , weather), operational priorities, and others. A set of artificial intelligence systems trained in various ways disclosed herein may be trained to recommend or to configure a route, such as based on the foregoing inputs and a set of training data, such as human routing activities, a route optimization model, iteration among a large number of simulated scenarios, or the like, or combination of any of the foregoing For example, a set of control instructions may direct valves and other elements of an energy pipeline to deliver an amount of fluid-based energy to a location while directing mobile or portable resources to another location that would otherwise have reduced energy availability based on the pipeline routing instructions.

[0351] The set of energy delivery type orchestration systems 714 may use various components, modules, facilities, services, functions and other elements of the platform 102 to orchestrate optimization of the type of energy delivery, such as based on location, timing and type of needs, available generation and storage capacity at places of energy need, available energy sources for routing (e.g, liquid fuel, portable energy generation systems, portable energy storage systems, and the like), available routes (e.g., main pipelines, pipeline branches, transmission lines, wireless power transfer systems, and transportation infrastructure (roads, railways and waterways, among others)), market factors (price of energy, price of goods, profit margins for production activities, timing of events that require energy, and others), environmental factors (e.g., weather), operational priorities, and others. A set of artificial intelligence systems trained in various ways disclosed herein may be trained to recommend or to configure a mix of energy types, such as based on the foregoing inputs and a set of training data, such as human type selection activities, a delivery type optimization model, iteration among a large number of simulated scenarios, or the like, or combination of any of the foregoing For example, a set of recommendations or control instructions may select a set of portable, modular energy resources that are compatible with needs (e.g., specifying renewable sources where there is high storage capacity to meet operational needs, such that inexpensive, intermittent sources are preferred), while the instructions may select more expensive natural gas energy where storage capacity is limited or absent and usage is continuous (such as for a 24 / 7 data center that operates remotely from the energy grid).

[0352] Many other examples of Al-based energy orchestration, optimization, and automation systems 114 are provided throughout this disclosure.CONFIGURABLE DATA AND INTELLIGENCE MODULES AND SERVICES

[0353] Referring to FIG 8 the set of configurable data and intelligence modules and services 118 may include the set of energy transaction enablement systems 144, the set of stakeholder energy digital twins 148 and the set of data integrated microservices 150, among many others. These data and intelligence modules may include various components, modules, services, subsystems, and other elements needed to configure a data stream or batch, to configure intelligence to provide a particular type of output, or the like, such as to enable other elements of the platform 102 and / or various stakeholder solutions.

[0354] The set of energy transaction enablement systems 144 may include a set of counterparty and arbitrage discovery systems 802, a set of automated transaction configuration systems 804 and a set of energy investment and divestiture recommendation systems 808, among others. The set of counterparty and arbitrage discovery systems 802 may be configured to operate on various data sources related to operating energy needs, contextual factors, and a set of energy market, renewable energy credit, carbon offset, pollution abatement credit, or other energy-related market offers by a set of counterparties in order to determine a recommendation or selection of a set of counterparties and offers. An intelligent agent of the set of counterparty and arbitrage discovery systems 802 may initiate a transaction with a set of counterparties based on the recommendation or selection. Factors may include cost, counterparty reliability, size of counterparty offer, timing, location of energy needs, and many others

[0355] The set of automated transaction configuration systems 804 may automatically or under human supervision recommend or automatically configure terms for a transaction, such as based on contextual factors (e.g, weather), historical, current, or anticipated / predicted market data (e.g. , relating to energy pricing, costs of production, costs of storage, and the like), timing and location of operating needs, and other factors. Automation may be by artificial intelligence, such as trained on human configuration interactions, trained by deep learning on outcomes, or trained by iterative improvement through a series of trials and adjustments (e.g., of the inputs and / or weights of a neural network).

[0356] The set of energy investment and divestiture recommendation systems 808 may automatically or under human supervision recommend or automatically configure terms for an investment or divestiture transaction, such as based on contextual factors (e.g. , weather), historical, current, or anticipated / predicted market data (e.g. , relating to energy pricing, costs of production, costs of storage, and the like), timing and location of operating needs, and other factors. Automation may be by artificial intelligence, such as trained on human configuration interactions, trained by deep learning on outcomes, or trained by iterative improvement through a series of trials and adjustments (e.g., of the inputs and / or weights of a neural network). For example, the set of energy investment and divestiture recommendation systems 808 may output a recommendation to invest in additional modular, portable generation units to support locations of planned energy exploration activities or the divestiture of relatively inefficient factories, where energy costs are forecast to produce negative marginal profits.

[0357] The set of stakeholder energy digital twins 148 may include a set of financial energy digital twins 810, a set of operational energy digital twins 812 and a set of executive energy digital twins 814, among many others. The set of financial energy digital twins 810 may, for example, represent a set of entities, such as operating assets of an enterprise, along with energy-related financial data, such as the cost of energy being used or forecast to be used by a machine, component, factory, or fleet of assets, the price of energy that could be sold, the cost or price of renewableenergy credits available through use of renewable energy generation capacity, the cost or price of carbon offsets needed to offset current of future anticipated operations, the cost of pollution abatement offsets or credits, and the like. The set of financial energy digital twins 810 may be integrated with other financial reporting systems and interfaces, such as enterprise resource planning suites, financial accounting suites, tax systems, and others

[0358] The set of operational energy digital twins 812 may, for example, represent operational entities involved in energy generation, storage, delivery, or consumption, along with relevant specification data, historical, current or anticipated / predicted operating states or parameters, and other information, such as to enable an operator to view components, machines, systems, factories, and various combinations and sets thereof, on an individual or aggregate level. The set of operational energy digital twins 812 may display energy data and energy-related data relevant to operations, such as generation, storage, delivery and consumption data, carbon production, pollution emissions, waste heat production, and the like. A set of intelligent agents may provide alerts in the digital twins. The digital twins may automatically adapt, such as by highlighting important changes, critical operations, maintenance, or replacement needs, or the like. The set of operational energy digital twins 812 may take data from onboard sensors, loT devices, and edge devices positioned at or near relevant operations, such as to provide real-time, current data.

[0359] The set of executive energy digital twins 814 may, for example, display entities involved in energy generation, storage, delivery' or consumption, along with relevant specification data, historical, current or anticipated / predicted operating states or parameters, and other information, such as to enable an executive to view key performance metrics driven by energy with respect to components, machines, systems, factories, and various combinations and sets thereof, on an individual or aggregate level The set of executive energy digital twins 814 may display energy data and energy-related data relevant to executive decisions, such as generation, storage, delivery and consumption data, carbon production, pollution emissions, waste heat production, and the like, as well as financial performance data, competitive market data, and the like. A set of intelligent agents may provide alerts in the digital twins, such as configured to the role of the executive (e.g., financial data to a CFO, risk management data to a chief legal officer, and aggregate performance data to a CEO or chief strategy officer. The set of executive energy digital twins 814 may automatically adapt, such as by highlighting important changes, critical operations, strategic opportunities, or the like. The set of executive energy digital twins 814 may take data from onboard sensors, loT devices, and edge devices positioned at or near relevant operations, such as to provide real-time, current data.

[0360] The set of data integrated microservices 150 may include a set of energy market data services 818, a set of operational data services 820 and a set of other contextual data services 822, among many others.

[0361] The set of energy market data services 818 may provide a configured, filtered and / or otherwise processed feed of relevant market data, such as market prices of the goods and sendees of an enterprise, a feed of historical, current and / or futures market energy prices in the operating jurisdictions of the enterprise (optionally weighted or ordered based on relative energy usage across the jurisdictions), a feed of historical and / or proposed transactions (optionally augmented with counterparty information) configured according to a set of preferences of a user or enterprise (e.g., to show transactions relevant to the operating requirements or energy capacities of the enterprise), a feed of historical, current or future renewable energy credit prices, a feed of historical, current or future carbon offset prices, a feed of historical, current or future pollution abatement credit prices, and others.

[0362] The set of operational data services 820 may provide a configured, filtered and / or otherwise processed feed of operational data, such as historical, current, and anticipated / predicted states and events of operating assets of anenterprise, such as collected by sensors, loT devices and / or edge devices and or anticipated or inferred based on a set of models, analytic systems, and or operation of artificial intelligence systems, such as intelligent forecasting agents.

[0363] The set of other contextual data services 822 may provide a wide range of configured, filtered, or otherwise processed feeds of contextual data, such as weather data, user behavior data, location data for a population, demographic data, psychographic data, and many others.

[0364] The configurable data integrated microservices of various types may provide various configured outputs, such as batches and files, database reports, event logs, data streams, and others. Streams and feeds may be automatically generated and pushed to other systems, services may be queried and / or may be pulled from sources (e.g., distributed databases, data lakes, and the like), and may be pulled by application programming interfaces.

[0365] In some embodiments, the platform 102 may include one or more virtual power plants The virtual power plants may be or include one or more of: a virtual power plant for aggregating and managing multiple heterogeneous energy resources in one place, a virtual power plant wherein the energy resources include solar plants, battery storage systems, wind turbines, electric vehicle charging stations, demand and response management centers, and smart meters, and a virtual power plant for managing a set of small, isolated power generation points used for load-leveling, to absorb excess supply from intermittent renewables, and to deliver supply during shortages.FIGS. 9-13: RELATED TECHNOLOGIES

[0366] Various embodiments of the techniques and systems presented herein may include a number of related technologies, including (without limitation) the following.DU L-PURPOSE ARTIFICI L NEURAL NETWORK (DP ANN)

[0367] FIG. 9 illustrates an example of a platform 102 including a dual process artificial neural network (DP ANN) system. The DPANN system includes an artificial neural network (ANN) having behaviors and operational processes (such as decision-making) that are products of a training system and a retraining system. The training system is configured to perform automatic, trained execution of ANN operations. The retraining system performs effortful, analytical, intentional retraining of the ANN, such as based on one or more relevant aspects of the ANN, such as memory, one or more input data sets (including time information with respect to elements in such data sets), one or more goals or objectives (including ones that may vary dynamically, such as periodically and / or based on contextual changes, such as ones relating to the usage context of the ANN), and / or others In cases involving memory-based retraining, the memory may include original / historical training data and refined training data. The DPANN system includes a dual process learning function or DPTF 902 configured to manage and perform an ongoing data retention process. The DPLF 902 (including, where applicable, memory management process) facilitate retraining and refining of behavior of the ANN. The DPLF 902 provides a framework by which the ANN creates outputs such as predictions, classifications, recommendations, conclusions and / or other outputs based on a historic inputs, new inputs, and new outputs (including outputs configured for specific use cases, including ones determined by parameters of the context of utilization (which may include performance parameters such as latency parameters, accuracy parameters, consistency parameters, bandwidth utilization parameters, processing capacity utilization parameters, prioritization parameters, energy utilization parameters, and many others).

[0368] In some embodiments, the DPANN system stores training data, thereby allowing for constant retraining based on results of decisions, predictions, and / or other operations of the ANN, as well as allowing for analysis of training data upon the outputs of the ANN. The management of entities stored in the memory allows the construction and execution of new models, such as ones that may be processed, executed or otherwise performed by or undermanagement of the training system. The DP ANN system uses instances of the memory’ to validate actions (e.g., in a manner similar to the thinking of a biological neural network (including retrospective or self-reflective thinking about whether actions that were undertaken under a given situation where optimal) and perform training of the ANN, including training that intentionally feeds the ANN with appropriate sets of memories (i.e., ones that produce favorable outcomes given the performance requirements for the ANN).

