Machine learning-based solar radiation prediction across heterogeneous climatic zones using NASA POWER data: a case study in Nigeria

Abstract

Accurate prediction of solar radiation is essential for renewable energy planning, climate analysis, and agricultural productivity. Solar radiation data are required for the evaluation of solar power generation potential and crop water requirement computation, which is almost unavailable due to insufficient instrument for its measurement in many locations of the developing nations. Studies that applied several kernels of the Gaussian Process Regression (GPR) for the prediction of solar radiation are scarce. This study evaluates the performance of several machine learning models for solar radiation prediction across three climatic regions in Nigeria: Kano, Ibadan, and Onne. These locations represent distinct ecological zones ranging from the semi-arid savanna in northern Nigeria to the humid coastal rainforest in the southern region. Meteorological data used in this study were obtained from the NASA (National Aeronautics and Space Administration) POWER. Two machine learning approaches—Gaussian Process Regression (GPR) and Support Vector Machine (SVM)—were applied using different kernel configurations to predict solar radiation. Model performance was evaluated using standard statistical error metrics including Root Mean Square Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), Normalized Root Mean Square Error (NRMSE), and the coefficient of determination ( R 2 ). The results show that Gaussian Process Regression models generally produced the most accurate predictions across the study locations. In particular, the Exponential GPR model demonstrated the best performance in the humid regions of Ibadan and Onne, with RMSE values of 1.46 and 1.36 MJ.m 2 .d −1 , respectively. Conversely, the Fine SVM model achieved the best prediction accuracy in Kano, recording the lowest RMSE value of 2.16 MJ.m 2 . d −1 . These differences are attributed to variations in regional climatic conditions, where the relatively stable atmospheric conditions in the dry northern region favor SVM models, while the complex and highly variable atmospheric processes in humid regions are better captured by probabilistic models such as GPR. Overall, the findings highlight the importance of selecting appropriate machine learning models based on climatic characteristics when predicting solar radiation. The results provide useful insights for improving solar energy resource assessment and environmental modeling in Nigeria and similar tropical regions.