Enhancing EV Charging Demand Forecasting for Highway Rest Area Stations: Integrating Day Type, Traffic Volume, and Weather Conditions

With global EV sales projected to reach 3.5 million units by 2023 and public charging stations increasing by 40%, effective management and optimization of charging infrastructure have become critical. While current research primarily focuses on models that address either users or charging stations, these approaches often overlook external factors such as weather and traffic conditions, which significantly impact driving patterns and energy consumption. The integration of these external variables into forecasting models is thus critical for enhancing prediction accuracy and infrastructure optimization. In this study, we conducted an in-depth analysis of EV charging patterns at highway rest areas. This study employed deep learning models, GRU, and LSTM architectures, trained using various data combinations. The experiments revealed that the inclusion of traffic data notably improves forecasting precision. In particular, the LSTM model demonstrated a 10.5% reduction in mean absolute percentage error(MAPE), decreasing the standard deviation from 4.95 to 3.87 when external factors were included.