Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a machine learning system is trained on a set of energy intelligence data and deployed on an edge device, wherein the machine learning system is configured to receive additional training by the edge device to improve energy management. In some embodiments, the energy management includes management of generation of energy by a set of distributed energy generation resources, management of storage of energy by a set of distributed energy storage resources management of delivery of energy by a set of distributed energy delivery resources, management of delivery of energy by a set of distributed energy delivery resources, and/or management of consumption of energy by a set of distributed energy consumption resources.

An artificial-intelligence-based platform for enabling intelligent orchestration and management of power and energy includes a governance system and a smart contract system. The governance system is configured to record, in a distributed ledger and/or blockchain, a set of mining operation events associated with a mining operation. The mining operation events are detected by one or more of a set of sensors in a mine of the mining operation, a set of wearable devices of a set of miners associated with the mining operation, or a set of sensors deployed on a set of smart containers for mined materials associated with the mining operation. The smart contract system is configured to allocate proceeds of the mining operation events among a set of owners, a set of services providers, and/or a set of governmental entities.

An AI-based platform with carbon generation and/or emissions awareness of a set of edge devices is disclosed. Each edge device of the set of edge devices is configured to maintain awareness of carbon generation and/or emissions of at least one entity of a set of energy-using entities that are linked to and/or governed by the set of edge devices. In some embodiments, at least one edge device of the set is configured to simulate carbon emissions of at least one entity of the set of energy-using entities. In some embodiments, at least one edge device of the set is configured to execute a set of machine-learned algorithms trained on a training data set of carbon generation data to calculate a metric of carbon generation for a set of operational entities.

An AI-based platform for automated labor law compliance associated with mining operations is disclosed. The AI-based platform includes a governance system for a mining operation and a reporting system for conveying at least one parameter that is sensed by a sensor of a mine of the mining operation, wherein the at least one parameter is associated with a compliance of the mining operation with a set of labor standards. In some embodiments, the set of labor standards is associated with at least one activity performed by a laborer of the mine, and the AI-based platform conveys an indication of a performance of the activity by the laborer. In some embodiments, the set of labor standards is associated with at least one object associated with a laborer of the mine, and the AI-based platform conveys an indication of a detection of the at least one object.

An AI-based platform for enabling intelligent orchestration and management of power and energy is disclosed. The AI-based platform includes an artificial intelligence system that is configured to perform an analysis of a pattern of energy associated with an operating process that involves a set of resources, the resources being at least partially independent of an electrical grid, and output a set of operating parameters to provision energy generation, storage, and/or consumption to enable the operating process, wherein the set of operating parameters is based on the analysis. In some embodiments, at least one operating parameter in the set of operating parameters is a generation output level for a distributed energy generation resource, a target storage level for a distributed energy storage resource, and/or a delivery timing for a distributed energy delivery resource.

An AI-based platform for enabling intelligent orchestration and management of power and energy is disclosed. The AI-based platform includes a digital twin that is updated by a data collection system that dynamically maintains a set of historical, current, and/or forecast energy demand parameters for a set of fixed entities and a set of mobile entities within a defined domain. The updating of the digital twin is based on the set of energy demand parameters. In some embodiments, the energy demand parameters are based on one or more public data resources, such as a weather data resource, a satellite data resource, or a census, population, demographic, and/or psychographic data resource. In some embodiments, the energy demand parameters are based on one or more enterprise data resources, such as resource planning data, demand planning data, or supply chain data.

An AI-based platform for enabling intelligent orchestration and management of power and energy is disclosed. The platform includes a set of adaptive, autonomous data handling systems. Each of the adaptive, autonomous data handling systems is configured to collect data relating to energy generation, storage, or delivery from a set of edge devices that are in operational control of a set of distributed energy resources. Each of the adaptive, autonomous data handling systems is configured to autonomously adjust, based on the collected data, a set of operational parameters for such operational control.

An AI-based platform for enabling intelligent orchestration and management of power and energy is disclosed. The AI-based platform includes an adaptive energy data pipeline configured to communicate data across a set of nodes in a network. Each node of the set of nodes is adapted to operate on an energy data set associated with at least one of energy generation, energy storage, energy delivery, or energy consumption. At least one node of the set of nodes is configured, by one or both of an algorithm or a rule set, to filter, compress, transform, error correct and/or route at least a portion of the energy data set based on at least one of a set of network conditions, data size, data granularity, or data content.

An AI-based platform for enabling intelligent orchestration and management of power and energy is disclosed. The AI-based platform includes an adaptive energy data pipeline configured to communicate data across a set of nodes in a network. Each node of the set of nodes is adapted to operate on an energy data set associated with at least one of energy generation, energy storage, energy delivery, or energy consumption. At least one node of the set of nodes is configured, by one or both of an algorithm or a rule set, to filter, compress, transform, error correct and/or route at least a portion of the energy data set based on at least one of a set of network conditions, data size, data granularity, or data content.

A method and a system of hybrid data-and-model-driven hierarchical network reconfiguration are provided. Taking into account that the optimal operation structure of the power grid may change after a new energy is connected into the power grid in the distribution manner, this method combines the mathematical model of network reconfiguration and clustering with the deep learning model, and uses data recorded by the smart meter, to reconfigure the network in a hybrid data-and-model driving mode. This method proposes a network compression method and a hierarchical decompression method based on deep learning, and improves the efficiency of network reconfiguration by the way of distributed calculation and combination of on-line and off-line calculation.