Predictive Modeling and Analysis of Energy Consumption in Ev Charging Stations Using Machine Learning Techniques

The rapid expansion of electric vehicle (EV) adoption has introduced significant challenges in managing energy demand and infrastructure planning for charging stations. Unpredictable usage patterns and limited real-time control hinder the efficiency and scalability of EV charging networks. Existing forecasting methods often struggle to capture the nonlinear and time-dependent behavior of charging sessions. Recent advancements in machine learning have demonstrated potential for improving prediction accuracy by leveraging historical session data. In this study, we propose a data-driven machine-learning framework to forecast energy consumption at EV charging stations using session-level features from real-world operational data. We compare three regression models, including Linear Regression, Random Forest, and Extreme Gradient Boosting (XGBoost), to evaluate their ability to capture complex consumption dynamics. Experimental results reveal that XGBoost significantly outperforms the others, achieving the lowest Mean Absolute Error (1.08 kWh), Root Mean Squared Error (3.69 kWh), and the highest <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex> score (0.85). These findings provide actionable insights for optimizing station management, enhancing energy efficiency, and guiding infrastructure expansion.