Using Bayesian Deep Learning for Electric Vehicle Charging Station Load Forecasting

Dan Zhou (), Zhonghao Guo, Yuzhe Xie, Yuheng Hu, Da Jiang, Yibin Feng and Dong Liu
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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

Energies, 2022, vol. 15, issue 17, 1-15

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.

Keywords: electric vehicle charging station; load forecasting; Bayesian deep learning; long short-term memory (LSTM) network; capture uncertainty (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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