Optimal Sizing and Operation of a Battery Energy Storage System for Demand Response in a Food Processing Plant Using Deep Learning for Load Forecasting

High energy prices pose a significant challenge for the food industry due to their energy intensity. One promising method to reduce energy costs is demand response (DR). In this paper, we design a battery energy storage system (BESS) for DR in a food processing plant, which we use to shift the electrical load from high-price periods to low-price periods. A known challenge of load shifting is load forecasting. We compare extended long short-term memory (xLSTM), long short-term memory (LSTM), and Transformer with persistent prediction and historical average load profiles for load forecasting and evaluate the financial impact of forecasting errors in a case study. Despite using a small feature set, machine learning-based (ML) forecasts demonstrate strong performance, with symmetric mean absolute percentage error (sMAPE) values of 11.65% for xLSTM, 11.02% for LSTM, and 11.03% for Transformer, compared to 16.59% for historical average load profiles and 20.08% for persistent prediction. In the case study investigated, savings in electrical costs of 37,270 EUR per year could be achieved. This shows the enormous potential of BESS in combination with ML-based forecasting for industrial DR.