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Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand

Author

Listed:
  • Sahar Koohfar

    (School of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Wubeshet Woldemariam

    (Mechanical and Civil Engineering Department, Purdue University Northwest, Hammond, IN 46323, USA)

  • Amit Kumar

    (School of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

Abstract

Electric vehicles (EVs) contribute to reducing fossil fuel dependence and environmental pollution problems. However, due to complex charging behaviors and the high demand for charging, EVs have imposed significant burdens on power systems. By providing reliable forecasts of electric vehicle charging loads to power systems, these issues can be addressed efficiently to dispatch energy. Machine learning techniques have been demonstrated to be effective in forecasting loads. This research applies six machine learning methods to predict the charging demand for EVs: RNN, LSTM, Bi-LSTM, GRU, CNN, and transformers. A dataset containing five years of charging events collected from 25 public charging stations in Boulder, Colorado, USA, is used to validate this approach. Compared to other highly applied machine learning models, the transformer method outperforms others in predicting charging demand, demonstrating its ability for time series forecasting problems.

Suggested Citation

  • Sahar Koohfar & Wubeshet Woldemariam & Amit Kumar, 2023. "Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4258-:d:1082238
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    References listed on IDEAS

    as
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