[0369] In some embodiments, FIG 9 illustrates an exemplary process of the DPLF 902. The DPLF 902 may be or include the continued process retention of one or more training datasets and / or memories stored in the memory over time. The DPLF 902 thereby allows the ANN to apply existing neural functions and draw upon sets of past events (including ones that are intentionally varied and / or curated for distinct purposes), such as to frame understanding of and behavior within present, recent, and / or new scenarios, including in simulations, during training processes, and in fully operational deployments of the ANN. The DPLF 902 may provide the ANN with a framework by which the ANN may analyze, evaluate, and / or manage data, such as data related to the past, present and future. As such, the DPLF 902 plays a crucial role in training and retraining the ANN via the training system and the retraining system.

[0370] In some embodiments, the DPLF 902 is configured to perform a dual-process operation to manage existing training processes and is also configured to manage and / or perform new training processes, i.e. , retraining processes. In some embodiments, each instance of the ANN is trained via the training system and configured to be retrained via the retraining system. The ANN encodes training and / or retraining datasets, stores the datasets, and retrieves the datasets during both training via the training system and retraining via the retraining system. The DP ANN system may recognize whether a dataset (the term dataset in this context optionally including various subsets, supersets, combinations, permutations, elements, metadata, augmentations, or the like, relative to a base dataset used for training or retraining), storage activity, processing operation and / or output, has characteristics that natively favor the training system versus the retraining system based on its respective inputs, processing (e.g., based on its structure, type, models, operations, execution environment, resource utilization, or the like) and / or outcomes (including outcome types, performance requirements (including contextual or dynamic requirements), and the like. For example, the DP ANN system may determine that poor performance of the training system on a classification task may indicate a novel problem for which the training of the ANN was not adequate (e.g., in type of data set, nature of input models and / or feedback, quantity of training data, quality of tagging or labeling, quality of supervision, or the like), for which the processing operations of the ANN are not well-suited (e.g., where they are prone to known vulnerabilities due to the type of neural network used, the type of models used, etc.), and that may be solved by engaging the retraining system to retrain the model to teach the model to learn to solve the new classification problem (e.g., by feeding it many more labeled instances of correctly classified items). With periodic or continuous evaluation of the performance of the ANN, the DPANN system may subsequently determine that highly stable performance of tire ANN (such as where only small improvements of the ANN occur over many iterations of retraining by the retraining system) indicates readiness for the training system to replace the retraining system (or be weighted more favorably where both are involved) Over longer periods of time, cycles of varying performance may emerge, such as where a series of novel problems emerge, such that the retraining system of the DPANN is serially engaged, as needed, to retrain the ANN and / or to augment the ANN by providing a second source of outputs (which may be fused or combined with ANN outputs to provide a single result (with various weightings across them), or may be provided in parallel, such as enabling comparison, selection, averaging, or context- or situation-specific application of the respective outputs).

[0371] In some embodiments, the ANN is configured to learn new functions in conjunction with the collection of data according to the dual-process training of the ANN via the training system and the retraining system. The DP ANN system performs analysis of the ANN via the training system and performs initial training of the ANN such that the ANN gains new internal functions (or internal functions are subtracted or modified, such as where existing functions are not contributing to favorable outcomes) After the initial training, the DP ANN system performs retraining of the ANN via the retraining system. To perform the retraining, the retraining system evaluates the memory and historic processing of the ANN to construct targeted DPLF 902 processes for retraining. The DPLF 902 processes may be specific to identified scenarios. The ANN processes can run in parallel with the DPLF 902 processes. By way of example, the ANN may function to operate a particular make and model of a self-driving car after tire initial training by the training system The DP ANN system may perform retraining of the functions of tire ANN via the retraining system, such as to allow the ANN to operate a different make and model of car (such as one with different cameras, accelerometers and other sensors, different physical characteristics, different performance requirements, and the like), or even a different kind of vehicle, such as a bicycle or a spaceship.

[0372] In some embodiments, as quality of outputs and / or operations of the ANN improves, and as long as the performance requirements and the context of utilization for the ANN remain fairly stable, performing the dual-process training process can become a decreasingly demanding process. As such, the DPANN system may determine that fewer neurons of the ANN are required to perform operations and / or processes of the ANN, that performance monitoring can be less intensive (such as with longer intervals between performance checks), and / or that the retraining is no longer necessary (at least for a period of time, such as until a long-term maintenance period arrives and / or until there are significant shifts in context of utilization) As the ANN continues to improve upon existing functions and / or add new functions via the dual-process training process, the ANN may perform other, at times more “intellectually- demanding” (e.g., retraining intensive) tasks simultaneously. For example, utilizing dual process-learned knowledge of a function or process being trained, the ANN can solve an unrelated complex problem or make a retraining decision simultaneously. The retraining may include supervision, such as where an agent (e. ., human supervisor or intelligent agent) directs the ANN to a retraining objective (e.g., “master this new function”) and provides a set of training tasks and feedback functions (such as supervisory grading) for the retraining. In-embodiments, the ANN can be used to organize the supervision, training and retraining of other dual process-trained ANNs, to seed such training or retraining, or the like.

[0373] In some embodiments, one or more behaviors and operational processes (such as decision-making) of the ANN may be products of training and retraining processes facilitated by the training system and the retraining system, respectively. The training system may be configured to perform automatic training of ANN, such as by continuously adding additional instances of training data as it is collected by or from various data sources. The retraining system may be configured to perform effortful, analytical, intentional retraining of the ANN, such as based on memory (e.g. , stored training data or refined training data) and / or optionally based on reasoning or other factors. For example, in a deployment management context, the training system may be associated with a standard response by the ANN, while the retraining system may implement DPLF 902 retraining and / or network adaptation of the ANN. In some cases, retraining of the ANN beyond the factory, or “out-of-the-box,” training level may involve more than retraining by the retraining system. Successful adjustment of the ANN by one or more network adaptations may be dependent on the operation of one or more network adjustments of the training system.

[0374] In some embodiments, the training system may facilitate fast operating by and training of the ANN by applying existing neural functions of the ANN based on training of the ANN with previous datasets. Standard operational activities of the ANN that may draw heavily on the training system may include one or more of the methods, processes, workflows, systems, or the like described throughout this disclosure and the documents incorporated herein, such as, without limitation: defined functions within networking (such as discovering available networks and connections, establishing connections in networks, provisioning network bandwidth among devices and systems, routing data within networks, steering traffic to available network paths, load balancing across networking resources, and many others); recognition and classification (such as of images, text, symbols, objects, video content, music and other audio content, speech content, and many others); spoken words; prediction of states and events (such as prediction of failure modes of machines or systems, prediction of events within workflows, predictions of behavior in shopping and other activities, and many others); control (such as controlling autonomous or semi-autonomous systems, automated agents (such as automated call-center operations, chat bots, and the like) and others); and / or optimization and recommendation (such as for products, content, decisions, and many others). ANNs may also be suitable for training datasets for scenarios that only require output. The standard operational activities may not require the ANN to actively analyze what is being asked of the ANN beyond operating on well-defined data inputs, to calculate well-defined outputs for well-defined use cases. The operations of the training system and / or the retraining system may be based on one or more historic data training datasets and may use the parameters of the historic data training datasets to calculate results based on new input values and may be performed with small or no alterations to the ANN or its input types In some embodiments, an instance of the training system can be trained to classify whether the ANN is capable of performing well in a given situation, such as by recognizing whether an image or sound being classified by the ANN is of a type that has historically been classified with a high accuracy (e.g., above a threshold).

[0375] In some embodiments, network adaptation of the ANN by one or both of the training system and the retraining system may include a number of defined network functions, knowledge, and intuition-like behavior of the ANN when subjected to new input values. In such embodiments, the retraining system may apply the new input values to the DPLF 902 system to adjust the functional response of the ANN, thereby performing retraining of the ANN. The DP ANN system may determine that retraining the ANN via network adjustment is necessary when, for example, without limitation, functional neural networks are assigned activities and assignments that require the ANN to provide a solution to a novel problem, engage in network adaptation or other higher-order cognitive activity, apply a concept outside of the domain in which the DP ANN was originally designed, support a different context of deployment (such as where the use case, performance requirements, available resources, or other factors have changed), or the like. The ANN can be trained to recognize where the retraining system is needed, such as by training the ANN to recognize poor performance of the training system, high variability of input data sets relative to the historical data sets used to train the training system, novel functional or performance requirements, dynamic changes in the use case or context, or other factors. The ANN may apply reasoning to assess performance and provide feedback to the retraining system The ANN may be trained and / or retrained to perform intuitive functions, optionally including by a combinatorial or re-combinatorial process (e.g., including genetic programming wherein inputs (e.g., data sources), processes / functions (e.g., neural network types and structures), feedback, and outputs, or elements thereof, are arranged in various permutations and combinations and the ANN is tested in association with each (whether in simulations or live deployments), such as in a series of rounds, or evolutionary steps, to promote favorable variants until a preferred ANN, or preferred set of ANNs is identified for a given scenario, use case, or set of requirements).This may include generating a set of input “ideas” (e.g. , combinations of different conclusions about cause-and-effect in a diagnostic process) for processing by the retraining system and subsequent training and / or by an explicit reasoning process, such as a Bayesian reasoning process, a casuistic or conditional reasoning process, a deductive reasoning process, an inductive reasoning process, or others (including combinations of the above) as described in this disclosure or the documents incorporated herein by reference

[0376] In some embodiments, the DPLF 902 may perform an encoding process of the DPLF 902 to process datasets into a stored form for future use, such as retraining of the ANN by the retraining system. The encoding process enables datasets to be taken in, understood, and altered by the DPLF 902 to better support storage in and usage from the memory. The DPLF 902 may apply current functional knowledge and / or reasoning to consolidate new input values. The memory can include short-term memory or STM 906, long-term memory or LTM 912, or a combination thereof. The datasets may be stored in one or both of the STM 906 and the LTM 912. The STM 906 may be implemented by the application of specialized behaviors inside the ANN (such as recurrent neural network, which may be gated or un-gated, or long-term short-term neural networks). The LTM 912 may be implemented by storing scenarios, associated data, and / or unprocessed data that can be applied to the discovery of new scenarios. The encoding process may include processing and / or storing, for example, visual encoding data (e.g., processed through a Convolution Neural Network), acoustic sensor encoding data (e.g., how something sounds, speech encoding data (e.g., processed through a deep neural network (DNN), optionally including for phoneme recognition), semantic encoding data of words, such to determine semantic meaning, e.g. , by using a Hidden Markov Model (HMM); and / or movement and / or tactile encoding data (such as operation on vibration / accelerometer sensor data, touch sensor data, positional or geolocation data, and the like) While datasets may enter the DPLF 902 system through one of these modes, the form in which the datasets are stored may differ from an original form of the datasets and may pass-through neural processing engines to be encoded into compressed and / or context-relevant format. For example, an unsupervised instance of the ANN can be used to learn the historic data into a compressed format.

