 A CRO-species optimization scheme for robust global solar radiation statistical downscalingS. Salcedo-Sanz, S. Jiménez-Fernández, A. Aybar-Ruiz, C. Casanova-Mateo, J. Sanz-Justo and R. García-Herrera Renewable Energy, 2017, vol. 111, issue C, 63-76 Abstract: This paper tackles the prediction of the global solar radiation (GSR) at a given point, using as predictive variables the outputs of a numerical weather model (the WRF meso-scale model) obtained at a different grid points. Prediction is obtained in this work using a Multilayer Perceptron (MLP) trained with Extreme Learning Machines (ELMs). Provided that the number of WRF outputs is vast, we propose the use of a Coral Reefs Optimization algorithm with species (CRO-SP) to obtain a reduced number of significant predictive variables, therefore improving the global solar radiation prediction attained without feature selection. The proposed system has been tested on real data from a radiometric station located at Toledo (Spain) and average best results of RMSE of 69.19 W/m2 have been achieved, resulting in a 21.62% improvement over the average prediction without considering the CRO-SP for the feature selection. Keywords: Coral reefs optimization algorithm; CRO with species; Global solar radiation; Solar energy; Extreme learning machines (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:111:y:2017:i:c:p:63-76 DOI: 10.1016/j.renene.2017.03.079 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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