An optimised grey buffer operator for forecasting the production and sales of new energy vehicles in China

[Display omitted] •A novel grey forecasting method is proposed to predict the production and sales of China’s new energy vehicles.•Accumulation and translation transformations are introduced into the grey buffer operator.•A genetic algorithm is used to optimise the parameters of the new operator to ensure high-precision prediction.•The proposed method exhibits much higher prediction accuracy than those of the classical grey forecasting methods. In recent years, under a series of policy shocks, the production and sales of new energy vehicles in China show the characteristics of trend mutation and non-smoothness. In order to forecast the production and sales of new energy vehicles in China, an optimised grey buffer operator is proposed by introducing accumulation and translation transformations. Meanwhile, a genetic algorithm is employed to ascertain its optimum parameters. Forecasting results indicate that the optimised buffer operator can significantly improve the adaptability of the grey model to the production and sales data of new energy vehicles in China, and exhibits much higher prediction accuracy than those of the classical buffer operator and grey model. Besides, the prediction results show that the production and sales of China’s new energy vehicles will continue to grow from 2018 to 2020, with an average annual growth rate of 27.53% and 30.49%, respectively.