[0377] In some embodiments, the encoded datasets are retained within the DPLF 902 system. Encoded datasets are first stored in short-term DPLF 902, i.e., STM 906. For example, sensor datasets may be primarily stored in STM 906, and may be kept in STM 906 through constant repetition. The datasets stored in the STM 906 are active and function as a kind of immediate response to new input values. The DP ANN system may remove datasets from STM 906 in response to changes in data streams due to, for example, running out of space in STM 906 as new data is imported, processed and / or stored. For example, it is viable for short-term DPLF 902 to only last between 15 and 30 seconds. STM 906 may only store small amounts of data typically embedded inside tire ANN.

[0378] In some embodiments, the DPANN system may measure attention based on utilization of the training system, of the DPANN system as a whole, and / or the like, such as by consuming various indicators of attention to and / or utilization of outputs from the ANN and transmitting such indicators to the ANN in response (similar to a “moment of recognition” in the brain where attention passes over something and the cognitive system says “aha!”). In some embodiments, attention can be measured by the sheer amount of the activity of one or both of the systems on the data stream. In some embodiments, a system using output from the ANN can explicitly indicate attention, such as by an operator directing the ANN to pay attention to a particular activity (e.g. , to respond to a diagnosed problem, among many other possibilities). The DPANN system may manage data inputs to facilitate measures of attention, such as by prompting and / or calculating greater attention to data that has high inherent variability from historical patterns (e.g., in rates of change, departure from norm, etc.), data indicative of high variability in historical performance (such asdata having similar characteristics to data sets involved in situations where the ANN performed poorly in training), or the like.

[0379] In some embodiments, the DP ANN system may retain encoded datasets within the DPLF 902 system according to and / or as part of one or more storage processes The DPLF 902 system may store the encoded datasets in LTM 912 as necessary after the encoded datasets have been stored in STM 906 and determined to be no longer necessary and / or low priority for a current operation of the ANN, training process, retraining process, etc. The LTM 912 may be implemented by storing scenarios, and the DPANN system may apply associated data and / or unprocessed data to the discovery of new scenarios. For example, data from certain processed data streams, such as semantically encoded datasets, may be primarily stored in LTM 912 The LTM 912 may also store image (and sensor) datasets in encoded form, among many other examples.

[0380] In some embodiments, the LTM 912 may have relatively high storage capacity, and datasets stored within LTM 912 may, in some scenarios, be effectively stored indefinitely. The DPANN system may be configured to remove datasets from the LTM 912, such as by passing LTM 912 data through a series of memory structures that have increasingly long retrieval periods or increasingly high threshold requirements to trigger utilization (similar to where a biological brain “drinks very' hard” to find precedent to deal with a challenging problem), thereby providing increased salience of more recent or more frequently used memories while retaining the ability to retrieve (with more time / effort) older memories when the situation justifies more comprehensive memory utilization. As such, the DPANN system may arrange datasets stored in the LTM 912 on a timeline, such as by storing the older memories (measured by time of origination and / or latest time of utilization) on a separate and / or slower system, by penalizing older memories by imposing artificial delays in retrieval thereof, and / or by imposing threshold requirements before utilization (such as indicators of high demand for improved results). Additionally or alternatively, LTM 912 may be clustered according to other categorization protocols, such as by topic. For example, all memories proximal in time to a periodically recognized person may be clustered for retrieval together, and / or all memories that were related to a scenario may be clustered for retrieval together.

[0381] In some embodiments, the DPANN system may modularize and link LTM 912 datasets, such as in a catalog, a hierarchy, a cluster, a knowledge graph (directed / acyclic or having conditional logic), or the like, such as to facilitate search for relevant memories. For example, all memory modules that have instances involving a person, a topic, an item, a process, a linkage of n-tuples of such things (e.g., all memory modules that involve a selected pair of entities), etc. The DPANN system may select sub-graphs of the knowledge graph for the DPLF 902 to implement in one or more domain-specific and / or task-specific uses, such as training a model to predict robotic or human agent behavior by using memories that relate to a particular set of robotic or human agents, and / or similar robotic or human agents. The DPLF 902 system may cache frequently used modules for different speed and / or probability of utilization. High value modules (e.g., ones with high-quality outcomes, performance characteristics, or the like) can be used for other functions, such as selection / training of STM 906 keep / forget processes.

[0382] In some embodiments, the DPANN system may modularize and link LTM datasets, such as in various ways noted above, to facilitate search for relevant memories. For example, memory modules that have instances involving a person, a topic, an item, a process, a linkage of n-tuples of such things (such as all memory' modules that involve a selected pair of entities), or all memories associated with a scenario, etc., may be linked and searched. The DPANN system may select subsets of the scenario (e.g., sub-graphs of a knowledge graph) for the DPLF 902 for a domainspecific and / or task-specific use, such as training a model to predict robotic or human agent behavior by usingmemories that relate to a particular set of robotic or human agents and / or similar robotic or human agents. Frequently used modules or scenarios can be cached for different speed / probability of utilization, or other performance characteristics. High value modules or scenarios (ones where high-quality outcomes results) can be used for other functions, such as selection / training of STM 906 keep / forget processes, among others

[0383] In some embodiments, the DP ANN system may perform LTM planning, such as to find a procedural course of action for a declaratively described system to reach its goals while optimizing overall performance measures. The DP ANN system may perform LTM planning when, for example, a problem can be described in a declarative way, the DP ANN system has domain knowledge that should not be ignored, there is a structure to a problem that makes the problem difficult for pure learning techniques, and / or the ANN needs to be trained and / or retrained to be able to explain a particular course of action taken by the DP ANN system. In some embodiments, the DPANN system may be applied to a plan recognition problem, i.e., the inverse of a planning problem: instead of a goal state, one is given a set of possible goals, and the objective in plan recognition is to find out which goal was being achieved and how.

[0384] In some embodiments, the DPANN system may facilitate LTM scenario planning by users to develop longterm plans For example, LTM scenario planning for risk management use cases may place added emphasis on identifying extreme or unusual, yet possible, risks and opportunities that are not usually considered in daily operations, such as ones that are outside a bell curve or normal distribution, but that in fact occur with greater-than- anticipated frequency in ‘Tong tail” or “fat tail” situations, such as involving information or market pricing processes, among many others LTM scenario planning may involve analyzing relationships between forces (such as social, technical, economic, environmental, and / or political trends) in order to explain the current situation, and / or may include providing scenarios for potential future states

[0385] In some embodiments, the DPANN system may facilitate LTM scenario planning for predicting and anticipating possible alternative futures along with the ability to respond to the predicted states. The LTM planning may be induced from expert domain knowledge or projected from current scenarios, because many scenarios (such as ones involving results of combinatorial processes that result in new entities or behaviors) have never yet occurred and thus cannot be projected by probabilistic means that rely entirely on historical distributions The DPANN system may prepare the application to LTM 912 to generate many different scenarios, exploring a variety of possible futures to the DPLM for both expected and surprising futures. This may be facilitated or augmented by genetic programming and reasoning techniques as noted above, among others.

[0386] In some embodiments, the DPANN system may implement LTM scenario plaiming to facilitate transforming risk management into a plan recognition problem and apply the DPLF 902 to generate potential solutions. LTM scenario induction addresses several challenges inherent to forecast planning LTM scenario induction may be applicable when, for example, models that are used for forecasting have inconsistent, missing, unreliable observations: when it is possible to generate not just one but many future plans; and / or when LTM domain knowledge can be captured and encoded to improve forecasting (e.g., where domain experts tend to outperform available computational models) LTM scenarios can be focused on applying LTM scenario planning for risk management LTM scenarios planning may provide situational awareness of relevant risk drivers by detecting emerging storylines. In addition, LTM scenario planning can generate future scenarios that allow DPLM, or operators, to reason about, and plan for, contingencies and opportunities in the future.

[0387] In some embodiments, the DPANN system may be configured to perform a retrieval process via the DPLF 902 to access stored datasets of the ANN. The retrieval process may determine how well the ANN performs withregard to assignments designed to test recall. For example, the ANN may be trained to perform a controlled vehicle parking operation, whereby the autonomous vehicle returns to a designated spot, or the exit, by associating a prior visit via retrieval of data stored in the LTM 912 The datasets stored in the STM 906 and the LTM may be retrieved by differing processes The datasets stored in the STM 906 may be retrieved in response to specific input and / or by order in which the datasets are stored, e.g., by a sequential list of numbers. The datasets stored in the LTM 912 may be retrieved through association and / or matching of events to historic activities, e.g. , through complex associations and indexing of large datasets.

[0388] In some embodiments, the DPANN system may implement scenario monitoring as at least a part of the retrieval process. A scenario may provide context for contextual decision-making processes In some embodiments, scenarios may involve explicit reasoning (such as cause-and-effect reasoning, Bayesian, casuistic, conditional logic, or the like, or combinations thereof) the output of which declares what LTM-stored data is retrieved (e.g., a timeline of events being evaluated and other timelines involving events that potentially follow a similar cause-and-effect pattern). For example, diagnosis of a failure of a machine or workflow may retrieve historical sensor data as well as LTM data on various failure modes of that type of machine or workflow (and / or a similar process involving a diagnosis of a problem state or condition, recognition of an event or behavior, a failure mode (e.g., a financial failure, contract breach, or the like), or many others).QUANTUM COMPUTING SERVICE

[0389] FIG. 10 illustrates an example quantum computing system 3800 according to some embodiments of the present disclosure In some embodiments, the quantum computing system 3800 provides a framework for providing a set of quantum computing sendees to one or more quantum computing clients. In some embodiments, the quantum computing system 3800 framework may be at least partially replicated in respective quantum computing clients. In these embodiments, an individual client may include some or all of the capabilities of the quantum computing system 3800, whereby the quantum computing system 3800 is adapted for the specific functions performed by the subsystems of the quantum computing client Additionally, or alternatively, in some embodiments, the quantum computing system 3800 may be implemented as a set of micro services, such that different quantum computing clients may leverage the quantum computing system 3800 via one or more APIs exposed to the quantum computing clients. In these embodiments, the quantum computing system 3800 may be configured to perform various types of quantum computing services that may be adapted for different quantum computing clients. In either of these configurations, a quantum computing client may provide a request to the quantum computing system 3800, whereby the request is to perform a specific task (e.g., an optimization). In response, the quantum computing system 3800 executes the requested task and returns a response to the quantum computing client.

[0390] Referring to FIG. 10, in some embodiments, the quantum computing system 3800 may include a quantum adapted services library 3802, a quantum general services library 3804, a quantum data services library 3806, a quantum computing engine library 3808, a quantum computing configuration service 3810, a quantum computing execution system 3812, and quantum computing API interface 3814.

[0391] In some embodiments, the quantum computing engine library 3808 includes quantum computing engine configurations 3816 and quantum computing process modules 3818 based on various supported quantum models. In some embodiments, the quantum computing system 3800 may support many different quantum models, including, but not limited to, the quantum circuit model, quantum Turing machine, adiabatic quantum computer, spintronic computing system (such as using spin-orbit coupling to generate spin-polarized electronic states in non-magneticsolids, such as ones using diamond materials), one-way quantum computer, quantum annealing, and various quantum cellular automata. Under the quantum circuit model, quantum circuits may be based on the quantum bit, or "qubit", which is somewhat analogous to the bit in classical computation. Qubits may be in a 1 or 0 quantum state or they may be in a superposition of the 1 and 0 states However, when qubits have measured the result of a measurement, qubits will always be in is always either a 1 or 0 quantum state. The probabilities related to these two outcomes depend on the quantum state that the qubits were in immediately before the measurement. Computation is performed by manipulating qubits with quantum logic gates, which are somewhat analogous to classical logic gates.

