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Forecasting Quarterly Sales Volume of the New Energy Vehicles Industry in China Using a Data Grouping Approach-Based Nonlinear Grey Bernoulli Model

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  • Ling-Ling Pei

    (School of Business Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

  • Qin Li

    (School of Business Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, China)

Abstract

The new energy vehicles (NEVs) industry has been regarded as the primary industry involving in the transformation of the China automobile industry and environmental pollution control. Based on the quarterly fluctuation characteristics of NEVs’ sales volume in China, this research puts forwards a data grouping approach-based nonlinear grey Bernoulli model (DGA-based NGBM (1,1)). The main ideas of this work are to effectively predict quarterly fluctuation of NEVs industry by introducing a data grouping approach into the NGBM (1,1) model, and then use the particle swarm optimization (PSO) algorithm to optimize the parameters of the model so as to increase forecasting precision. By empirical comparison between the DGA-based NGBM (1,1) and existing data grouping approach-based GM (1,1) model (DGA-based GM (1,1)), DGA-based NGBM (1,1) can effectively reduce the prediction error resulting from quarterly fluctuation of sales volume of the NEVs, and prediction performance are proven to be favorable. The results of out-of-sample forecasting using the model proposed show that the sales volume of NEVs in China will increase by 57% in 2019–2020 with a quarterly fluctuation. In 2020, the sales volume of NEVs will exceeds the target of 2 million in the “13th Five-Year Strategic Development Plan”. Therefore, China needs to pay more attention to infrastructure construction and after-sales service for NEVs.

Suggested Citation

  • Ling-Ling Pei & Qin Li, 2019. "Forecasting Quarterly Sales Volume of the New Energy Vehicles Industry in China Using a Data Grouping Approach-Based Nonlinear Grey Bernoulli Model," Sustainability, MDPI, vol. 11(5), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1247-:d:209354
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    Cited by:

    1. Zhang, Juan & Huang, Jian, 2021. "Vehicle product-line strategy under government subsidy programs for electric/hybrid vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    2. Wenqing Wu & Xin Ma & Bo Zeng & Yuanyuan Zhang & Wanpeng Li, 2021. "Forecasting short-term solar energy generation in Asia Pacific using a nonlinear grey Bernoulli model with time power term," Energy & Environment, , vol. 32(5), pages 759-783, August.
    3. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    4. Wen-Ze Wu & Tao Zhang & Chengli Zheng, 2019. "A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP," Complexity, Hindawi, vol. 2019, pages 1-10, October.
    5. 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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