Application of ERA-Interim, empirical models, and an artificial intelligence-based model for estimating daily solar radiation

Abstract

Solar radiation plays a pivotal role in the energy balance at the Earth's surface, evaporation, snow melting, water requirements of plants, and hydrological control of catchments. In this work, performance of ERA-Interim (a reanalysis dataset) was examined to estimate solar radiation at Ahvaz, BandarAbbas, and Kermanshah weather stations representing the even spatial distribution over Iran using eight empirical models and an artificial intelligence-based model (SVM: Support Vector Machine). In the calibration set, SVM exhibited the best performance with RMSEs of 249, 299 and 437 J.cm −2 .day −1 at the aforementioned stations, respectively. In validation set, SVM reduced the errors in the estimates of solar radiation by 2.5 and 7.3 percent compared to the best empirical model at Ahvaz station (Abdallah model, RMSE = 242 J.cm −2 .day −1 ) and Kermanshah station (Angstrom-Prescott model, RMSE = 315 J.cm −2 .day −1 ), respectively. During the validation at BandarAbbas station, Bahel and Abdallah model (RMSE = 309 J.cm −2 .day −1 ), Angstrom-Prescott model (RMSE = 310 J.cm −2 .day −1 ) and SVM (RMSE = 312 J.cm −2 .day −1 ) showed a relatively similar performance. The results also showed that the ERA-Interim dataset can be a comparatively suitable alternative to some of the empirical models, where radiation or the input parameters of empirical models are not directly measured, with RMSEs ​​of 382.81, 320.82 and 414.1 J.cm −2 .day −1 at Ahvaz, BandarAbbas, and Kermanshah stations, respectively (in validation phase); although its error rates are significant compared with the SVM model, and substituting it for artificial intelligence-based models is not recommended.