[0392] In some embodiments, the quantum computing system 3800 may be physically implemented using an analog approach or a digital approach. Analog approaches may include, but are not limited to, quantum simulation, quantum annealing, and adiabatic quantum computation. In some embodiments, digital quantum computers use quantum logic gates for computation. Both analog and digital approaches may use quantum bits, or qubits.

[0393] In some embodiments, the quantum computing system 3800 includes a quantum annealing module 3820 wherein the quantum annealing module may be configured to find the global minimum or maximum of a given objective function over a given set of candidate solutions (e. ., candidate states) using quantum fluctuations. As used herein, quantum annealing may refer to a meta-procedure for finding a procedure that identifies an absolute minimum or maximum, such as a size, length, cost, time, distance or other measure, from within a possibly very' large, but finite, set of possible solutions using quantum fluctuation-based computation instead of classical computation. The quantum annealing module 3820 may be leveraged for problems where the search space is discrete (e. ., combinatorial optimization problems) with many local minima, such as finding the ground state of a spin glass or the traveling salesman problem

[0394] In some embodiments, the quantum annealing module 3820 starts from a quantum-mechanical superposition of all possible states (candidate states) with equal weights. The quantum annealing module 3820 may then evolve, such as following the time-dependent Schrodinger equation, a natural quantum-mechanical evolution of systems (e.g. , physical systems, logical systems, or the like). In some embodiments, the amplitudes of all candidate states change, realizing quantum parallelism according to the time-dependent strength of the transverse field, which causes quantum tunneling between states. If the rate of change of the transverse field is slow enough, the quantum annealing module 3820 may stay close to the ground state of the instantaneous Hamiltonian. If the rate of change of the transverse field is accelerated, the quantum annealing module 3820 may leave the ground state temporarily but produce a higher likelihood of concluding in the ground state of the final problem energy state or Hamiltonian.

[0395] In some embodiments, the quantum computing system 3800 may include arbitrarily large numbers of qubits and may transport ions to spatially distinct locations in an array of ion traps, building large, entangled states via photonically connected networks of remotely entangled ion chains.

[0396] In some implementations, the quantum computing system 3800 includes a trapped ion computer module 3822, which may be a quantum computer that applies trapped ions to solve complex problems. Trapped ion computer module 3822 may have low' quantum decoherence and may be able to construct large solution states Ions, or charged atomic particles, may be confined and suspended in free space using electromagnetic fields. Qubits are stored in stable electronic states of each ion, and quantum information may be transferred through the collective quantized motion of the ions in a shared trap (interacting through the Coulomb force). Lasers may be applied to induce coupling between the qubit states (for single-qubit operations) or coupling between the internal qubit states and the external motional states (for entanglement between qubits).

[0397] In some embodiments of the invention, a traditional computer, including a processor, memory, and a graphical user interface (GUI), may be used for designing, compiling, and providing output from the execution and the quantum computing system 3800 may be used for executing the machine language instructions. In some embodiments of the invention, the quantum computing system 3800 may be simulated by a computer program executed by the traditional computer. In such embodiments, a superposition of states of the quantum computing system 3800 can be prepared based on input from the initial conditions. Since the initialization operation available in a quantum computer can only initialize a qubit to either the |0> or |1> state, initialization to a superposition of states is physically unrealistic. For simulation purposes, however, it may be useful to bypass the initialization process and initialize the quantum computing system 3800 directly.

[0398] In some embodiments, the quantum computing system 3800 provides various quantum data services, including quantum input filtering, quantum output filtering, quantum application filtering, and a quantum database engine.

[0399] In some embodiments, the quantum computing system 3800 may include a quantum input filtering service 3824. In some embodiments, quantum input filtering service 3824 may be configured to select whether to run a model on the quantum computing system 3800 or to run the model on a classic computing system. In some embodiments, quantum input filtering sendee 3824 may filter data for later modeling on a classic computer. In some embodiments, the quantum computing system 3800 may provide input to traditional compute platforms while filtering out unnecessary information from flowing into distributed systems. In some embodiments, the quantum computing system 3800 may trust through filtered specified experiences for intelligent agents

[0400] In some embodiments, a system in the system of systems may include a model or system for automatically determining, based on a set of inputs, whether to deploy quantum computational or quantum algorithmic resources to an activity, whether to deploy traditional computational resources and algorithms, or whether to apply a hybrid or combination of them. In some embodiments, inputs to a model or automation system may include demand information, supply information, financial data, energy cost information, capital costs for computational resources, development costs (such as for algorithms), energy costs, operational costs (including labor and other costs), performance information on available resources (quantum and traditional), and any of the many other data sets that may be used to simulate (such as using any of a wide variety of simulation techniques described herein and / or in the documents incorporated herein by refence) and / or predict tire difference in outcome between a quantum-optimized result and a non-quantum-optimized result. A machine learned model (including in a DPANN system) may be trained, such as by deep learning on outcomes or by a data set from human expert decisions, to determine what set of resources to deploy given the input data for a given request. The model may itself be deployed on quantum computational resources and / or may use quantum algorithms, such as quantum annealing, to determine whether, where and when to use quantum systems, conventional systems, and / or hybrids or combinations.

[0401] In some embodiments of the invention, the quantum computing system 3800 may include a quantum output filtering service 3826 In some embodiments, the quantum output filtering service 3826 may be configured to select a solution from solutions of multiple neural networks. For example, multiple neural networks may be configured to generate solutions to a specific problem and the quantum output filtering sendee 3826 may select the best solution from the set of solutions.

[0402] In some embodiments, the quantum computing system 3800 connects and directs a neural network development or selection process. In this embodiment, the quantum computing system 3800 may directly programthe weights of a neural network such that the neural network gives the desired outputs. This quantum-programmed neural network may then operate without the oversight of the quantum computing system 3800 but will still be operating within the expected parameters of the desired computational engine.

[0403] In some embodiments, the quantum computing system 3800 includes a quantum database engine 3828 In some embodiments, the quantum database engine 3828 is configured with in-database quantum algorithm execution. In some embodiments, a quantum query language may be employed to query the quantum database engine 3828. In some embodiments, the quantum database engine may have an embedded policy engine 3830 for prioritization and / or allocation of quantum workflows, including prioritization of query workloads, such as based on overall priority as well as the comparative advantage of using quantum computing resources versus others. In some embodiments, quantum database engine 3828 may assist with the recognition of entities by establishing a single identity for that is valid across interactions and touchpoints. The quantum database engine 3828 may be configured to perform optimization of data matching and intelligent traditional compute optimization to match individual data elements. The quantum computing system 3800 may include a quantum data obfuscation system for obfuscating data.

[0404] The quantum computing system 3800 may include, but is not limited to, analog quantum computers, digital computers, and / or error-corrected quantum computers. Analog quantum computers may directly manipulate the interactions between qubits without breaking these actions into primitive gate operations. In some embodiments, quantum computers that may run analog machines include, but are not limited to, quantum annealers, adiabatic quantum computers, and direct quantum simulators. The digital computers may operate by carrying out an algorithm of interest using primitive gate operations on physical qubits Error-corrected quantum computers may refer to a version of gate-based quantum computers made more robust through the deployment of quantum error correction (QEC), which enables noisy physical qubits to emulate stable logical qubits so that the computer behaves reliably for any computation. Further, quantum information products may include, but are not limited to, computing power, quantum predictions, and quantum inventions

[0405] In some embodiments, the quantum computing system 3800 is configured as an engine that may be used to optimize traditional computers, integrate data from multiple sources into a decision-making process, and the like. The data integration process may involve real-time capture and management of interaction data by a wide range of tracking capabilities, both directly and indirectly related to value chain network activities. In some embodiments, the quantum computing system 3800 may be configured to accept cookies, email addresses and other contact data, social media feeds, news feeds, event and transaction log data (including transaction events, network events, computational events, and many others), event streams, results of web crawling, distributed ledger information (including blockchain updates and state information), results from distributed or federated queries of data sources, streams of data from chat rooms and discussion forums, and many others.

[0406] In some embodiments, the quantum computing system 3800 includes a quantum register having a plurality of qubits. Further, the quantum computing system 3800 may include a quantum control system for implementing the fundamental operations on each of the qubits in the quantum register and a control processor for coordinating the operations required.

[0407] In some embodiments, the quantum computing system 3800 is configured to optimize the pricing of a set of goods or sendees In some embodiments, the quantum computing system 3800 may utilize quantum annealing to provide optimized pricing In some embodiments, the quantum computing system 3800 may use q-bit based computational methods to optimize pricing.

[0408] In some embodiments, the quantum computing system 3800 is configured to automatically discover smart contract configuration opportunities. Automated discovery of smart contract configuration opportunities may be based on published APIs to marketplaces and machine learning (e.g., by robotic process automation (RPA) of stakeholder, asset, and transaction types

[0409] In some embodiments, quantum-established or other blockchain-enabled smart contracts enable frequent transactions occurring among a network of parties, and manual or duplicative tasks are performed by counterparties for each transaction. The quantum-established or other blockchain acts as a shared database to provide a secure, single source of truth, and smart contracts automate approvals, calculations, and other transacting activities that are prone to lag and error Smart contracts may use software code to automate tasks, and in some embodiments, this software code may include quantum code that enables extremely sophisticated optimized results.

[0410] In some embodiments, the quantum computing system 3800 or other system in the system of systems may include a quantum-enabled or other risk identification module that is configured to perform risk identification and / or mitigation. The steps that may be taken by the risk identification module may include, but are not limited to, risk identification, impact assessment, and the like. In some embodiments, the risk identification module determines a risk type from a set of risk types. In some embodiments, risks may include, but are not limited to, preventable, strategic, and external risks Preventable risks may refer to risks that come from within and that can usually be managed on a rule-based level, such as employing operational procedures monitoring and employee and manager guidance and instruction. Strategy risks may refer to those risks that are taken on voluntarily to achieve greater rewards. External risks may refer to those risks that originate outside and are not in the businesses’ control (such as natural disasters) External risks are not preventable or desirable In some embodiments, the risk identification module can determine a predicted cost for many categories of risk. The risk identification module may perform a calculation of current and potential impact on an overall risk profile. In some embodiments, the risk identification module may determine the probability and significance of certain events. Additionally, or alternatively, the risk identification module may be configured to anticipate events.

[0411] In some embodiments, the quantum computing system 3800 or other system of the quantum computing system 3800 is configured for graph clustering analysis for anomaly and fraud detection.

[0412] In some embodiments, the quantum computing system 3800 includes a quantum prediction module, which is configured to generate predictions. Furthermore, the quantum prediction module may construct classical prediction engines to further generate predictions, reducing the need for ongoing quantum calculation costs, which, can be substantial compared to traditional computers

[0413] In some embodiments, the quantum computing system 3800 may include a quantum principal component analysis (QPCA) algorithm that may process input vector data if the covariance matrix of the data is efficiently obtainable as a density matrix, under specific assumptions about the vectors given in the quantum mechanical form. It may be assumed that the user has quantum access to the training vector data in a quantum memory. Further, it may be assumed that each training vector is stored in the quantum memory in terms of its difference from the class means These QPCA algorithms can then be applied to provide for dimension reduction using the calculational benefits of a quantum method.

