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Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles

Author

Listed:
  • Young-Eun Jeon

    (Department of Statistics, Yeungnam University, Gyeongsan 38541, Korea)

  • Suk-Bok Kang

    (Department of Statistics, Yeungnam University, Gyeongsan 38541, Korea)

  • Jung-In Seo

    (Department of Information Statistics, Andong National University, Andong 36729, Korea)

Abstract

In recent years, the supply of electric vehicles, which are eco-friendly cars that use electric energy rather than fossil fuels, which cause air pollution, is increasing. Accordingly, it is emerging as an urgent task to predict the charging demand for the smooth supply of electric energy required to charge electric vehicle batteries. In this paper, to predict the charging demand, time series analysis is performed based on two types of frames: One is using traditional time series techniques such as dynamic harmonic regression, seasonal and trend decomposition using Loess, and Bayesian structural time series. The other is the most widely used machine learning techniques, including random forest and extreme gradient boosting. However, the tree-based machine learning approaches have the disadvantage of not being able to capture the trend, so a hybrid strategy is proposed to overcome this problem. In addition, the seasonal variation is reflected as the feature by using the Fourier transform which is useful in the case of describing the seasonality patterns of time series data with multiple seasonality. The considered time series models are compared and evaluated through various accuracy measures. The experimental results show that the machine learning approach based on the hybrid strategy generally achieves significant improvements in predicting the charging demand. Moreover, when compared with the original machine learning method, the prediction based on the proposed hybrid strategy is more accurate than that based on the original machine learning method. Based on these results, it can find out that the proposed hybrid strategy is useful for smoothly planning future power supply and demand and efficiently managing electricity grids.

Suggested Citation

  • Young-Eun Jeon & Suk-Bok Kang & Jung-In Seo, 2022. "Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5426-:d:806915
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    References listed on IDEAS

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    1. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    2. Li, Chengzhe & Zhang, Libo & Ou, Zihan & Wang, Qunwei & Zhou, Dequn & Ma, Jiayu, 2022. "Robust model of electric vehicle charging station location considering renewable energy and storage equipment," Energy, Elsevier, vol. 238(PA).
    3. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    4. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    5. 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.
    6. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    7. Neil Stephen Lopez & Adrian Allana & Jose Bienvenido Manuel Biona, 2021. "Modeling Electric Vehicle Charging Demand with the Effect of Increasing EVSEs: A Discrete Event Simulation-Based Model," Energies, MDPI, vol. 14(13), pages 1-15, June.
    8. 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.
    9. Huasheng Liu & Yu Li & Chongyu Zhang & Jin Li & Xiaowen Li & Yuqi Zhao, 2022. "Electric Vehicle Charging Station Location Model considering Charging Choice Behavior and Range Anxiety," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    10. Christos Karolemeas & Stefanos Tsigdinos & Panagiotis G. Tzouras & Alexandros Nikitas & Efthimios Bakogiannis, 2021. "Determining Electric Vehicle Charging Station Location Suitability: A Qualitative Study of Greek Stakeholders Employing Thematic Analysis and Analytical Hierarchy Process," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    11. 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.
    12. Munseok Chang & Sungwoo Bae & Gilhwan Cha & Jaehyun Yoo, 2021. "Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
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    Cited by:

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