Performance of the Angstrom-Prescott Model (A-P) and SVM and ANN techniques to estimate daily global solar irradiation in Botucatu/SP/Brazil

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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]

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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.



Angstrom-Prescott, Artificial intelligence, Meteorological variables, Solar radiation, Statistical modeling

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Journal of Atmospheric and Solar-Terrestrial Physics, v. 160, p. 11-23.