[0414] In some embodiments, the quantum computing system 3800 is configured for graph clustering analysis for certified randomness for proof-of-stake blockchains. Quantum cryptographic schemes may make use of quantum mechanics in their designs, which enables such schemes to rely on presumably unbreakable laws of physics for theirsecurity. The quantum cryptography schemes may be information-theoretically secure such that their security is not based on any non-fundamental assumptions. In the design of blockchain systems, information-theoretic security is not proven. Rather, classical blockchain technology typically relies on security arguments that make assumptions about the limitations of attackers’ resources

[0415] In some embodiments, the quantum computing system 3800 is configured for detecting adversarial systems, such as adversarial neural networks, including adversarial convolutional neural networks. For example, the quantum computing system 3800 or other systems of the quantum computing system 3800 may be configured to detect fake trading patterns.

[0416] In some embodiments, the quantum computing system 3800 includes a quantum continual learning system, or QCL system 3832, wherein the QCL system 3832 learns continuously and adaptively about the external world, enabling the autonomous incremental development of complex skills and knowledge by updating a quantum model to account for different tasks and data distributions. The QCL system 3832 operates on a realistic time scale where data and / or tasks become available only during operation. Previous quantum states can be superimposed into the quantum engine to provide the capacity for QCL. Because the QCL system 3832 is not constrained to a finite number of variables that can be processed deterministically, it can continuously adapt to future states, producing a dynamic continual learning capability. The QCL system 3832 may have applications where data distributions stay relatively static, but where data is continuously being received. For example, the QCL system 3832 may be used in quantum recommendation applications or quantum anomaly detection systems where data is continuously being received and where the quantum model is continuously refined to provide for various outcomes, predictions, and the like QCL enables asynchronous alternate training of tasks and only updates the quantum model on the real-time data available from one or more streaming sources at a particular moment.

[0417] In some embodiments, the QCL system 3832 operates in a complex environment in which the target data keeps changing based on a hidden variable that is not controlled. In some embodiments, the QCL system 3832 can scale in terms of intelligence while processing increasing amounts of data and while maintaining a realistic number of quantum states. The QCL system 3832 applies quantum methods to drastically reduce the requirement for storage of historic data while allowing the execution of continuous computations to provide for detail-driven optimal results. In some embodiments, a QCL system 3832 is configured for unsupervised streaming perception data since it continually updates the quantum model with new available data.

[0418] In some embodiments, QCL system 3832 enables multi-modal-multi-task quantum learning. The QCL system 3832 is not constrained to a single stream of perception data but allows for many streams of perception data from different sensors and input modalities. In some embodiments, the QCL system 3832 can solve multiple tasks by duplicating the quantum state and executing computations on the duplicate quantum environment. A key advantage to QCL is that the quantum model does not need to be retrained on historic data, as the superposition state holds information relating to all prior inputs. Multi-modal and multi-task quantum learning enhance quantum optimization since it endows quantum machines with reasoning skills through the application of vast amounts of state information

[0419] In some embodiments, the quantum computing system 3800 supports quantum superposition, or the ability of a set of states to be overlaid into a single quantum environment.

[0420] In some embodiments, the quantum computing system 3800 supports quantum teleportation. For example, information may be passed between photons on chipsets even if the photons are not physically linked.

[0421] In some embodiments, the quantum computing system 3800 may include a quantum transfer pricing system. Quantum transfer pricing allows for the establishment of prices for the goods and / or services exchanged between subsidiaries, affiliates, or commonly controlled companies that are part of a larger enterprise and may be used to provide tax savings for corporations In some embodiments, solving a transfer pricing problem involves testing the elasticities of each system in the system of systems with a set of tests. In these embodiments, the testing may be done in periodic batches and then may be iterated. As described herein, transfer pricing may refer to the price that one division in a company charges another division in that company for goods and services.

[0422] In some embodiments, the quantum transfer pricing system consolidates all financial data related to transfer pricing on an ongoing basis throughout the year for all entities of an organization wherein the consolidation involves applying quantum entanglement to overlay data into a single quantum state. In some embodiments, the financial data may include profit data, loss data, data from intercompany invoices (potentially including quantities and prices'), and the like.

[0423] In some embodiments, the quantum transfer pricing system may interface with a reporting system that reports segmented profit and loss, transaction matrices, tax optimization results, and the like based on superposition data. In some embodiments, the quantum transfer pricing system automatically generates forecast calculations and assesses the expected local profits for any set of quantum states.

[0424] In some embodiments, the quantum transfer pricing system may integrate with a simulation system for performing simulations. Suggested optimal values for new product prices can be discussed cross-border via integrated quantum workflows and quantum teleportation communicated states

[0425] In some embodiments, quantum transfer pricing may be used to proactively control the distribution of profits within a multi-national enterprise (MNE), for example, during the course of a calendar year, enabling the entities to achieve arms-length profit ranges for each type of transaction.

[0426] In some embodiments, the QCL system 3832 may use a number of methods to calculate quantum transfer pricing, including the quantum comparable uncontrolled price (QCUP) method, the quantum cost plus percent method (QCPM), the quantum resale price method (QRPM), the quantum transaction net margin method (QTNM), and the quantum profit-split method.

[0427] The QCUP method may apply quantum calculations to find comparable transactions made between related and unrelated organizations, potentially through the sharing of quantum superposition data. By comparing the price of goods and / or services in an intercompany transaction with the price used by independent parties through the application of a quantum comparison engine , a benchmark price may be determined.

[0428] The QCPM method may compare the gross profit to the cost of sales, thus measuring the cost-plus mark-up (the actual profit earned from the products). Once this mark-up is determined, it should be equal to what a third party would make for a comparable transaction in a comparable context with similar external market conditions. In some embodiments, the quantum engine may simulate the external market conditions.

[0429] The QRPM method looks at groups of transactions rather than individual transactions and is based on the gross margin or difference between the price at which a product is purchased and the price at which it is sold to a third party. In some embodiments, the quantum engine may be applied to calculate the price differences and to record the transactions in the superposition system

[0430] The QTNM method is based on the net profit of a controlled transaction rather than comparable external market pricing. The calculation of the net profit is accomplished through a quantum engine that can consider a widevariety of factors and solve optimally for the product price. The net profit may then be compared with the net profit of independent enterprises, potentially using quantum teleportation.

[0431] The quantum profit-split method may be used when two related companies work on the same business venture, but separately In these applications, the quantum transfer pricing is based on profit The quantum profitsplit method applies quantum calculations to determine how the profit associated with a particular transaction would have been divided between the independent parties involved.

[0432] In some embodiments, the quantum computing system 3800 may leverage one or artificial networks to fulfill the request of a quantum computing client. For example, the quantum computing system 800 may leverage a set of artificial neural networks to identify patterns in images (e.g., using image data from a liquid lens system), perform binary matrix factorization, perform topical content targeting, perform similarity-based clustering, perform collaborative filtering, perform opportunity mining, or the like.

[0433] In some embodiments, the system of systems may include a hybrid computing allocation system for prioritization and allocation of quantum computing resources and traditional computing resources. In some embodiments, the prioritization and allocation of quantum computing resources and traditional computing resources may be measure-based (e.g., measuring the extent of the advantage of the quantum resource relative to other available resources), cost-based, optimality-based, speed-based, impact-based, or the like. In some embodiments the hybrid computing allocation system is configured to perform time-division multiplexing between the quantum computing system 3800 and a traditional computing system. In some embodiments, the hybrid computing allocation system may automatically track and report on the allocation of computational resources, the availability of computational resources, the cost of computational resources, and the like

[0434] In some embodiments, the quantum computing system 3800 may be leveraged for queue optimization for utilization of quantum computing resources, including context-based queue optimizations.

[0435] In some embodiments, the quantum computing system 3800 may support quantum-computation-aware location-based data caching.

[0436] In some embodiments, the quantum computing system 3800 may be leveraged for optimization of various system resources in the system of systems, including the optimization of quantum computing resources, traditional computing resources, energy resources, human resources, robotic fleet resources, smart container fleet resources, I / O bandwidth, storage resources, network bandwidth, attention resources, or the like.

[0437] The quantum computing system 3800 may be implemented where a complete range of capabilities are available to or as part of any configured service. Configured quantum computing services may be configured with subsets of these capabilities to perform specific predefined function, produce newly defined functions, or various combinations of both.

[0438] FIG. 11 illustrates quantum computing sendee request handling according to some embodiments of the present disclosure A directed quantum computing request 3902 may come from one or more quantum-aware devices or stack of devices, where the request is for known application configured with specific quantum instance(s), quantum computing engine(s), or other quantum computing resources, and where data associated with the request may be preprocessed or otherwise optimized for use with quantum computing.

[0439] A general quantum computing request 3904 may come from any system in the system of systems or configured service, where the requestor has determined that quantum computing resources may provide additional value or other improved outcomes. Improved outcomes may also be suggested by the quantum computing service inassociation with some form of monitoring and analysis. For a general quantum computing request 3904, input data may not be structured or formatted as necessarv for quantum computing.

[0440] In some embodiments, external data requests 3906 may include any available data that may be necessary for training new quantum instances The sources of such requests could be public data, sensors, ERP systems, and many others

[0441] Incoming operating requests and associated data may be analyzed using a standardized approach that identifies one or more possible sets of known quantum instances, quantum computing engines, or other quantum computing resources that may be applied to perform the requested operation's ). Potential existing sets may be identified in the quantum set library 3908.

[0442] In some embodiments, the quantum computing system 3800 includes a quantum computing configuration service 3810. The quantum computing configuration service may work alone or with the intelligence service 3834 to select a best available configuration using a resource and priority analysis that also includes the priority of the requestor. The quantum computing configuration service may provide a solution (YES) or determine that a new configuration is required (NO).

[0443] In one example, the requested set of quantum computing services may not exist in the quantum set library' 3908. In this example, one or more new quantum instances must be developed (trained) with the intelligence service 3834 using available data. In some embodiments, alternate configurations may be developed with assistance from the intelligence service 3834 to identify alternate ways to provide all or some of the requested quantum computing sendees until appropriate resources become available For example, a quantum / traditional hybrid model may be possible that provides the requested service, but at a slower rate

[0444] In some embodiments, alternate configurations may be developed with assistance from the intelligence service 3834 to identify alternate and possibly temporary ways to provide all or some of the requested quantum computing services. For example, a hybrid quantum / traditional model may be possible that provides the requested service, but at a slower rate. This may also include a feedback learning loop to adjust services in real time or to improved stored library elements.

[0445] When a quantum computing configuration has been identified and available, it is allocated and programmed for execution and delivery of one or more quantum states (solutions).BIOLOGY-BASED SYSTEMS, METHODS, KITS, AND APPARATUSES

[0446] FIGS. 12 and 13 together show a thalamus service 4000 and a set of input sensors streaming data from various sources across a central control system 4002 with its centrally-managed data sources 4004. The thalamus service 4000 filters the into the central control system 4002 such that the control system is never overwhelmed by the total volume of information. In some embodiments, the thalamus sendee 4000 provides an information suppression mechanism for information flows within the system. This mechanism monitors all data streams and strips away irrelevant data streams by ensuring that the maximum data flows from all input sensors are always constrained

[0447] The thalamus service 4000 may be a gateway for all communication that responds to the prioritization of the central control system 4002. The central control system 4002 may decide to change the prioritization of the data streamed from the thalamus service 4000, for example, during a known fire in an isolated area, and the event may direct the thalamus service 4000 to continue to provide flame sensor information despite the fact that majority of this data is not unusual The thalamus service 4000 may be an integral part of the overall system communication framework.

