Abstract
The vigorous development of new energy vehicles (NEVs) has become an effective approach for achieving carbon emission reduction and carbon neutrality goals. The prediction of the demand for NEVs can provide quantitative decision-making basis for governments and enterprises. In consideration of the condition under which the demand for NEVs is affected by subsidy policy and limited samples, a novel demand forecasting model for NEVs is constructed based on the improved Bass model and grey theory in this study. First, the price function is introduced into the improved Bass model to establish a differential equation of demand for NEVs. Considering the limited samples, the differential equation model is transformed into a grey Bass extended model (GBEM). Second, the parameters of GBEM are estimated using the least squares method, the time response sequence is obtained through the Gaussian hypergeometric function, and the background value is optimized using the particle swarm optimization algorithm. Third, three data sets from Norway, France, and Europe are studied to confirm the validity. The calculation results show that the proposed model achieves higher accuracy than six existing models, and the MAPE of the model are all below 10% in three cases. Lastly, GBEM is applied to predict the demand for NEVs in the three aforementioned regions from 2020 to 2023. The results show that the demand for NEVs in Norway, France, and Europe in 2023 are 343,860, 280,685 and 2,157,908, with an average annual growth rate of 46.3133%, 47.1837% and 40.0457%, respectively, which provide a certain reference value for the formulation of national government policies and the production activities of NEV enterprises.
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References
European Commission (2019) Communication from the Commission: The European Green Deal. COM (2019) 640 Final
Guille des ButtesJeanneretKéromnès ABA et al (2020) Energy management strategy to reduce pollutant emissions during the catalyst light-off of parallel hybrid vehicles. Appl Energy. https://doi.org/10.1016/j.apenergy.2020.114866
Su CW, Yuan X, Tao R, Umar M (2021) Can new energy vehicles help to achieve carbon neutrality targets? J Environ Manage. https://doi.org/10.1016/j.jenvman.2021.113348
Jiali J, Yuanyuan L, Zhenyang Z, Jun W (2021) Optimal production decision of new energy vehicle and traditional fuel vehicle. J Intell Fuzzy Syst. https://doi.org/10.3233/jifs-189918
Zheng S, Huang J (2018) New energy vehicles sales prediction method and empirical eesearch under the environment of big data.
Tan T, Huang Z, Lin Y, Bi G (2020) Big data driven demand analysis of new energy vehicles. Renew Energy Resour. https://doi.org/10.13941/j.cnki.21-1469/tk.2020.07.019
Ma J, Wang N, Kong D (2009) Market forecasting modeling study for new energy vehicle based on AHP and logit regression. Tongji Daxue Xuebao/Journal Tongji Univ
Wang Z, Guo D, Wang H (2019) Sales forecast of Chinese new energy vehicles based on wavelet and BP neural network. In: Proceedings - 2019 18th International Symposium on Distributed Computing and Applications for Business Engineering and Science, DCABES 2019
Bass FM (1969) A simultaneous equation regression study of advertising and sales of cigarettes. J Mark Res. https://doi.org/10.2307/3150135
Bass FM (1969) A new product growth for model consumer durables. Manage Sci. https://doi.org/10.1287/mnsc.15.5.215
Robinson B, Lakhani C (1975) Dynamic price models for new-product planning. Manage Sci. https://doi.org/10.1287/mnsc.21.10.1113
Bass FM (1980) The relationship between diffusion rates, experience curves, and demand elasticities for consumer durable technological innovations. J Bus. https://doi.org/10.1086/296099
Dolan RJ, Jeuland AP (1981) Experience curves and dynamic demand models: Implications for optimal pricing strategies. J Mark. https://doi.org/10.1177/002224298104500106
Kalish S (1983) Monopolist pricing with dynamic demand and production cost. Mark Sci. https://doi.org/10.1287/mksc.2.2.135
Easingwood CJ, Mahajan V, Muller E (1983) A nonuniform influence innovation diffusion model of new product acceptance. Mark Sci. https://doi.org/10.1287/mksc.2.3.273
Xu Y, Li L (2020) Effects of price factors on the promotion of new energy vehicles. Sci Technol Ind 20:109–114
Liu T, Chen K (2016) Research on the diffusion model of China's new energy vehicles based on the Bass model. Enterp Econ. https://doi.org/10.13529/j.cnki.enterprise.economy.2016.03.021
