Current status and future trends of the evaluation of solar global irradiation using soft-computing-based models

  1. Antonanzas-Torres, F. 3
  2. Sanz-Garcia, A. 2
  3. Antonanzas-Torres, J. 3
  4. Perpiñán-Lamiguero, O. 1
  5. Martínez-de-Pisón-Ascacibar, F.J. 3
  1. 1 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

  2. 2 University of Helsinki
    info

    University of Helsinki

    Helsinki, Finlandia

    ROR https://ror.org/040af2s02

  3. 3 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

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Libro:
Soft Computing Applications for Renewable Energy and Energy Efficiency

ISBN: 9781466666320; 1466666315; 9781466666313

Año de publicación: 2014

Páginas: 1-22

Tipo: Capítulo de Libro

DOI: 10.4018/978-1-4666-6631-3.CH001 SCOPUS: 2-s2.0-84928659059 WoS: WOS:000419544500003 GOOGLE SCHOLAR

Resumen

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. © 2015, IGI Global. All rights reserved.