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Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models

Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models

Fernando Antonanzas-Torres, Andres Sanz-Garcia, Javier Antonanzas-Torres, Oscar Perpiñán-Lamiguero, Francisco Javier Martínez-de-Pisón-Ascacibar
Copyright: © 2015 |Pages: 22
ISBN13: 9781466666313|ISBN10: 1466666315|EISBN13: 9781466666320
DOI: 10.4018/978-1-4666-6631-3.ch001
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MLA

Antonanzas-Torres, Fernando, et al. "Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models." Soft Computing Applications for Renewable Energy and Energy Efficiency, edited by Maria del Socorro García Cascales, et al., IGI Global, 2015, pp. 1-22. https://doi.org/10.4018/978-1-4666-6631-3.ch001

APA

Antonanzas-Torres, F., Sanz-Garcia, A., Antonanzas-Torres, J., Perpiñán-Lamiguero, O., & Martínez-de-Pisón-Ascacibar, F. J. (2015). Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models. In M. Cascales, J. Lozano, A. Arredondo, & C. Corona (Eds.), Soft Computing Applications for Renewable Energy and Energy Efficiency (pp. 1-22). IGI Global. https://doi.org/10.4018/978-1-4666-6631-3.ch001

Chicago

Antonanzas-Torres, Fernando, et al. "Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models." In Soft Computing Applications for Renewable Energy and Energy Efficiency, edited by Maria del Socorro García Cascales, et al., 1-22. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6631-3.ch001

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Abstract

Most of the research on estimating Solar Global Irradiation (SGI) is based on the development of parametric models. However, the use of methods based on the use of statistics and machine-learning theories can provide a significant improvement in reducing the prediction errors. The chapter evaluates the performance of different Soft Computing (SC) methods, such as support vector regression and artificial neural networks-multilayer perceptron, in SGI modeling against classical parametric and lineal models. SC methods demonstrate a higher generalization capacity applied to SGI modeling than classic parametric models. As a result, SC models suppose an alternative to satellite-derived models to estimate SGI in near-to-present time in areas in which no pyranometers are installed nearby.

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