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.

A grid power configuration may provide a reliable, efficient, inexpensive and environmentally conscious power source to a site, for example, a remote site such as a well services environment. Grid power may be provided for one or more operations at the site by coupling a main breaker to a switchgear unit coupled to one or more loads. The switchgear unit may be coupled to the main breaker via a main power distribution unit and may also be coupled to one or more loads. At least one of a grid power unit and a switchgear unit may be coupled to the main breaker via the main power distribution unit and may also be coupled to one or more additional loads. A control center may be communicatively coupled to the main breaker or any one or more other components to control one or more operations of the grid power configuration.

Extensive deployment of interoperable distributed energy resources (DER) on power systems is increasing the power system cybersecurity attack surface. National and jurisdictional interconnection standards require DER to include a range of autonomous and commanded grid-support functions which can drastically influence power quality, voltage, and the generation-load balance. Investigations of the impact to the power system in scenarios where communications and operations of DER are controlled by an adversary show that each grid-support function exposes the power system to distinct types and magnitudes of risk. The invention provides methods for minimizing the risks to distribution and transmission systems using an engineered control system which detects and mitigates unsafe control commands.

A microcontroller powered by a power management integrated circuit (PMIC) includes a plurality of cores. A first core of the microcontroller can be configured to implement a system power transient management component. One or more other or second cores of the microcontroller can be configured to implement one or more applications. The system power transient management component implemented by the first core can be configured to dynamically identify an expected load transient event to occur in the microcontroller, determine power control data to optimize a response to the identified expected load transient event, the power control data comprising a power control mode and associated parameters, and provide the power control data to the power management integrated circuit (PMIC).

A power load data prediction method and device, and a storage medium are disclosed. In an embodiment, the he method comprises: acquiring historical power load data of a one-dimensional time sequence, the historical power load data including values of corresponding time points; mapping the values of corresponding time points to a coordinate system in which a horizontal axis is a set time period, and a vertical axis is time points within the time period, and performing marking at each mapping point by using predetermined pixel values corresponding to the values to obtain a mapping image, wherein different values correspond to different pixel values; and inputting the pixel values of the mapping image to a trained data prediction model, and acquiring a power load data prediction value output by the data prediction model. The method and device and the storage medium can improve the prediction accuracy of the power load data.