New Energy Electric Vehicle Industry Development Trends Forecast
A Ten-Year Outlook for China Based on MLPRegressor
DOI:
https://doi.org/10.54097/t1amhm15Keywords:
MLPRegressor, new energy electric vehicle, MAPE, MSLE.Abstract
This study focuses on developing the MLPRegressor model for predicting the future trends in China's new energy electric vehicle industry over the next decade. We employed a dataset comprising 16,500 records, encompassing 12 features, such as the number of public electric vehicles, the length of electric vehicle operational routes, and the number of electric vehicles owned by residents, for model training. We compared our model with traditional models like ARIMA, XGBRegressor, and Random Forest Regressor, using MAPE and MSLE as evaluation metrics. The final results revealed that our model exhibited superior performance in terms of loss, outperforming the other models significantly. This research not only provides businesses with more accurate strategies for developing the new energy electric vehicle industry but also serves as a crucial reference for the government in formulating environmentally friendly policies.
References
[1] Li C ,Feng D ,Chengfeng Z , et al.Spatial distribution patterns and influencing factors of China's new energy vehicle industry[J].Journal of Cleaner Production,2022,379.
[2] Yang G .The Effect of Government Subsidies on Innovation Capability of the New Energy Vehicle Industry[J].Nature Environment and Pollution Technology,2023,22(4):1857-1866.
[3] Jürgen Schmidhuber.Deep learning in neural networks: An overview[J].Neural Networks,2015.
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