Machine Learning Approaches for Predicting Electric Vehicle Charging Demand and Energy Consumption

This study explores the prediction of electric vehicle (EV) energy consumption and charging patterns using machine learning techniques. The research emphasizes the importance of accurate predictions in optimizing charging infrastructure management and alleviating range anxiety. Utilizing a novel dataset spanning 29,600 days of synthetic EV charging data, the study employs various regression models, including Gaussian Process Regression (GPR), Support Vector Machines (SVM), Neural Networks, and Linear Regression, enhanced by Grid Search Optimization. The analysis focuses on two primary objectives, predicting daily electrical energy requirements and forecasting the number of vehicles charged. Results indicate that the GPR model performs exceptionally well in predicting energy consumption, while Neural Networks excel in estimating the number of vehicles charged daily. The study highlights the potential of these methods in addressing the complexities of EV charging behavior and energy demand. Key challenges identified include data variability, quality issues, and the need for real-time model adaptation. However, the research also points to promising future trends, such as increasing battery efficiency, automated charging systems, and the use of ensemble models. The findings provide valuable insights for EV industry, contributing to more efficient energy management and supporting the broader adoption of electric vehicles. This approach to EV charging prediction demonstrates the potential of advanced machine learning techniques in enhancing the reliability and efficiency of EV infrastructure, thus facilitating the transition to sustainable transportation solutions.