Decision focused forecasting for smart grid energy management systems

Accurate forecasting of energy time series is essential to support Energy Management Systems (EMS) in taking real time decisions of energy flows in microgrids. In the traditional two-stage methodology, forecasts are made upstream and provided as input to the EMS by a prediction model trained in advance on energy prices, loads and generation time series. This study adopts an innovative approach of Decision Focused Learning (DFL) for energy demand, production and price forecasting in a microgrid environment, wherein for each time series an LSTM neural network is trained end-to-end by embedding the optimization cost directly into the loss function. Results show that the proposed DFL approach reduces the operational cost by 11% compared to the conventional two-stage method.