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An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles

Author

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  • Zhou, Huimin
  • Dang, Yaoguo
  • Yang, Yingjie
  • Wang, Junjie
  • Yang, Shaowen

Abstract

An accurate prediction of electric vehicles stock and sales is a prerequisite for planning industrial policies for renewable sources to be used by a transportation system. We propose a novel time-varying grey Bernoulli model to investigate the nonlinear, complexity, and time-varying characteristics associated with electric vehicles stock and sales. We first design the time-varying parameters and a power exponent to explore the nonlinear developing trends of sequences. Subsequently, the cuckoo search algorithm determines optimum solutions because of its competence in dealing with complex optimization problems. Furthermore, its relationship with existing grey prediction models is presented, which demonstrates the flexibility and practicality of the newly-designed model. In order to validate this new model, the global electric vehicles stock and electric vehicles sales in France are predicted in comparison with six benchmark models. As demonstrated by the empirical findings, the proposed model is superior in terms of its capacity for forecasting, confirming its significant potential as a promising tool for electric vehicles stock and sales prediction.

Suggested Citation

  • Zhou, Huimin & Dang, Yaoguo & Yang, Yingjie & Wang, Junjie & Yang, Shaowen, 2023. "An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222027578
    DOI: 10.1016/j.energy.2022.125871
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    Cited by:

    1. An, Yimeng & Dang, Yaoguo & Wang, Junjie & Zhou, Huimin & Mai, Son T., 2024. "Mixed-frequency data Sampling Grey system Model: Forecasting annual CO2 emissions in China with quarterly and monthly economic-energy indicators," Applied Energy, Elsevier, vol. 370(C).
    2. Li, Mingyang & Tang, Jinjun, 2023. "Simulation-based optimization considering energy consumption for assisted station locations to enhance flex-route transit," Energy, Elsevier, vol. 277(C).
    3. Liu, Bingchun & Chen, Jiali & Gao, Yuan & Zhang, Xinming & Zhao, Shiming, 2025. "Regional differences in the recycling capacity of retired batteries for new energy vehicles in China: A perspective of sales volume forecasting," Transport Policy, Elsevier, vol. 172(C).
    4. Liang, Xuting & Wang, Qiong & Chen, Wei, 2026. "Forecasting new energy vehicle sales using a fractional reverse accumulation non-equidistant grey time power model," Applied Mathematics and Computation, Elsevier, vol. 519(C).
    5. repec:bcp:journl:v:9:y:2025:i:10:p:2091-2106 is not listed on IDEAS
    6. Ding, Song & Cai, Zhijian & Qin, Xinghuan & Shen, Xingao, 2024. "Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms," Applied Energy, Elsevier, vol. 367(C).
    7. Li, Liangshuai & Zhang, Zhuo, 2026. "A novel nonlinear grey model with parameter estimation optimization and its application in wind power forecasting," Applied Energy, Elsevier, vol. 409(C).
    8. Ding, Yuanping & Dang, Yaoguo, 2023. "Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model," Energy, Elsevier, vol. 277(C).
    9. Yang, Zhongsen & Wang, Yong & Zhou, Ying & Wang, Li & Ye, Lingling & Luo, Yongxian, 2023. "Forecasting China's electricity generation using a novel structural adaptive discrete grey Bernoulli model," Energy, Elsevier, vol. 278(C).

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