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Using Bayesian Deep Learning for Electric Vehicle Charging Station Load Forecasting

Author

Listed:
  • Dan Zhou

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Zhonghao Guo

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yuzhe Xie

    (State Grid Ningbo Power Supply Company, Ningbo 315033, China)

  • Yuheng Hu

    (Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China)

  • Da Jiang

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yibin Feng

    (State Grid Ningbo Power Supply Company, Ningbo 315033, China)

  • Dong Liu

    (Candela New Energy Technology (Yangzhou) Co., Ltd., Yangzhou 225200, China)

Abstract

In recent years, replacing internal combustion engine vehicles with electric vehicles has been a significant option for supporting reducing carbon emissions because of fossil fuel shortage and environmental contamination. However, the rapid growth of electric vehicles (EVs) can bring new and uncertain load conditions to the electric network. Precise load forecasting for EV charging stations becomes vital to reduce the negative influence on the grid. To this end, a novel day-ahead load forecasting method is proposed to forecast loads of EV charging stations with Bayesian deep learning techniques. The proposed methodological framework applies long short-term memory (LSTM) network combined with Bayesian probability theory to capture uncertainty in forecasting. Based on the actual operational data of the EV charging station collected on the Caltech campus, the experiment results show the superior performance of the proposed method compared with other methods, indicating significant potential for practical applications.

Suggested Citation

  • Dan Zhou & Zhonghao Guo & Yuzhe Xie & Yuheng Hu & Da Jiang & Yibin Feng & Dong Liu, 2022. "Using Bayesian Deep Learning for Electric Vehicle Charging Station Load Forecasting," Energies, MDPI, vol. 15(17), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6195-:d:897930
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    References listed on IDEAS

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    1. Byungsung Lee & Haesung Lee & Hyun Ahn, 2020. "Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation," Energies, MDPI, vol. 13(18), pages 1-15, September.
    2. 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).
    3. Wu, Chuanshen & Jiang, Sufan & Gao, Shan & Liu, Yu & Han, Haiteng, 2022. "Charging demand forecasting of electric vehicles considering uncertainties in a microgrid," Energy, Elsevier, vol. 247(C).
    4. Majidpour, Mostafa & Qiu, Charlie & Chu, Peter & Pota, Hemanshu R. & Gadh, Rajit, 2016. "Forecasting the EV charging load based on customer profile or station measurement?," Applied Energy, Elsevier, vol. 163(C), pages 134-141.
    5. Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
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    Cited by:

    1. Parichada Trairat & David Banjerdpongchai, 2022. "Multi-Objective Optimal Operation of Building Energy Management Systems with Thermal and Battery Energy Storage in the Presence of Load Uncertainty," Sustainability, MDPI, vol. 14(19), pages 1-26, October.
    2. Francesco Lo Franco & Mattia Ricco & Vincenzo Cirimele & Valerio Apicella & Benedetto Carambia & Gabriele Grandi, 2023. "Electric Vehicle Charging Hub Power Forecasting: A Statistical and Machine Learning Based Approach," Energies, MDPI, vol. 16(4), pages 1-27, February.

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