Comparing different methods for estimating hourly solar ultraviolet radiation: Empirical models, artificial neural network and support vector machine

• Autor(a) principal: Teramoto, Érico Tadao [UNESP]
• Data de Publicação: 2020
• Outros Autores: Dos Santos, Cícero Manoel, Escobedo, João Francisco [UNESP], Dal Pai, Alexandre [UNESP], da Silva, Silvia Helena Modenese Gorla [UNESP]
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1590/0102-7786351010; http://hdl.handle.net/11449/200413
• topic: Input variables selection; Machine learning; Solar radiation

In the present paper, the comparison of three of the main estimation methods of solar radiation was performed: empirical models, Artificial Neural Network (ANN) and Support Vector Machine (SVM). Four classical empirical models were calibrated and validated in order to estimate hourly solar UV data in Botucatu, São Paulo State, Brazil. Taken the empirical models as reference of accuracy and set for input variables, the performance of ANN and SVM were assessed. Through the statistical parameters Mean Bias Error (MBE) and Mean Absolute Error (MAE) was confirmed the super-iority of the SVM over the ANN and empirical models. The SVM is capable to generate better results than ANN using a less number of input variables. Among all estimation methods, SVM using the set of input variables {UV0, KT } is con-sidered the best alternative due to the smaller number of input variables and relative precision.