A study of photovoltaic power plant siting based on multi criteria decision analysis and spatio temporal prediction

This study is dedicated to optimizing the site selection of photovoltaic power stations, aiming to address China's dual challenges in ensuring power supply and environmental protection by integrating multi criteria decision analysis and spatiotemporal prediction technology. The Spearman correlation analysis and heatmap revealed the high correlation between key factors affecting the site selection of photovoltaic power plants, and the Elastic Net algorithm was introduced to optimize the selection of independent variables, improving the stability and prediction accuracy of the model. The ARIMA (2,1,1) model was used to predict the future trend of electricity supply in China, and the results showed that the electricity supply in China will reach 2179 GW · h by 2060. A comprehensive evaluation model for photovoltaic power station site selection was constructed based on multi criteria decision analysis, ensuring reasonable planning of power station locations, fully utilizing local natural conditions, and reducing pressure on the power grid. The experiment verified the effectiveness and reliability of the model, providing theoretical basis and technical support for promoting the development of China's photovoltaic industry.