Day-Ahead Short-Term Load Forecasting for Holidays Based on Modification of Similar Days' Load Profiles

Abstract

Short-term load forecasting (STLF) is necessary for system operators; however, its difficulty has been increasing since distributed resources, particularly behind-the-meter (BTM) PV resources, have been introduced to power systems. This study proposes a framework for STLF for holidays considering the four major factors that affect the net load profiles - calendar, trend, weather, and BTM PV. The target holiday is first paired with historical holidays following its calendar factor, which are defined as 'similar days.' Subsequently, in terms of the remaining three factors, the differences between the historical holidays and target holidays are calculated, and their effects on load differences (factor-induced load differences) are quantified and reflected. Finally, for each pair, the modified load profiles are generated and combined to obtain a daily load profile of the target holiday. The proposed framework was implemented on a case study of Korean national holidays, and its forecasting accuracy was compared with conventional forecasting methods. The accuracy metrics show that the proposed framework outperforms conventional methods. The results suggest that the proposed framework can be applied to STLF for holidays to improve forecasting accuracy.