Improving Solar Radiation Prediction in China: A Stacking Model Approach with Categorical Boosting Feature Selection

Solar radiation is an important energy source, and accurately predicting it [daily global and diffuse solar radiation (Rs and Rd)] is essential for research on surface energy exchange, hydrologic systems, and agricultural production. However, Rs and Rd estimation relies on meteorological data and related model parameters, which leads to inaccuracy in some regions. To improve the estimation accuracy and generalization ability of the Rs and Rd models, 17 representative radiation stations in China were selected. The categorical boosting (CatBoost) feature selection algorithm was utilized to construct a novel stacking model from sample and parameter diversity perspectives. The results revealed that the characteristics related to sunshine duration (n) and ozone (O3) significantly affect solar radiation prediction. The proposed new ensemble model framework had better accuracy than base models in root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and global performance index (GPI). The solar radiation prediction model is more applicable to coastal areas, such as Shanghai and Guangzhou, than to inland regions of China. The range and mean of RMSE, MAE, and R2 for Rs prediction are 1.5737–3.7482 (1.9318), 1.1773–2.6814 (1.4336), and 0.7597–0.9655 (0.9226), respectively; for Rd prediction, they are 1.2589–2.9038 (1.8201), 0.9811–2.1024 (1.3493), and 0.5153–0.9217 (0.7248), respectively. The results of this study can provide a reference for Rs and Rd estimation and related applications in China.