Generation of daily global solar irradiation with support vector machines for regression

  1. Antonanzas-Torres, F. 1
  2. Urraca, R. 1
  3. Antonanzas, J. 1
  4. Fernandez-Ceniceros, J. 1
  5. Martinez-De-Pison, F.J. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Energy Conversion and Management

ISSN: 0196-8904

Año de publicación: 2015

Volumen: 96

Páginas: 277-286

Tipo: Artículo

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DOI: 10.1016/J.ENCONMAN.2015.02.086 SCOPUS: 2-s2.0-84924732702 WoS: WOS:000353729200026 GOOGLE SCHOLAR

Otras publicaciones en: Energy Conversion and Management

Proyectos relacionados

SISTEMA INTELIGENTE EN TIEMPO REAL BASADO EN REDES DE SENSORES Y MINERÍA DE DATOS PARA LA TOMA DE DECISIONES EN PROCESOS DE CONSTRUCCIÓN DE EDIFICIOS E INFRAESTRUCTURAS (CONOBUILD)

2013/00131/001

Resumen

Solar global irradiation is barely recorded in isolated rural areas around the world. Traditionally, solar resource estimation has been performed using parametric-empirical models based on the relationship of solar irradiation with other atmospheric and commonly measured variables, such as temperatures, rainfall, and sunshine duration, achieving a relatively high level of certainty. Considerable improvement in soft-computing techniques, which have been applied extensively in many research fields, has lead to improvements in solar global irradiation modeling, although most of these techniques lack spatial generalization. This new methodology proposes support vector machines for regression with optimized variable selection via genetic algorithms to generate non-locally dependent and accurate models. A case of study in Spain has demonstrated the value of this methodology. It achieved a striking reduction in the mean absolute error (MAE)-41.4% and 19.9%-as compared to classic parametric models; Bristow & Campbell and Antonanzas-Torres et al., respectively. © 2015 Elsevier Ltd All rights reserved.