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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Taicheng Zhang & Qiao Peng & Shihong Zeng, 2025. "Predicting EV Charging Demand in Renewable-Energy-Powered Grids Using Explainable Machine Learning," Sustainability, MDPI, vol. 17(9), pages 1-22, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4158-:d:1649168
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    as
    1. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    2. Wolf-Peter Schill, Alexander Zerrahn, and Friedrich Kunz, 2017. "Prosumage of solar electricity: pros, cons, and the system perspective," Economics of Energy & Environmental Policy, International Association for Energy Economics, vol. 0(Number 1).
    3. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    4. Umm e Hanni & Toshiyuki Yamamoto & Toshiyuki Nakamura, 2024. "An Analysis of Electric Vehicle Charging Intentions in Japan," Sustainability, MDPI, vol. 16(3), pages 1-22, January.
    5. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    6. Trizoglou, Pavlos & Liu, Xiaolei & Lin, Zi, 2021. "Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines," Renewable Energy, Elsevier, vol. 179(C), pages 945-962.
    7. Mostafaeipour, Ali & Bidokhti, Abbas & Fakhrzad, Mohammad-Bagher & Sadegheih, Ahmad & Zare Mehrjerdi, Yahia, 2022. "A new model for the use of renewable electricity to reduce carbon dioxide emissions," Energy, Elsevier, vol. 238(PA).
    8. Fuad Un-Noor & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Mohammad Nurunnabi Mollah & Eklas Hossain, 2017. "A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development," Energies, MDPI, vol. 10(8), pages 1-84, August.
    9. Lund, Henrik & Kempton, Willett, 2008. "Integration of renewable energy into the transport and electricity sectors through V2G," Energy Policy, Elsevier, vol. 36(9), pages 3578-3587, September.
    10. Stephen P. Holland & Erin T. Mansur & Nicholas Z. Muller & Andrew J. Yates, 2016. "Are There Environmental Benefits from Driving Electric Vehicles? The Importance of Local Factors," American Economic Review, American Economic Association, vol. 106(12), pages 3700-3729, December.
    11. Li, Raymond & Lee, Hazel, 2022. "The role of energy prices and economic growth in renewable energy capacity expansion – Evidence from OECD Europe," Renewable Energy, Elsevier, vol. 189(C), pages 435-443.
    12. Tianze Lan & Kittisak Jermsittiparsert & Sara T. Alrashood & Mostafa Rezaei & Loiy Al-Ghussain & Mohamed A. Mohamed, 2021. "An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand," Energies, MDPI, vol. 14(3), pages 1-25, January.
    13. Peng, Qiao & Liu, Weilong & Zhang, Yong & Zeng, Shihong & Graham, Byron, 2023. "Generation planning for power companies with hybrid production technologies under multiple renewable energy policies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    14. Cao, Tingwei & Xu, Yinliang & Liu, Guowei & Tao, Shengyu & Tang, Wenjun & Sun, Hongbin, 2024. "Feature-enhanced deep learning method for electric vehicle charging demand probabilistic forecasting of charging station," Applied Energy, Elsevier, vol. 371(C).
    15. Peng, Qiao & Bakkar, Yassine & Wu, Liangpeng & Liu, Weilong & Kou, Ruibing & Liu, Kailong, 2024. "Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    16. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    17. Ma, Tai-Yu & Faye, Sébastien, 2022. "Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks," Energy, Elsevier, vol. 244(PB).
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