Influencing Factors Analysis of China's Photovoltaic Industry Development Based on Machine Learning Method
To reveal the spatial distribution disparities in China's photovoltaic(PV)industry development and to im-prove model fitting capacity of nonlinear complex system,on the basis of clarifying the influence of multiple linear regres-sion analysis indicators on the photovoltaic industry,the RF,Adaboost,GBDT,and XGBoost algorithms optimized by metaheuristic algorithm were utilized to identify the crucial influencing factors.The results show that solar energy re-sources,nuclear power generation,number of patents granted,per capita electricity consumption,land area for construc-tion,and silicon reserves are the pivotal factors influencing PV industry development.Wind power generation shows a significant positive correlation with PV power generation,yet wind power generation is significantly negatively correlated with the clustering of photovoltaic enterprises.The development of centralized PV power stations emphasizes adaptability to local conditions,while the proliferation of distributed PV stations is chiefly influenced by green transformation policies within the power system.The research results offer reference for the government in formulating policies to drive the de-velopment of the PV industry.
万方数据
维普
