Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis

As the number of electric vehicles (EVs) continues to grow, the demand for charging stations is also increasing, leading to challenges such as long waiting times and insufficient infrastructure. High-precision forecasting of EV charging demand is crucial for efficient station management. This paper presents an approach to predict the charging demand at 15-minute intervals for the day ahead using a multivariate long short-term memory (LSTM) network with an attention mechanism. Additionally, the model leverages explainable AI techniques to evaluate the influence of various factors on the predictions, including weather conditions, day of the week, month, and any holiday. Shapley additive explanation (SHAP) is used to quantify the contribution of each feature to the final forecast, providing deeper insights into how these factors affect prediction accuracy. The efficacy of the proposed method is demonstrated by simulations using the test data collected from the EV charging stations at California State University, Long Beach.