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

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 (