Título Evolutionary artificial neural networks for accurate solar radiation prediction
Autores Guijo-Rubio D. , DURAN ROSAL, ANTONIO MANUEL, Gutiérrez P.A. , Gómez-Orellana A.M. , Casanova-Mateo C. , Sanz-Justo J. , Salcedo-Sanz S. , Hervás-Martínez C.
Publicación externa No
Medio Energy
Alcance Article
Naturaleza Científica
Cuartil JCR 1
Cuartil SJR 1
Impacto JCR 7.147
Impacto SJR 1.961
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089243647&doi=10.1016%2fj.energy.2020.118374&partnerID=40&md5=6adb578082c4f4f363e8730013402154
Fecha de publicacion 01/11/2020
ISI 000606796000001
Scopus Id 2-s2.0-85089243647
DOI 10.1016/j.energy.2020.118374
Abstract This paper evaluates the performance of different evolutionary neural network models in a problem of solar radiation prediction at Toledo, Spain. The prediction problem has been tackled exclusively from satellite-based measurements and variables, which avoids the use of data from ground stations or atmospheric soundings. Specifically, three types of neural computation approaches are considered: neural networks with sigmoid-based neurons, radial basis function units and product units. In all cases these neural computation algorithms are trained by means of evolutionary algorithms, leading to robust and accurate models for solar radiation prediction. The results obtained in the solar radiation estimation at the radiometric station of Toledo show an excellent performance of evolutionary neural networks tested. The structure sigmoid unit-product unit with evolutionary training has been shown as the best model among all tested in this paper, able to obtain an extremely accurate prediction of the solar radiation from satellite images data, and outperforming all other evolutionary neural networks tested, and alternative Machine Learning approaches such as Support Vector Regressors or Extreme Learning Machines. © 2020 Elsevier Ltd
Palabras clave Evolutionary algorithms; Forecasting; Machine learning; Solar radiation; Evolutionary artificial neural networks; Evolutionary neural network; Extreme learning machine; Machine learning approaches; Radial basis functions; Solar radiation estimation; Solar radiation predictions; Support vector regressor; Neural networks; artificial neural network; prediction; satellite data; satellite imagery; solar radiation; support vector machine; Castilla-La Mancha; Spain; Toledo [Castilla-La Mancha]
Miembros de la Universidad Loyola

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