Forecasting the sales and stock of electric vehicles using a novel self-adaptive optimized grey model

To alleviate the threatening pressure of energy shortage and environmental issues, the adoption of electric vehicles (EVs) is regarded as an effective measure. Therefore, accurate predictions of EVs sales and stock are crucial to deploying charging infrastructures, improving industrial policies, and providing credible references of the renewable sources demand in the transportation system. To this end, a new self-adaptive optimized grey model is proposed with the following improvements: first, a dynamic weighted sequence is generated to extract more value from the available observations by sufficiently highlighting the new data without information lapses. Second, the weighted coefficient and modified initial condition can adjust to various samples and thus augment the applicability of the proposed model. Third, Simpson?s formula is utilized to reconstruct the background value and then integrated with the modified initial condition to smooth the data saltations and further enhance the forecasting precision. To validate the rationality and efficacy of the novel model, four cases regarding the sales and stock of EVs are simulated by the proposed model compared with six benchmarks. As demonstrated in the empirical results, the novel model performs with the highest forecasting precision in most cases, which reveals that the optimization techniques exerted on the initial condition and background value can strikingly enhance the adaptability and prediction accuracy of the grey model. Thus, the novel model can be regarded as a promising tool for EVs prediction.

Automated summary

The study proposes a self-adaptive optimized grey model to enhance the prediction accuracy of EVs sales and stock through improvements such as generating dynamic weighted sequences, adjusting weighted coefficients and modified initial conditions, as well as reconstructing background values using Simpson's formula.