Predicting EV Charging Demand in Renewable-Energy-Powered Grids Using Explainable Machine Learning

Taicheng Zhang, Qiao Peng () and Shihong Zeng ()
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Taicheng Zhang: Adam Smith Business School, University of Glasgow, Glasgow G12 8QQ, UK
Qiao Peng: Queen’s Business School, Queen’s University Belfast, Belfast BT9 5EE, UK
Shihong Zeng: Applied Economics Department, College of Economics & Management, Beijing University of Technology, Beijing 100124, China

Sustainability, 2025, vol. 17, issue 9, 1-22

Abstract: The increasing adoption of electric vehicles (EVs) and the growing reliance on renewable energy sources underscore the urgent need for accurate forecasting of EV charging demand to support the development of sustainable and resilient energy systems. This study proposes an explainable machine learning (ML)-based approach to predict hourly EV charging demand using a high-resolution dataset from California, spanning January 2021 to May 2024. Five ML models—XGBoost, random forest, LightGBM, CatBoost, and linear regression—were evaluated, with XGBoost achieving the highest predictive accuracy. Scenario analysis revealed a strong positive relationship between renewable energy penetration and EV charging demand: 10%, 20%, and 30% increases in renewable usage led to 20%, 33%, and 47% increases in predicted demand, respectively. SHAP-based feature importance analysis identified renewable energy usage, carbon footprint reduction, and grid stability as key drivers of charging behavior. The proposed framework offers a scalable, interpretable, and data-driven solution to support the alignment of EV charging infrastructure with decarbonization goals. By linking renewable energy integration with demand-side dynamics, the findings offer actionable insights for the design of adaptive electricity pricing strategies and sustainable mobility policies, contributing to the broader vision of low-carbon, environmentally responsible transportation systems.

Keywords: electric vehicle charging demand; renewable energy integration; explainable machine learning; scenario analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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