Li Y, Ma G, Li L (2017) Development of a generalization Bass diffusion model for Chinese electric vehicles considering charging stations. In: Proceedings - 2017 5th International Conference on Enterprise Systems: Industrial Digitalization by Enterprise Systems, ES 2017
Zeng M, Zeng F, Zhu X, Xue S (2013) Forecast of electric vehicles in China based on Bass model. Electr Power 1:36–39
Liu Y, Wang M, Wang J (2106) The predictive research on China's new energy vehicles market. Res Econ Manage. https://doi.org/10.13502/j.cnki.issn1000-7636.2016.04.012
Zhou J, Zhang T, Hu P (2020) Development forecast of new energy vehicles based on grey forecast. Electron World. https://doi.org/10.19353/j.cnki.dzsj.2020.03.007
Zhou Y, Wang H (2019) Research on monthly sales forecasting model of new energy vehicles in China. Softw Guide 18:149–153
He LY, Pei LL, Yang YH (2020) An optimised grey buffer operator for forecasting the production and sales of new energy vehicles in China. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.135321
Pei LL, Li Q (2019) Forecasting quarterly sales volume of the new energy vehicles industry in China using a data grouping approach-based nonlinear grey Bernoulli model. Sustain. https://doi.org/10.3390/su11051247
Luo D, Zhang J (2012) The development model of new energy vehicle in Henan Province based on the weighted grey target decision. Grey Syst Theory Appl. https://doi.org/10.1108/20439371211273320
Ding S, Li R, Wu S (2021) A novel composite forecasting framework by adaptive data preprocessing and optimized nonlinear grey Bernoulli model for new energy vehicles sales. Commun Nonlinear Sci Numer Simul. https://doi.org/10.1016/j.cnsns.2021.105847
Abu N, Ismail Z (2015) Forecasting sales of new vehicle with limited data using Bass diffusion model and grey theory. In: AIP Conference Proceedings
Li S, Chen H, Zhang G (2017) Comparison of the short-term forecasting accuracy on battery electric vehicle between modified bass and Lotka-Volterra model: a case study of China. J Adv Transp. https://doi.org/10.1155/2017/7801837
Wang FK, Hsiao YY, Chang KK (2012) Combining diffusion and grey models based on evolutionary optimization algorithms to forecast motherboard shipments. Math Probl Eng. https://doi.org/10.1155/2012/849634
Wang ZX, Dang YG, Pei LL (2011) On greying bass model and its application. J Grey Syst 23:7–14
Wang ZX (2013) A new grey bass equation for modelling new product diffusion. In: Applied Mechanics and Materials
Wang Y, Pei L, Wang Z (2017) The nls-based grey bass model for simulating new product diffusion. Int J Mark Res. https://doi.org/10.2501/IJMR-2017-045
Yu F, Wang L, Li X (2020) The effects of government subsidies on new energy vehicle enterprises: the moderating role of intelligent transformation. Energy Policy. https://doi.org/10.1016/j.enpol.2020.111463
Zhou N, Wu Q, Hu X (2020) Research on the policy evolution of China’s new energy vehicles industry. Sustain. https://doi.org/10.3390/su12093629
Yuan X, Liu X, Zuo J (2015) The development of new energy vehicles for a sustainable future: a review. Renew Sustain Energy Rev
Han L (2019) Research on private consumer's value perception and adoption intention of electric vehicles. USTC
Sun X, Xu S (2018) The impact of government subsidies on consumer preferences for alternative fuel vehicles. J Dalian Univ Technol https://doi.org/10.19525/j.issn1008-407x.2018.03.002
Ren B, Shao S, You J (2013) Development of a generalized Bass model for Chinese electric vehicles based on innovation diffusion theory. Soft Sci 27:17–22
Ye L, Xie N, Hu A (2021) A novel time-delay multivariate grey model for impact analysis of CO2 emissions from China’s transportation sectors. Appl Math Model. https://doi.org/10.1016/j.apm.2020.09.045
Chiu YJ, Hu YC, Jiang P et al (2020) A multivariate grey prediction model using neural networks with application to carbon dioxide emissions forecasting. Math Probl Eng. https://doi.org/10.1155/2020/8829948
Duan H, Luo X (2020) Grey optimization verhulst model and its application in forecasting coal-related CO2 emissions. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-020-09572-9
Mansfield E (1961) Technical change and the rate of imitation. Econometrica. https://doi.org/10.2307/1911817
Floyd A (1962) Trend forecasting: a methodology for figure of merit. Technological forecasting for industry and government.
