Machine learning model selection for forecasting entity energy usage

A non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, cause the processor to generate time-series energy usage forecast predictions for energy consuming entities, wherein, when executed, the instructions cause the processor to:

• train a plurality of machine learning models, each machine learning model corresponding to an entity segment, the training of each machine learning model comprising receiving historical energy usage data for entities that match the corresponding entity segment using training data from energy consuming entities that match the trained machine learning model's entity segment;
• receive input data comprising historical time-series energy usage data for an energy consuming entity;
• match the energy consuming entity to one of the entity segments;
• select one of the trained machine learning models that corresponds to the matched entity segment;
• generate a forecast prediction using the selected trained machine learning model and the input data, wherein the forecast prediction comprises a time-series energy usage forecast for the energy consuming entity over a period of time;
• and
• in response to the forecast prediction, configure a distribution of an electrical load of a utility electrical grid by revising an expected load from an enterprise location.

A system for generate time-series energy usage forecast predictions for energy consuming entities, the system comprising:

• a processor;
• and
• a memory storing instructions for execution by the processor, the instructions configuring the processor to:
• train a plurality of machine learning models, each machine learning model corresponding to an entity segment, the training of each machine learning model comprising receiving historical energy usage data for entities that match the corresponding entity segment using training data from energy consuming entities that match the trained machine learning model's entity segment;
• receive input data comprising historical time-series energy usage data for an energy consuming entity;
• match the energy consuming entity to one of the entity segments;
• select one of the trained machine learning models that corresponds to the matched entity segment;
• generate a forecast prediction using the selected trained machine learning model and the input data, wherein the forecast prediction comprises a time-series energy usage forecast for the energy consuming entity over a period of time;
• and
• in response to the forecast prediction, configure a distribution of an electrical load of a utility electrical grid by revising an expected load from an enterprise location.