Medium-Term Minimum Demand Forecasting Based on the Parallel LSTM-MLP Model

Power systems face new challenges owing to the rapid expansion of photovoltaic (PV) power. Recent trends indicate a decline in demand coupled with extensive solar power utilization during the spring season, resulting in increased behind-the-meter (BTM) PV generation, subsequently reducing the net demand. This reduction introduces stability issues in the power grid. Accurate minimum demand forecasting in the spring season is pivotal for grid stability and directly influences essential power system security services. However, minimum demand forecasting in the spring season is challenging owing to the fluctuating net demand affecting BTM PV generation, decreased temperature sensitivity in heating and cooling, diverse consumer behavior during holidays, and shifting industrial activities post-COVID-19. These complexities significantly hinder the forecasting accuracy. In this paper, a medium-term minimum demand forecasting framework is proposed, which considers the various features that affect the load profiles: temperature, economic indicators, seasonal changes, and BTM PV capacity. The optimal input variables are determined annually using feature selection. In particular, the proposed framework improves the forecasting accuracy by using forecasted energy consumption information. Furthermore, the proposed parallel long short-term memory (LSTM)-multi layer perception (MLP) model extracts annual temporal characteristics, learns non-linear relationships, and captures patterns. A validation using historical load demand data demonstrates its superiority over conventional methods through a comparative analysis.