Machine learning model selection for forecasting entity energy usage

Abstract

Embodiments relate to generating time-series energy usage forecast predictions for energy consuming entities. Machine learning model(s) can be trained to forecast energy usage for different energy consuming entities. For example, a local coffee shop location and a large grocery store location are both considered retail locations, however their energy usage over days or weeks may differ significantly. Embodiments organize energy consuming entities into different entity segments and store trained machine learning models that forecast energy usage for each of these individual entity segments. For example, a given machine learning model that corresponds to a given entity segment can be trained using energy usage data for entities that match the given entity segment. A forecast manager can generate a forecast prediction for an energy consuming entity by matching the entity to a given entity segment and generating the forecast prediction using the entity segment's trained machine learning model.