Demand Prediction of New Electric Vehicle Charging Stations: A Deep Learning Approach

Prediction of the Electric Vehicle (EV) charging demand is of great importance to charging stations, especially for newly established charging stations whose demand is difficult to predict due to the absence of past time-series transaction data. This paper develops a deep learning method to fill the literature gap to predict charging demands for the new EV charging stations in the next few days, using a transaction dataset containing over 270 charging stations in Nanjing, eastern China. Specifically, our study introduces the average transactions of neighboring stations as new time-series variables and constructs a Convolutional Neural Network (CNN) model, which is a novel deep learning method. The R-squares of the CNN model achieve an average value of 0.90, which outperforms four time-series prediction models, e.g., the Long Short-Term Memory Network (LSTM) and the Extreme Gradient Boosting (XGBoost). In addition, we visualize the areas with high predicted demand for new charging stations using the trained CNN model and achieve a recommendation accuracy rate of 0.70, providing a reference for EV charging operation companies to find the optimal location of new charging stations. Accurate prediction for new charging stations in this study can provide actionable insights to charging station operators in location selection and create a more favorable EV ecosystem.