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Research on New Energy Vehicle Development Prediction based on Random Forest Model and gray Prediction
Abstract
This paper focuses on predicting the development of new energy vehicles (NEVs) using random forest model and gray Prediction models. New energy vehicles, including hybrid, pure electric, and fuel cell electric vehicles, have seen rapid growth due to their low pollution, low energy consumption, and strong peak load capacity. This research aims to analyze the factors influencing the NEV industry and forecast its future development. The gray Prediction method, known for addressing small sample sizes and incomplete data, is used to forecast the conservative quantity of NEVs in this paper. Meanwhile, the random forest model regression model evaluates the impact of various factors on the market share of traditional fuel vehicles. Key variables include production, fuel prices, charging stations, subsidies, and sales volumes. Results indicate that the NEV market in China will experience rapid growth over the next decade, with increasing market penetration and sales. Factors such as government subsidies and technological advancements significantly influence the traditional fuel vehicle market.
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