A Prediction Model for Electric Vehicle Sales Using Machine Learning Approaches
Jen-Yin Yeh, Yuting Wang
Abstract
The electric vehicle (EV) market is booming, but EV market trends vary by region. This study draws on the environmental, economic, and human development factors of 31 countries to predict sales of EVs. Based on machine learning (ML) algorithms and the PLS method, the authors constructed an EV sales performance prediction model and carried out the experiments. The experimental results demonstrate that ML algorithms can effectively achieve the desired accuracy and predictive performance levels. At the same time, this study investigates the relationship between quality training indicators and EV sales. CO2 emissions, PM2.5, consumer price index (CPI), renewable energy, and life expectancy are found to be significantly positive related to EV sales. The proposed model can be used globally by governments as a decision support tool to impose policies encouraging the adoption of EVs and develop sustainable strategies.