Improving Short-term load forecasting for a local energy storage system

Short-term load forecasting is a crucial step for proper operation of a battery energy storage system. In this paper, an artificial neural network forecaster is used for hourly based forecasting of the distributed power generation and load consumption. This paper focusses on using mutual information for the selection of training data for the artificial neural network models of the forecaster. The proposed approach reduces the forecasting error, especially after transients in the input-output mapping. Simulations with real data sets are executed to verify the effectiveness of the method.