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Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN

Author

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  • Minfeng Wu

    (School of Electrical Engineering and Artificial Intelligence, Xiamen University Malaysia, Sepang 43900, Malaysia)

  • Wen Chen

    (College of Ocean Information Engineering, Jimei University, Xiamen 361021, China)

Abstract

Since electric vehicles (EVs) could reduce the growing concerns on environmental pollution issues and relieve the social dependency of fossil fuels, the EVs market is fast increased in recent years. However, a large growth in the number of EVs will bring a great challenge to the present traffic system; thus, an acceptable model is necessary to forecast the sales of EVs in order to better plan the appropriate supply of necessary facilities (e.g., charging stations and sockets in car parks) as well as the electricity required on the road. In this study, we propose a model to predict the sales volume and increase rate of EVs in the world and China, using both statistics and machine learning methods by combining principle component analysis and a general regression neural network, based on the previous 11 years of sales data of EVs. The results indicate that a continuing growth in the sales of EVs will appear in both the world and China in the coming eight years, but the sales increase rate is slowly and continuously deceasing because of the persistent growth of the basic sales volume. The results also indicate that the increase rate of sales of EVs in China is higher than that of the world, and the proportion of sales of EVs in China will increase gradually and will be above 50% in 2025. In this case, large accessory facilities for EVs are required in China in the coming few years.

Suggested Citation

  • Minfeng Wu & Wen Chen, 2022. "Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN," Sustainability, MDPI, vol. 14(4), pages 1-14, February.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2206-:d:749799
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    References listed on IDEAS

    as
    1. Hidrue, Michael K. & Parsons, George R. & Kempton, Willett & Gardner, Meryl P., 2011. "Willingness to pay for electric vehicles and their attributes," Resource and Energy Economics, Elsevier, vol. 33(3), pages 686-705, September.
    2. Zhuang Yang & Qu Zhou & Xiaodong Wu & Zhongyong Zhao & Chao Tang & Weigen Chen, 2019. "Detection of Water Content in Transformer Oil Using Multi Frequency Ultrasonic with PCA-GA-BPNN," Energies, MDPI, vol. 12(7), pages 1-12, April.
    3. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
    4. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    5. Wang, Yun & Sun, Xiaohua & Wang, Baocai & Liu, Xiaoling, 2020. "Energy saving, GHG abatement and industrial growth in OECD countries: A green productivity approach," Energy, Elsevier, vol. 194(C).
    6. Yong Zhang & Miner Zhong & Nana Geng & Yunjian Jiang, 2017. "Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-15, May.
    7. Yeojin Kim & Jin Hur, 2020. "An Ensemble Forecasting Model of Wind Power Outputs Based on Improved Statistical Approaches," Energies, MDPI, vol. 13(5), pages 1-11, March.
    8. Alexis Gerossier & Robin Girard & George Kariniotakis, 2019. "Modeling and Forecasting Electric Vehicle Consumption Profiles," Energies, MDPI, vol. 12(7), pages 1-14, April.
    9. Aurélie Glerum & Lidija Stankovikj & Michaël Thémans & Michel Bierlaire, 2014. "Forecasting the Demand for Electric Vehicles: Accounting for Attitudes and Perceptions," Transportation Science, INFORMS, vol. 48(4), pages 483-499, November.
    10. Serradilla, Javier & Wardle, Josey & Blythe, Phil & Gibbon, Jane, 2017. "An evidence-based approach for investment in rapid-charging infrastructure," Energy Policy, Elsevier, vol. 106(C), pages 514-524.
    11. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    12. Juncheng Zhu & Zhile Yang & Monjur Mourshed & Yuanjun Guo & Yimin Zhou & Yan Chang & Yanjie Wei & Shengzhong Feng, 2019. "Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches," Energies, MDPI, vol. 12(14), pages 1-19, July.
    13. Shafique, Muhammad & Azam, Anam & Rafiq, Muhammad & Luo, Xiaowei, 2022. "Life cycle assessment of electric vehicles and internal combustion engine vehicles: A case study of Hong Kong," Research in Transportation Economics, Elsevier, vol. 91(C).
    14. Li, Yanfei & Taghizadeh-Hesary, Farhad, 2022. "The economic feasibility of green hydrogen and fuel cell electric vehicles for road transport in China," Energy Policy, Elsevier, vol. 160(C).
    15. He, Shuying & Guo, Kun, 2021. "What factors contribute to the mutual dependence degree of China in its crude oil trading relationship with oil-exporting countries?," Energy, Elsevier, vol. 228(C).
    16. José Alberto Fuinhas & Matheus Koengkan & Nuno Carlos Leitão & Chinazaekpere Nwani & Gizem Uzuner & Fatemeh Dehdar & Stefania Relva & Drielli Peyerl, 2021. "Effect of Battery Electric Vehicles on Greenhouse Gas Emissions in 29 European Union Countries," Sustainability, MDPI, vol. 13(24), pages 1-26, December.
    17. Zhou, Guanghui & Ou, Xunmin & Zhang, Xiliang, 2013. "Development of electric vehicles use in China: A study from the perspective of life-cycle energy consumption and greenhouse gas emissions," Energy Policy, Elsevier, vol. 59(C), pages 875-884.
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    3. Jian Huang & Qinyu Chen & Chengqing Yu, 2022. "A New Feature Based Deep Attention Sales Forecasting Model for Enterprise Sustainable Development," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    4. Min Zhao & Yu Fang & Debao Dai, 2023. "Forecast of the Evolution Trend of Total Vehicle Sales and Power Structure of China under Different Scenarios," Sustainability, MDPI, vol. 15(5), pages 1-22, February.

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