Exploring the potential of tree-based ensemble methods in solar radiation modeling

This article provides the first comprehensive study to explore the potential of tree-based ensemble methods in modeling solar radiation. Gradient boosting, bagging and random forest (RF) models have been developed for estimating global, diffuse and normal radiation components in daily and hourly time-scales. The developed ensemble models have been compared to their corresponding multi-layer perceptron (MLP), support vector regression (SVR) and decision tree (DT) models. The results show that the suggested techniques are very reliable and accurate, despite being relatively simple. The average validation coefficients of determination (R2) for boosting, bagging and RF algorithms are (0.957, 0.971, 0.967) for the global irradiation model, (0.768, 0.786, 0.791) for the diffuse irradiation model, (0.769, 0.785, 0.792) for the normal irradiation model, (0.852, 0.890, 0.883) for the hourly global irradiance model, (0.778, 0.869, 0.853) for the diffuse irradiance model, and (0.797, 0.897, 0.880) for the normal irradiance model. In general, the bagging and RF algorithms showed better estimates than gradient boosting. However, the gradient boosting algorithm was the most stable with maximum increase of 10.32% in the test root mean square error, compared to 41.3% for the MLP algorithm. The SVR algorithm offers the best combination of stability and prediction accuracy. Nevertheless, its computational costs are up to 39 times the computational costs of ensemble methods. The new ensemble methods have been recommended for generating synthetic radiation data to be used for simulating and evaluating the performance of different solar energy systems.