Global horizontal irradiance modeling from environmental inputs using machine learning with automatic model selection

Solar radiation prediction is essential in load control on electrical networks allowing a balanced energy supply in accordance with the network demand. However, stations needing specific equipment, which is typically expensive and rare, to collect the data needed for such predictions. As a result of the tension between the demand for data and the challenge of acquiring it, the adoption of novel, low-cost technological solutions is necessary to address pressing issues. Successful predictions of solar radiation at many sites across the world have been made using machine learning (ML) models. This research develops a more straightforward strategy for efficient input variable selection. The proposed framework uses the most relevant environmental information to build an efficient model to predict global horizontal irradiance for the Dar es Salaam region, Tanzania. Four different ML models are developed and compared to predict solar radiation, i.e., Multivariate Adaptive Regression Spline (MARS), Support Vector Regression (SVR), ensemble Extreme Gradient Boosting (XGB), and polynomial Ridge Regression (RR). A total of 252 different scenarios are analyzed in the search for the best combination between the ML models and variables subset. The results showed that all optimized machine learning models could predict global horizontal irradiation with small error rates. The research also indicates that the proper choice of the available environmental variables can lead to less complex modeling with improved predictions.