Zhan C, Yeung LF (2011) Parameter estimation in systems biology models using spline approximation. BMC Syst Biol. https://doi.org/10.1186/1752-0509-5-14
Wen J, Wu C, Zhang R et al (2020) Rear-end collision warning of connected automated vehicles based on a novel stochastic local multivehicle optimal velocity model. Accid Anal Prev. https://doi.org/10.1016/j.aap.2020.105800
Wu W, Ma X, Zeng B et al (2020) Forecasting short-term solar energy generation in Asia Pacific using a nonlinear grey Bernoulli model with time power term. Energy Environ. https://doi.org/10.1177/0958305X20960700
Wu W, Ma X, Zhang Y et al (2020) A novel conformable fractional non-homogeneous grey model for forecasting carbon dioxide emissions of BRICS countries. Sci Total Environ 707:135447. https://doi.org/10.1016/j.scitotenv.2019.135447
Xiao Q, Gao M, Xiao X, Goh M (2020) A novel grey Riccati-Bernoulli model and its application for the clean energy consumption prediction. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2020.103863
Duan H, Xiao X, Xiao Q (2020) An inertia grey discrete model and its application in short-term traffic flow prediction and state determination. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04364-w
Wang MK, Chu YM, Song YQ (2016) Asymptotical formulas for Gaussian and generalized hypergeometric functions. Appl Math Comput. https://doi.org/10.1016/j.amc.2015.11.088
Ancarani LU, Gasaneo G (2009) Derivatives of any order of the Gaussian hypergeometric function 2F1(a, b, c; Z) with respect to the parameters a, b and c. J Phys A Math Theor. https://doi.org/10.1088/1751-8113/42/39/395208
Xiao X, Duan H (2020) A new grey model for traffic flow mechanics. Eng Appl Artif Intell 88:103350. https://doi.org/10.1016/j.engappai.2019.103350
Xiao Q, Shan M, Gao M et al (2020) Parameter optimization for nonlinear grey Bernoulli model on biomass energy consumption prediction. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2020.106538
Chen H, Xiao X, Wen J (2020) Novel multivariate compositional data’s model for structurally analyzing sub-industrial energy consumption with economic data. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05227-5
Zhan C, Wu F, Huang Z et al (2020) Analysis of collective action propagation with multiple recurrences. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04756-3
Zhan C, Li B, Zhong X et al (2020) A model for collective behaviour propagation: a case study of video game industry. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3686-8
Liu L, Wang Q, Wang J, Liu M (2016) A rolling grey model optimized by particle swarm optimization in economic prediction. Comput Intell. https://doi.org/10.1111/coin.12059
Xiao Q, Shan M, Gao M et al (2021) Evaluation of the coordination between china’s technology and economy using a grey multivariate coupling model. Technol Econ Dev Econ. https://doi.org/10.3846/tede.2020.13742
Gao M, Yang H, Xiao Q et al (2022) A novel method for carbon emission forecasting based on Gompertz’s law and fractional grey model: evidence from American industrial sector. Renew Energy. https://doi.org/10.1016/j.renene.2021.09.07
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The authors are grateful to the editor for their valuable comments. This work is supported by the National Natural Science Foundation of China (71871174).
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Li, X., Xiao, X. & Guo, H. A novel grey Bass extended model considering price factors for the demand forecasting of European new energy vehicles. Neural Comput & Applic 34, 11521–11537 (2022). https://doi.org/10.1007/s00521-022-07041-7
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DOI: https://doi.org/10.1007/s00521-022-07041-7