[0448] In some embodiments, the thalamus service 4000 includes an intake management system 4006. The intake management system 4006 may be configured to receive and process multiple large datasets by converting them into data streams that are sized and organized for subsequent use by a central control system 4002 operating within one or more systems For example, a robot may include vision and sensing systems that are used by the central control system 4002 to identify and move through an environment in real time. The intake management system 4006 can facilitate robot decision-making by parsing, filtering, classifying, or otherwise reducing the size and increasing the utility of multiple large datasets that would otherwise overwhelm the central control system 4002. In some embodiments, the intake management system may include an intake controller 4008 that works with an intelligence service 4010 to evaluate incoming data and take actions-based evaluation results. Evaluations and actions may include specific instruction sets received by the thalamus service 4000, for example the use of a set of specific compression and prioritization tools stipulated within a “Networking" library module. In another example, thalamus service inputs may direct the use of specific filtering and suppression techniques In a third example, thalamus service inputs may stipulate data filtering associated with an ...

Claims

1. CLAIMS1. A configured artificial intelligence system comprising: an intelligence system including an intelligence controller and a system of models architecture featuring a plurality of intelligence models; wherein the intelligence controller is configured to receive task requests, analyze task complexity, decompose tasks into manageable subtasks, and dynamically select appropriate models from the plurality of intelligence models to execute each subtask based on model suitability and performance characteristics: wherein the plurality of intelligence models encompasses diverse model architectures including one or more of large language models, audio models, visual models, classification models, and foundation or multimodal models; a scoring system configured to generate know-your-model scores that quantify suitability for specific tasks of each model; a model execution system configured to provide standardized execution environment for the plurality of intelligence models; a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by the plurality of intelligence models and use outcome data as feedback to reinforce model performance; and a governance and analysis system configured to ensure model operations comply with governance standards.2 The configured artificial intelligence system of claim 1, wherein the intelligence controller implements orchestration algorithms that coordinate simultaneous execution of multiple models within the plurality of intelligence models for parallel processing of different aspects of complex tasks.

3. The configured artificial intelligence system of claim 1, wherein the system of models architecture enables hybrid model topologies including a set of competing model configurations, wherein multiple models process identical inputs simultaneously and outputs are evaluated through automated scoring mechanisms.

4. The configured artificial intelligence system of claim 3, wherein the set of competing model configurations includes ensemble voting mechanisms that combine outputs from multiple models through structured decisionmaking processes including majority voting and weighted consensus mechanisms.

5. The configured artificial intelligence system of claim 1, wherein the system of models architecture supports hierarchical hybrid topologies that process tasks through multiple organizational levels with different models operating at local subsystem levels and integrator models reasoning over combined outputs at higher system levels.

6. The configured artificial intelligence system of claim 1, wherein the system of models architecture enables federated hybrid topologies that coordinate collaborative model operation across distributed environments while maintaining data privacy and security boundaries.7 The configured artificial intelligence system of claim 1, further comprising a hardware abstraction layer that provides unified interface for coordinating Al model execution across heterogeneous hardware platforms while abstracting underlying complexity of different chipset architectures.

8. The configured artificial intelligence system of claim 7, wherein the hardware abstraction layer includes a model-to-hardware mapping module that analyzes model characteristics and computational requirements to determine optimal hardware assignments for different Al models.

9. The configured artificial intelligence system of claim 7, wherein the hardware abstraction layer includes a resource management module that implements dynamic resource allocation and performance optimization across hardware acceleration components and specialized processing units.10 The configured artificial intelligence system of claim 9, wherein the hardware acceleration components include at least one of neural processing units, tensor processing units, graphics processing units, FPGA-based adaptive accelerators, or physics simulation and co-processor chips.

11. The configured artificial intelligence system of claim 1 , wherein the configured artificial intelligence system supports 3D chipset and chiplet architectures that incorporate vertically stacked processing layers to increase computational throughput per unit area while reducing interconnect distances between processing elements.

12. The configured artificial intelligence system of claim 1, further comprising a digital twin system configured to create and maintain digital twins, wherein the training and reinforcement system interfaces with the digital twin system to monitor outcomes from model decisions made within digital twin environments.

13. The configured artificial intelligence system of claim 1 , wherein the governance and analysis system implements longitudinal tracking capabilities that provide continuous visibility into model performance, decisionmaking processes, and operational effectiveness over extended time periods.

14. The configured artificial intelligence system of claim 1, wherein the training and reinforcement system supports continuous model development through automated retraining capabilities that incorporate optimization techniques including parameter adjustments, hyperparameter adjustments, and augmented data for new training.15 The configured artificial intelligence system of claim 1, wherein the training and reinforcement system implements automation of evolutionary model refinement through genetic programming techniques that automatically generate, test, and refine model architectures and parameters.

16. The configured artificial intelligence system of claim 1, configured as a modular system of models in a box that packages complete intelligence system components into a standardized enterprise solution capable of seamless integration with existing organizational technology infrastructure.

17. The configured artificial intelligence system of claim 16, wherein the modular system of models implements comprehensive enterprise integration capabilities that enable connectivity with at least one of customer relationship management systems, enterprise resource planning systems, or electronic medical record software through standardized APIs.

18. A system-of-models system comprising: an intelligence controller configured to coordinate execution of multiple artificial intelligence models; a plurality of intelligence models including large language models, visual models, and classification models; a hardware abstraction layer including a model-to-hardware mapping module and a resource management module; and hardware acceleration components including at least one of neural processing units, tensor processing units, or graphics processing units; wherein the intelligence controller implements hybrid model topologies including competing model configurations that process identical inputs through multiple models simultaneously and evaluate outputs through automated scoring mechanisms;wherein the model-to-hardware mapping module analyzes model computational requirements to determine optimal hardware assignments: and wherein the system-of-models system enables dynamic switching between different hybrid topologies based on changing task requirements and performance optimization needs19. The system-of-models system of claim 18, wherein the hybrid model topologies include ensemble voting mechanisms that aggregate individual model contributions into collective decisions through weighted voting that considers each model's confidence scores and historical performance metrics.

20. A method for coordinating artificial intelligence model execution comprising: receiving, at an intelligence controller, a request to perform a task; analyzing task complexity and decomposing the task into manageable subtasks; dynamically selecting appropriate models from a plurality of intelligence models based on model suitability scores; implementing hybrid model topologies including competing model configurations that process identical inputs through multiple models simultaneously; evaluating outputs from the multiple models through automated scoring mechanisms; mapping selected models to hardware acceleration components through a model-to-hardware mapping module; coordinating parallel execution of the selected models across the hardware acceleration components; and dynamically switching between different hybrid topologies based on real-time assessment of task complexity and performance requirements21. An Al-based platform for enabling intelligent orchestration and management of distributed energy resources, comprising: an adaptive energy data pipeline configured to communicate data across a set of nodes in a network, each node being adapted to operate on an energy data set associated with at least one of energy generation, energy storage, energy delivery, or energy consumption; and a KYM system including a processor and memory and configured to execute KYM Al -based learning models for model lifecycle management of energy optimization models; wherein the adaptive energy data pipeline is configured to filter, compress, transform, error correct and route energy data sets based on at least one of network conditions, data size, data granularity, or data content22. The Al -based platform of claim 21, wherein the adaptive energy data pipeline is configured to match communication value with quality-of-service needs of routes associated with energy-related communications.

23. The Al -based platform of claim 21, wherein the KYM system includes a model evaluation and risk assessment scoring system configured to generate quantitative scores for foundational properties, task performance, and safety management.24 The Al-based platform of claim 21, wherein the adaptive energy data pipeline is configured to prioritize occurrence and frequency of communications based on matching value with quality-of-service needs.

25. The Al-based platform of claim 21 , further comprising intelligent data layers configured to produce at least one of energy generation data layers, energy storage data layers, energy delivery data layers, or energy consumption data layers.

26. The Al-based platform of claim 21, wherein the adaptive energy data pipeline is configured to identify and use least-cost routes for communications and switch to higher-cost routes to meet quality-of-service needs.

27. The Al-based platform of claim 21, wherein the KYM system is configured to perform at least one of model intake and registration actions, model evaluation and risk assessment actions, or model deployment actions for energy optimization models.

28. The Al-based platform of claim 21 , wherein the adaptive energy data pipeline includes quality-of-service needs for communicating reports of at least one of energy generation, energy storage, energy transport, or energy consumption occurrences.

29. The Al -based platform of claim 21, wherein the adaptive energy data pipeline is configured to forecast energy demands and adjust data transmission processes accordingly.

30. The Al-based platform of claim 25, wherein the intelligent data layers are configured to perform at least one of extraction, transformation, loading, normalization, cleansing, compression, route selection, protocol selection, self-organization of storage, filtering, timing of transmission, encoding, or decoding.

31. An Al-based platform for adaptive energy data management, comprising: an adaptive energy data pipeline configured to communicate data across a set of nodes in a network, each node being adapted to operate on an energy data set associated with at least one of energy generation, energy storage, energy delivery, or energy consumption; a system of models architecture including an intelligence controller configured to coordinate energy optimization models; configurable data and intelligence modules configured to access data from various sources throughout the Al-based platform; and a cross-service resource optimization system including one or more Al agents trained to manage, configure, deploy, provision, and optimize subsystems operating within a linked system; wherein the system of models architecture includes at least one of model execution systems or model interface systems for coordinating multiple Al models.

32. The Al-based platform of claim 31, wherein the adaptive energy data pipeline is configured to prioritize data transmission from energy storage systems during predicted demand spikes.

33. The Al -based platform of claim 31, wherein the configurable data and intelligence modules provide at least one of batches, files, database reports, event logs, or data streams as configured outputs.

34. The Al-based platform of claim 31, wherein the intelligence controller is configured to receive task requests, analyze complexity, decompose into subtasks, and dynamically select appropriate energy optimization models.

35. The Al -based platform of claim 31 , wherein the cross-sendee resource optimization system is configured to measure and allocate energy used across platforms including at least one of batter;’ storage by devices, energy use by GPUs in cloud computing, or energy use by data centers for generative Al workloads36. The Al-based platform of claim 31 , further comprising at least one of distributed ledger and smart contract systems configured to ensure secure and transparent transactions, or energy simulation systems configured to model potential energy scenarios.

37. The Al-based platform of claim 31 , wherein the adaptive energy data pipeline includes network topology adaptation capabilities based on parameters of the network.

38. The Al-based platform of claim 31, wherein the system of models architecture includes training and reinforcement systems configured to monitor outcomes and provide feedback for model improvement.

39. The Al-based platform of claim 31 , wherein the configurable data and intelligence modules are configured to be automatically generated and pushed to other systems or queried and pulled from distributed databases40. A method for adaptive energy data pipeline management, comprising: communicating data across nodes in a network using an adaptive energy data pipeline, wherein each node operates on energy data sets; executing KYM Al-based learning models to manage lifecycle of energy optimization models: filtering, compressing, and routing energy data sets based on at least one of network conditions or data content; and coordinating energy optimization models using a system of models architecture.

