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

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

Energy, 2023, vol. 263, issue PC

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.

Keywords: Grey Bernoulli prediction model; Time-varying parameters; Cuckoo search algorithm; Electric vehicles forecasting (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222027578
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222027578

DOI: 10.1016/j.energy.2022.125871

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().