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Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate

  • Mawloud Guermoui
  • Kacem Gairaa
  • Abdelaziz RabehiEmail author
  • Djelloul Djafer
  • Said Benkaciali
Regular Article

Abstract.

Accurate estimation of solar radiation is the major concern in renewable energy applications. Over the past few years, a lot of machine learning paradigms have been proposed in order to improve the estimation performances, mostly based on artificial neural networks, fuzzy logic, support vector machine and adaptive neuro-fuzzy inference system. The aim of this work is the prediction of the daily global solar radiation, received on a horizontal surface through the Gaussian process regression (GPR) methodology. A case study of Ghardaïa region (Algeria) has been used in order to validate the above methodology. In fact, several combinations have been tested; it was found that, GPR-model based on sunshine duration, minimum air temperature and relative humidity gives the best results in term of mean absolute bias error (MBE), root mean square error (RMSE), relative mean square error (rRMSE), and correlation coefficient (r) . The obtained values of these indicators are 0.67 MJ/m2, 1.15 MJ/m2, 5.2%, and 98.42%, respectively.

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Copyright information

© Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mawloud Guermoui
    • 1
  • Kacem Gairaa
    • 1
  • Abdelaziz Rabehi
    • 1
    Email author
  • Djelloul Djafer
    • 1
  • Said Benkaciali
    • 1
  1. 1.Unité de Recherche Appliquée en Energies Renouvelables, URAERCentre de Développement des Energies Renouvelables, CDERGhardaïaAlgeria

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