Electric vehicles load forecasting for day-ahead market participation using machine and deep learning methods

As the significance of participation in the Day-Ahead Market (DAM) for stakeholders managing the charging of Electric Vehicle (EV) fleets increases, the necessity for precise EV Load Curve (EVLC) forecasting emerges as crucial. This paper presents an extensive investigation of nine diverse EVLC forecasting methodologies, encompassing statistical, machine learning, and deep learning techniques. These methodologies are evaluated on four public, real-world EV datasets, keeping in line with the specific forecasting horizon required for DAM. The study incorporates models with and without online historical data, ensuring broad applicability across varied data availability scenarios. An exploration of seasonal variations in forecasting performance is conducted via one year-long rolling simulations, providing deep insight on seasonal patterns. Furthermore, a detailed methodology for constructing EVLCs from session-based, tabular EV datasets is presented. The conducted research establishes a first-of-its-kind comprehensive comparison of EVLC forecasting methodologies for the real-world challenge of DAM participation. The research results provide indispensable guidance to wholesale electricity market participants, namely suppliers and electric vehicle aggregators, advancing the understanding in the field and significantly contributing to optimization of DAM participation for EV fleets.