A Robust Minute-Ahead Short-Term Forecasting Algorithm for Demand Reduction Using Battery Energy Storage System

The current energy infrastructure of Malaysia is overly centralized that focuses on supply-side management, which consists of large and peaking power plants. The current approach is not economically nor environmentally sustainable due to the infrastructural upgrade cost as well as the high carbon emissions rate. Thus, battery energy storage system (BESS) technology is proposed to overcome this issue. To utilize the BESS, this paper proposed a maximum demand reduction strategy that leverages a deep learning model as the core of a short-Term forecasting algorithm. The proposed algorithm analyses the load demand data to estimate the peak demand threshold for the BESS to coordinate energy supply when the load demand exceeds that threshold. With a 60-minute lookback window, it produces the most optimal results. When tested with multiple experimental sites' dataset, the robust algorithm can effectively reduce their maximum demand as long as it is initially trained with the site's data. Furthermore, it can achieve a maximum demand reduction factor of at least 43% among four experimental sites. © 2026 IEEE.