Forecasting Residential EV Charging Behavior: A Transfer Learning-Enabled Approach to Address Data Scarcity in the Residential Sector

Charging a significant number of electric vehicles (EVs) can pose challenges for power systems, and a viable solution is smart charging. Reliable smart charging can be achieved by forecasting the required energy and plug-in duration of a charging event. However, despite the availability of data from public charging stations, the lack of sufficient real data for residential EV charging has consistently been a barrier, particularly when using deep learning methods. Moreover, enhancing the accuracy of forecasting residential EV charging behavior is crucial due to its significant impact on power systems. To address these challenges, this paper proposes an intelligent deep learning-based framework to forecast the required energy and plug-in duration of residential EV charging events by leveraging transfer learning. Additionally, the proposed framework utilizes an auxiliary deep learning model that forecasts charging duration to improve the accuracy of required energy forecasting. Various case studies have been conducted, and the results show that the proposed framework significantly improves the accuracy of residential charging behavior forecasting.