Research on the change of new energy vehicle indicators based on ARIMA algorithm and machine learning
Dongting Xie · 2024
In recent years, new energy electric vehicles have been developing rapidly and are well known in the world for their low pollution and low energy consumption. This study establishes a mathematical model to analyze multiple factors related to electric vehicles. A multiple linear regression model and a gray correlation analysis model are used to calculate the coefficients of correlation between the sales of electric vehicles, the price of fuel and the number of charging piles using data related to the number of charging piles, the price of fuel and the sales of new vehicles in China’s energy-electric vehicle market for the period of 2014-2022, and the results illustrate that the number of charging piles and the price of fuel will contribute to the development of the sales industry of electric vehicles; these data show a gradual increasing trend in the last nine years. Data showed a trend of gradual increase. Subsequently, an ARIMA model was developed to predict that the future sales volume and other indicators are also expected to show a positive trend. Global market share data for electric vehicles was collected. As a result, the size of the Chinese electric vehicle market occupies a significant position in the global scale. Therefore, the global market share data with the stock and the market penetration of electric vehicles in China with the impact of conventional cars to infer the development of electric vehicles globally. The growth of electric vehicles has a strong negative correlation with conventional automobiles, indicating a competitive relationship. This indicates that the growth of electric the automobile industry will inhibit the growth of the traditional automobile industry. Gather information about policies and relevant data. The development of new energy vehicles in China comes from the United States, Japan, and Germany. Using the random forest model, determine the impact of development among the three. Among them, the policies of the United States and Germany have a more significant impact on China thus affecting the development of China’s new energy electric vehicle industry. The Ridge regression model, combined with logarithmic transformation, is used to obtain the changes of the three main indicators. The results show that when all gasoline-fueled vehicles are replaced by new energy electric vehicles, there is a general decrease in carbon emissions and the level of pollutants in the environment. This indicates that the promotion of new energy electric vehicles is conducive to the protection of the ecological environment.