Extreme Gradient Boosting: A Machine Learning Technique for Daily Global Solar Radiation Forecasting on Tilted Surfaces

Abstract

Enhancing solar irradiance and accurate forecasting is required for improved performance of photovoltaic and solar thermal systems. In this study, Extreme Gradient Boosting (XGBoost) model was developed using three input parameters (time, day number, and horizontal solar radiation) and was utilized to forecast daily global solar radiation on tilted surfaces. The proposed model was built using XGBRegressor with five generations, 100 n estimators, and a learning rate of 0.1. Three statistical metrics, such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), were used to compare the model’s results to observed solar radiation data from the Nation Centre for Energy, Research and Development, University of Nigeria, Nsukka. The results showed improved prediction accuracy and XGBoost capability to estimate daily global solar radiation on tilted surfaces. In the training section, the proposed model had a statistical performance of R2 = 0.9977, RMSE = 1.6988, and MAE = 1.081, and in the testing section, R2 = 0.9934, RMSE = 2.8558, and MAE = 2.033. XGBoost model demonstrated a better performance when compared with other models in the literature. As a result, the proposed model provides an effective approach for estimating solar radiation.