Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas

Abstract This study aims to evaluate the potential of Bayesian additive regression trees (BART) for predicting global and diffuse solar radiation. Long-term daily weather data were collected at four stations in arid and humid areas. Models with different input combinations were created. The default parameters within R language package of BART were used. Model accuracy was assessed with Pearson correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), relative root mean square error (RRMSE), and Nash-Sutcliffe efficiency coefficient (NSE). Taylor diagram was applied to illustrate model performance. On average, the model with sunshine duration, theoretical sunshine duration, mean temperature, maximum temperature, minimum temperature, relative humidity, and rainfall performed best for predicting global solar radiation, with mean R of 0.973, RMSE of 1.685 MJ/m2d, NSE of 0.944, RRMSE of 0.124, and MAE of 1.265 MJ/m2d. The model with sunshine duration, theoretical sunshine duration, global solar radiation, extraterrestrial solar radiation, and day of year outperformed others for predicting diffuse solar radiation, with mean R of 0.912, RMSE of 1.291 MJ/m2d, NSE of 0.827, RRMSE of 0.214, and MAE of 0.933 MJ/m2d. The results showed that BART was a suitable method for predicting global and diffuse solar radiation using climatic variables.