Short-term Individual Electric Vehicle Charging Behavior Prediction Using Long Short-term Memory Networks

In this paper, we present a novel approach for individual electric vehicle (EV) charging prediction based on long short-term memory networks. Unlike existing methods, our proposed approach does not require any charging information from the other EV users, and can also separately predict the total charging duration on the next day within a certain range. In addition, the proposed approach can forecast the charging start time slots for the next day, and the number of times charging will take place in each time slot. This information can also be used to predict whether the next day will be a charging day or not. The performance of the proposed approach is validated using real EV charging data, and comparison with other machine learning methods shows its superior prediction accuracy.