41. An Al-based platform for enabling intelligent orchestration and management of distributed energy resources, comprising: a set of intelligence enablement systems including at least one of intelligent data layers, distributed ledger and smart contract systems, adaptive energy digital twin systems, or energy simulation systems; and a plurality of Al-based energy orchestration, optimization, and automation systems including at least two of energy generation orchestration systems, energy consumption orchestration systems, energy marketplace orchestration systems, energy delivery orchestration systems, or energy storage orchestration systems; wherein the set of intelligence enablement systems is configured to utilize algorithms and computational tools to parse datasets, perform pattern recognition, and / or perform informed decision-making42. The Al-based platform of claim 41 , wherein the intelligent data layers are configured to manage and process information for energy-relevant tasks including at least one of prediction, forecasting, optimization, automated discovery-', configuration, execution of energy transactions, monitoring, tracking, or automated generation of energy-related content.

43. The Al -based platform of claim 41, wherein the distributed ledger and smart contract systems are configured to ensure secure and transparent transactions and data management for energy-related operations.

44. The Al-based platform of claim 41, wherein the adaptive energy digital twin systems are configured to create virtual replicas of physical energy assets for at least one of monitoring or optimization.

45. The Al-based platform of claim 41, wherein the energy simulation systems are configured to model potential energy scenarios to aid in decision-making.

46. The Al-based platform of claim 41, further comprising a KYM system configured to manage Al model deployment and monitoring for energy applications.

47. The Al-based platform of claim 41, wherein the energy generation orchestration systems are configured to manage and coordinate energy production sources.48 The Al-based platform of claim 41 , wherein the energy consumption orchestration systems are configured to oversee and optimize how energy is used.

49. The Al-based platform of claim 41 , wherein the energy marketplace orchestration systems are configured to facilitate energy trading and transactions.

50. The Al-based platform of claim 41 , wherein the energy delivery orchestration systems are configured to ensure efficient and reliable energy distribution.

51. An Al-based intelligence enablement platform, comprising : a set of intelligence enablement systems including at least one of intelligent data layers, distributed ledger and smart contract systems, adaptive energy digital twin systems, or energy simulation systems; configurable data and intelligence modules including at least one of energy transaction enablement systems, stakeholder energy digital twins, or data integrated microservices; a cross-service resource optimization system including Al agents trained to manage, configure, deploy, provision, and optimize subsystems operating within a linked system; a governance and analysis system configured to ensure compliance with established governance standards; and a training and reinforcement system configured to monitor outcomes relating to decisions or predictions made by models; wherein the cross- service resource optimization system is configured to measure and allocate energy used across platforms including at least one of battery storage by devices, energy use by GPUs in cloud computing, or energy use by data centers for generative Al workloads.

52. The Al-based intelligence enablement platform of claim 51, wherein the energy transaction enablement systems are configured to facilitate and streamline energy-related transactions.

53. The Al-based intelligence enablement platform of claim 51, wherein the stakeholder energy digital twins provide virtual representations of stakeholder-specific energy assets for at least one of monitoring or management.54 The Al-based intelligence enablement platform of claim 51, wherein the data integrated microservices enable configured stakeholder energy edge solutions ensuring integrated energy management approaches55. The Al-based intelligence enablement platform of claim 51, wherein the governance and analysis system implements monitoring and analytics frameworks that provide visibility into Al system behavior through at least one of real-time data collection, analysis, or reporting capabilities.

56. The Al-based intelligence enablement platform of claim 51, wherein the training and reinforcement system supports various types of learning models including at least one of supervised learning, unsupervised learning, semi-supervised learning, deep learning, regression, decision tree, or random forest models.

57. The Al-based intelligence enablement platform of claim 51, further comprising a reporting system configured to provide comprehensive documentation and transparency of model outcomes over extended operational periods.

58. The Al-based intelligence enablement platform of claim 51, wherein the governance and analysis system is configured to monitor model behavior, validate outputs against policy requirements, and implement safeguards.

59. The Al-based intelligence enablement platform of claim 51, wherein the training and reinforcement system is configured to use feedback data to reinforce model performance through iterative improvement processes.

60. A method for intelligence enablement in energy management, comprising: utilizing intelligence enablement systems as cognitive backbone with advanced algorithms for energy management; orchestrating energy operations using Al-based systems for at least two of energy generation, energy consumption, energy marketplace operations, energy delivery, or energy storage; optimizing cross-service resources using Al agents trained to manage and optimize subsystems; and ensuring compliance through governance and analysis systems.

61. An Al-based platform for enabling intelligent orchestration and management of distributed energy resources, comprising: a digital twin platform configured to provide an environment for decision making with adaptive energy digital twins representing energy stakeholder entities; and a decision making framework for distributing authority among at least one of human beings, human- Al systems, or autonomous Al systems; wherein the decision making framework is selected from at least one of hierarchical, rules-based, simulation, enterprise planning, algorithmic, principles-based, collaborative, peer-to-peer, or competitive frameworks.

62. The Al-based platform of claim 61, wherein the digital twin platform includes an interface system for designating trainers for Al system creation and a system for displaying training metrics.

63. The Al-based platform of claim 61 , wherein the adaptive energy digital twins are configured to represent at least one of energy stakeholder entities, energy distribution resources, stakeholder information technology, networking infrastructure entities, energy-dependent stakeholder production facilities, stakeholder transportation systems, market conditions, or energy usage priority conditions.

64. The Al-based platform of claim 61, further comprising a KYM system integrated with the digital twin platform to generate digital twin representations of Al models subject to model intake and registration processes.

65. The Al -based platform of claim 61, wherein the digital twin platform includes an embedded intelligent agent system for discovering available systems among at least one of human systems, combined human- Al systems, or standalone artificial intelligence systems66. The Al-based platform of claim 61 , wherein the adaptive energy digital twins are configured to provide at least one of visual indicators of energy consumption by energy consumers, analytic indicators of energy consumption, filtering of energy data, highlighting of energy data, or adjustment of energy data.

67. The Al-based platform of claim 64, wherein the KYM system is configured to analyze intake and registration data and generate digital twin representations that model at least one of Al model behavior, performance characteristics, or operational parameters.

68. The Al-based platform of claim 61, wherein the decision making framework is configured to distribute authority based on contextual factors including at least one of time of day, workforce availability, market data, or environmental data.

69. The Al-based platform of claim 61, wherein the adaptive energy digital twins are configured to generate visual and analytic indicators of energy consumption by at least one of machines, factories, or vehicles in vehicle fleets.

70. The Al-based platform of claim 61 , wherein the digital twin platform is configured to run simulations receiving parameters and executing simulations using libraries that model behaviors of different types of systems71 An Al-based digital twin and simulation platform, comprising: a digital twin platform configured to provide an environment for decision making with adaptive energy digital twins representing energy stakeholder entities and configured to create, maintain, and interrogate digital twins with data visualization features;one or more energy simulation systems configured to model potential energy scenarios including simulation environments that simulate outcomes of algorithms governing at least one of generation, consumption, or storage; a training and reinforcement system configured to interface with the digital twin platform to monitor outcomes relating to decisions or predictions made by models within digital twin environments; a data services system configured to provide at least one of data lakes or data pools maintained for training and reinforcement of models; and a governance and analysis system configured to ensure actions comply with governance standards throughout feedback processes; wherein the one or more energy simulation systems are configured to simulate interaction of non- controllable loads and optimized charging processes.

72. The Al-based digital twin and simulation platform of claim 71 , wherein the digital twin platform incorporates data from real- world entities that are twinned providing comprehensive comparison capabilities between simulated and actual performance results.

73. The Al-based digital twin and simulation platform of claim 71, wherein the training and reinforcement system is configured to collect outcome data from model predictions and decisions, analyze at least one of success or failure, and use feedback to adjust model parameters or retrain models.

74. The Al-based digital twin and simulation platform of claim 71 , wherein the digital twin platform provides at least one of output to, integration with, or data sharing with adaptive energy digital twin systems75 The Al-based digital twin and simulation platform of claim 71 , wherein the one or more energy simulation systems enable simulation of various outcomes for renewable energy transitions including at least one of solar panel responses to weather conditions, wind turbine operations during seasons, or energy storage management during peak demand76. The Al-based digital twin and simulation platform of claim 71, wherein the data services system includes data augmentation services with capabilities for at least one of normalizing data sets, synthesizing data for inclusion in data sets, or anonymizing data while preserving longitudinal tracking capabilities.

77. The Al-based digital twin and simulation platform of claim 71, wherein the governance and analysis system implements at least one of intrinsic governance monitoring general operations or extrinsic governance monitoring proposed outputs.

78. The Al-based digital twin and simulation platform of claim 71 , further comprising an intelligence controller configured to coordinate execution of model decisions within digital twin environments while monitoring intermediate subtasks and aggregating results.

79. The Al-based digital twin and simulation platform of claim 71 , wherein the governance and analysis system implements decision-making algorithms that consider multiple factors including at least one of severity levels, historical patterns, or potential impact assessments based on longitudinal performance data80. A method for digital twin and simulation-based energy management, comprising: creating and maintaining digital twins with data visualization features for energy stakeholder entities; modeling potential energy scenarios using simulation environments that simulate algorithm outcomes; monitoring outcomes from digital twin environments and using feedback data to reinforce model performance;distributing decision-making authority among at least one of human beings, human- Al systems, or autonomous Al systems; and ensuring compliance with governance standards through at least one of intrinsic or extrinsic governance monitoring81. A cross-service resource optimization system for energy management, comprising: a plurality of Al agents trained to manage, configure, deploy, provision, and / or optimize subsystems operating within a linked system; and a converged workflow orchestration system including Al algorithms for transaction monitoring and machine learning techniques for risk assessment; wherein the cross-service resource optimization system is configured to measure and allocate energy used across platforms including at least one of battery storage by devices, energy use by GPUs in cloud computing, or energy use by data centers for generative Al workloads.

82. The cross-service resource optimization system of claim 81 , wherein the plurality of Al agents is configured to coordinate distributed energy assets and balance system-wide energy consumption.

83. The cross-service resource optimization system of claim 81, wherein the converged workflow orchestration system includes at least one of robotics and process automation for repetitive tasks or blockchain technology for transaction recording84. The cross-service resource optimization system of claim 81, further comprising automated governance modules for policy automation and regulatory' framework monitoring85 The cross-service resource optimization system of claim 81 , wherein the cross-service resource optimization system implements at least one of deep neural networks for pattern recognition, predictive analytics, natural language processing for transaction documents, or cloud computing infrastructure.

86. The cross-service resource optimization system of claim 81, wherein the plurality of Al agents implement resource optimization frameworks to coordinate distributed energy assets.

87. The cross-service resource optimization system of claim 81, further comprising API integrations for system communication and real-time monitoring capabilities.

88. The cross-service resource optimization system of claim 81, wherein the converged workflow orchestration system provides capabilities for at least one of automated edge transaction orchestration, adjustment of transaction parameters, monitoring of marketplace conditions, or analysis of sensor data from energy entities.

89. The cross-service resource optimization system of claim 81 , wherein the cross-service resource optimization system includes resource optimization modules that provide at least one of real-time monitoring capabilities, predictive analytics, automated control systems, resource allocation optimization, or automated execution of optimization strategies.

