Performance of the Angstrom-Prescott Model (A-P) and SVM and ANN techniques to estimate daily global solar irradiation in Botucatu/SP/Brazil - da Silva, Maurício Bruno Prado [UNESP]; Francisco Escobedo, João [UNESP]; Juliana Rossi, Taiza [UNESP]; dos Santos, Cícero Manoel; da Silva, Sílvia Helena Modenese Gorla [UNESP] (Elsevier, 2017)
This study describes the comparative study of different methods for estimating daily global solar irradiation (H): Angstrom-Prescott (A-P) model and two Machine Learning techniques (ML) – Support Vector Machine (SVM) and Artificial Neural Network (ANN). The H database was measured from 1996 to 2011 in Botucatu/SP/Brazil. Different combinations of input variables were adopted. MBE, RMSE, d Willmott, r and r2 statistical indicators obtained in the validation of A-P and SVM and ANN models showed that: SVM technique has better performance in estimating H than A-P and ANN models. A-P model has better performance in estimating H than ANN.
Keywords: Angstrom-Prescott; Artificial intelligence; Meteorological variables; Solar radiation; Statistical modeling