Abstract: A technique is described herein for using computing technology to intelligently manage the consumption of a resource in a physical environment and/or controlling the physical environment in other ways. The technique maintains environment information that describes entities within the physical environment, together with the relationships among the entities. The technique leverages the environment information and collected sensor data to generate forecast data using one or more machine-trained models. The technique then leverages the environment information, sensor data, and forecast data to generate a control plan. The control plan provides a strategy for controlling the physical environment that satisfies a specified optimization objective. In one use case, the technique contributes to the efficient consumption of power provided by a distribution system by avoiding consumption of power in periods in which the distribution system is expected to experience high loads.

Abstract: A technique is described herein for using computing technology to intelligently manage the consumption of a resource in a physical environment and/or controlling the physical environment in other ways. The technique maintains environment information that describes entities within the physical environment, together with the relationships among the entities. The technique leverages the environment information and collected sensor data to generate forecast data using one or more machine-trained models. The technique then leverages the environment information, sensor data, and forecast data to generate a control plan. The control plan provides a strategy for controlling the physical environment that satisfies a specified optimization objective. In one use case, the technique contributes to the efficient consumption of power provided by a distribution system by avoiding consumption of power in periods in which the distribution system is expected to experience high loads.

Abstract: Systems and methods for optimizing an energy storage device charging schedule are provided. In one example, a method may include calculating a merit value for a current time frame based on current data concerning predetermined criteria; calculating respective merit values for one or more future time frames based on the current data and historical data concerning the predetermined criteria; identifying, from among the current time frame and the one or more future time frames, a plurality of candidate charge adjustment time frames; and assigning, from among the plurality of candidate charge adjustment time frames, one or more charge adjustment time frames during which to adjust a charge level of an energy storage device based on at least the merit values associated with the plurality of candidate charge adjustment time frames.