90. The cross-service resource optimization system of claim 81 , wherein the plurality of Al agents are configured to optimize computational resource allocation and manage energy resources across subsystems91. An Al-based enterprise transactional decision support system, comprising: a cross-service resource optimization system for energy management, including:Al agents trained to manage, configure, deploy, provision, and optimize subsystems operating within a linked system; anda converged workflow orchestration system including Al algorithms for transaction monitoring and machine learning techniques for risk assessment; one or more strategic energy resource planning and / or transaction planning simulation modules; one or more sets of digital twins and a set of intelligent dashboards configured to integrate operational data; and one or more automated governance systems configured to facilitate energy transactions and operations including distributed energy resource governance and energy grid governance capabilities; wherein the Al-based enterprise transactional decision support system implements energy edge convergence capabilities including automated governance of energy transactions and Al-based enterprise decision support.

92. The Al-based enterprise transactional decision support system of claim 91, wherein the energy edge convergence capabilities include simulation and modeling tools for complex energy scenarios.

93. The Al-based enterprise transactional decision support system of claim 91, wherein the set of intelligent dashboards provides real-time visualization of energy operations and performance metrics.

94. The Al-based enterprise transactional decision support system of claim 91, wherein the one or more sets of digital twins integrate operational data from multiple sources to provide comprehensive system representations.

95. The Al-based enterprise transactional decision support system of claim 91 , wherein the one or more automated governance systems include at least one of policy automation systems, regulatory compliance monitoring, transaction orchestration, or workflow optimization96 The Al-based enterprise transactional decision support system of claim 91 , wherein the Al-based enterprise transactional decision support system implements at least one of intelligent edge networking, context- aware sensor fusion, analytics integration, Al classification systems, or optimization systems.

97. The Al-based enterprise transactional decision support system of claim 91 , wherein the one or more automated governance systems implement at least one of Al-based decision support, strategic planning capabilities, marketplace simulation, operational data integration, or digital twin modeling.

98. The Al-based enterprise transactional decision support system of claim 91 , further comprising context- aware sensor fusion systems for energy management with data fusion capabilities for marketplace data and operational data integration.

99. The Al-based enterprise transactional decision support system of claim 91 , wherein the Al-based enterprise transactional decision support system provides joint optimization of energy and computation with computation resource management and energy resource management.

100. A method for cross-service resource optimization in energy systems, comprising: managing and optimizing subsystems using Al agents trained for linked system operations; orchestrating converged workflows with Al algorithms for transaction monitoring; measuring and allocating energy used across platforms including battery storage and cloud computing; implementing automated governance for energy transactions and operations; and providing enterprise decision support through strategic planning and intelligent dashboards.

101. A virtual power plant system for distributed energy resource management, comprising:one or more aggregation and management modules for heterogeneous energy resources including at least one of solar plants, battery storage systems, wind turbines, electric vehicle charging stations, demand response management centers, or smart meters; and a DER market orchestration system including at least one of market forming systems, market demand systems, market response systems, or market value analysis systems; wherein the virtual power plant system is configured to manage small, isolated power generation points used for load-leveling and to absorb excess supply from intermittent renewables.

102. The virtual power plant system of claim 101, wherein the virtual power plant system is configured to deliver supply during shortages and manage load-leveling operations103. The virtual power plant system of claim 101, wherein the DER market orchestration system includes energy marketplaces based on at least one of type of energy or location of energy.

104. The virtual power plant system of claim 101, further comprising transaction aggregation systems configured to automatically orchestrate energy-related transactions for at least one of energy generation, energy storage, energy delivery, energy consumption, renewable energy credits, carbon abatement credits, or pollution abatement credits.

105. The virtual power plant system of claim 101, wherein the market demand systems include transaction aggregation capabilities for automated energy transaction orchestration.

106. The virtual power plant system of claim 101, further comprising a DER market interface configured to broadcast information regarding current and forecasted energy capacity and pricing107 The virtual power plant system of claim 101 , wherein the market forming systems are configured to establish and maintain energy trading environments.

108. The virtual power plant system of claim 101 , wherein the market response systems are configured to coordinate responses to energy demand fluctuations.

109. The virtual power plant system of claim 101, wherein the market value analysis systems are configured to assess and optimize energy transaction values110. The virtual power plant system of claim 101 , wherein the virtual power plant system includes market orchestration assist layers configured to coordinate multiple DER providers and DER clients.

111. A comprehensive energy marketplace orchestration platform, comprising: a virtual power plant system for distributed energy resource management, including: aggregation and management capabilities for multiple heterogeneous energy resources including at least one of solar plants, battery storage systems, wind turbines, electric vehicle charging stations, demand response management centers, or smart meters; and a DER market orchestration layer including at least one of market forming systems, market demand systems, market response systems, or market value analysis systems; a futures market optimization system configured to automatically orchestrate aggregation of futures markets contracts based on forecast of future energy needs; an energy trading systems intelligence platform including at least one of neural networks for automated energy trading, deep learning models for price prediction, natural language processing systems for market analysis, or reinforcement learning algorithms for trading optimization; andat least one set of distributed ledger and smart contract systems configured to enable energy-related transactions including at least one of purchases, sales, leases, futures contracts, renewable energy credits, carbon abatement credits, pollution abatement credits, leasing of assets, shared economy transactions, shared consumption contracts, bulk purchases, or provisioning of mobile resources; wherein the futures market optimization system generates forecasts using at least one of machine learning on outcomes, human output, or human-labeled data.

112. The comprehensive energy marketplace orchestration platform of claim 111, wherein the futures market optimization system is configured to design, configure, and execute series of futures market transactions across various jurisdictions.

113. The comprehensive energy marketplace orchestration platform of claim 111, wherein the energy trading systems intelligence platform implements automated trading algorithms with real-time market analysis.

114. The comprehensive energy marketplace orchestration platform of claim 111, wherein the at least one set of distributed ledger and smart contract systems ensures secure and transparent transaction processing115. The comprehensive energy marketplace orchestration platform of claim 111, wherein the futures market optimization system bases forecasts on at least one of historical usage patterns, current operating conditions, current market conditions, or anticipated operational needs.

116. The comprehensive energy marketplace orchestration platform of claim 111, further comprising energy transaction intelligent agents configured to at least one of discover counterparties, discover arbitrage opportunities, design smart contracts, generate smart contracts, deploy smart contracts, optimize transaction parameters, recommend contract execution steps, or resolve contracts upon completion117. The comprehensive energy marketplace orchestration platform of claim 111 , wherein the energy trading systems intelligence platform includes predictive models for market trend analysis and automated decision-making.

118. The comprehensive energy marketplace orchestration platform of claim 111, wherein the at least one set of distributed ledger and smart contract systems provides immutable transaction records and automated contract execution.

119. The comprehensive energy marketplace orchestration platform of claim 111, further comprising market broadcast and poll systems for providing information regarding current and forecasted energy capacity and pricing.

120. A method for virtual power plant and market orchestration, comprising : aggregating and managing multiple heterogeneous energy resources using virtual power plant systems; orchestrating energy markets using DER market orchestration layers with market forming, demand, response, and value analysis systems; optimizing futures market contracts based on forecasted energy needs; implementing automated energy trading using neural networks and deep learning models; and enabling secure energy transactions using distributed ledger and smart contract systems.121 A chipset system for systems of models enabling Al model execution, comprising: a hardware abstraction layer containing at least one of model-to-hardware mapping modules or resource management modules; a plurality of hardware acceleration components including at least one of a neural processing unit, a tensor processing unit, a graphics processing unit, a FPGA-based adaptive accelerator, or a physics simulation and coprocessor chip; anda specialized processing unit including at least one of an embedded microcontroller, a real-time control unit, a sensor fusion processor, a trusted platform module, a hardware security modules, or an edge Al system-on- chip implementation; wherein the hardware abstraction layer provides unified interface for coordinating Al model execution across heterogeneous hardware platforms.

122. The chipset system of claim 121, wherein the model-to-hardware mapping modules implement algorithms that analyze model characteristics and computational requirements to determine optimal hardware assignments123. The chipset system of claim 121, wherein the resource management modules implement dynamic resource allocation and performance optimization capabilities.

124. The chipset system of claim 121, wherein the neural processing unit provides dedicated acceleration for neural network inference operations with specialized instruction sets.

125. The chipset system of claim 121, wherein the tensor processing unit offers optimized acceleration for tensor operations that form computational foundation of transformer models.

126. The chipset system of claim 121, wherein the graphics processing unit leverages massively parallel architecture to accelerate training and inference operations for visual models and LLMs127. The chipset system of claim 121, wherein the FPGA-based adaptive accelerator enables reconfigurable hardware optimization for specific model requirements128. The chipset system of claim 121, wherein the embedded microcontroller enables deployment of Al models in resource-constrained environments with low power consumption requirements129 The chipset system of claim 121 , wherein the sensor fusion processor provides specialized capabilities for integrating and processing data from multiple sensor types simultaneously.

130. The chipset system of claim 121, wherein the trusted platform module implements cryptographic acceleration and secure execution environments for Al models and sensitive data.

131. An advanced 3D chipset system for transformer model optimization, comprising : a chipset architecture for systems of models enabling Al model execution, including: a hardware abstraction layer containing at least one of model-to-hardware mapping modules or resource management modules; hardware acceleration components including at least one of neural processing units, tensor processing units, graphics processing units, FPGA-based adaptive accelerators, or physics simulation and coprocessor chips; and specialized processing units including at least one of embedded microcontrollers and real-time control units, sensor fusion processors, trusted platform modules and hardware security modules, or edge Al system- on-chip implementations; a 3D chipset implementation incorporating vertically stacked processing layers that increase computational throughput per unit area; a chiplet architecture that enables modular hardware designs where different processing capabilities are combined based on application requirements; a high-bandwidth memory integration providing direct access to large memory pools with minimal latency; anda chiplet interconnect module implementing high-speed, low-latency communication pathways between processing elements; wherein the advanced 3D chipset system implements advanced thermal management systems including at least one of integrated heat spreaders, thermal interface materials, or active cooling solutions132. The advanced 3D chipset system of claim 131 , wherein the 3D chipset implementation reduces interconnect distances between processing elements resulting in improved performance and reduced power consumption.

133. The advanced 3D chipset system of claim 131, wherein the chiplet architecture provides flexibility to optimize hardware configurations for different model types and deployment scenarios.

134. The advanced 3D chipset system of claim 131, wherein the high-bandwidth memory integration enables efficient processing of large language models and memory-intensive Al applications.

135. The advanced 3D chipset system of claim 131, wherein the chiplet interconnect module enables efficient coordination between specialized processing units during complex multi-model workflows.

136. The advanced 3D chipset system of claim 131, wherein the advanced thermal management systems enable reliable operation of high-density processing configurations under intensive computational workloads.

137. The advanced 3D chipset system of claim 131, wherein the advanced 3D chipset system facilitates higher density and faster computation making transformer models more cost-effective.

138. The advanced 3D chipset system of claim 131 , further comprising integration capabilities with data services systems, governance and analysis systems, and external systems including at least one of sensor systems, loT systems, or robotic systems139. The advanced 3D chipset system of claim 131 , wherein the advanced 3D chipset system supports comprehensive plug-and-play deployment through modular system of models in a box configuration.

140. A method for chipset-optimized Al model execution, comprising: coordinating Al model execution across heterogeneous hardware platforms using hardware abstraction layers: mapping models to optimal hardware based on computational requirements and performance objectives: implementing dynamic resource allocation using resource management modules; accelerating model operations using specialized hardware acceleration components; optimizing performance using 3D chipset architectures with vertically stacked processing layers; and ensuring secure execution using trusted platform modules and hardware security modules.

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