End-to-end model learning for distributed energy resource management systems

Abstract

Certain aspects of the present disclosure provide techniques for selecting and training a forecasting model for a distributed energy resource management system. An example method includes selecting a forecasting model from a plurality of candidate forecasting models; training the selected forecasting model using accuracy measurements comprising at least one of root mean squared error (RMSE), mean absolute error (MAE), or mean squared error (MSE); generating forecasting output data from the trained forecasting model based on predetermined test data; running a simulation using the forecasting output data, the simulation utilizing optimization logic and constraints to simulate control behaviors of distributed energy resources, the simulation being run by generating operating set points based on the forecasting output data to yield an energy cost result; and providing the energy cost result as feedback for determining differential weightings applied to one or more of the accuracy measurements and selection of an updated forecasting model.