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

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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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Authors and Affiliations

  1. Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Algeria

    Mawloud Guermoui, Kacem Gairaa, Abdelaziz Rabehi, Djelloul Djafer & Said Benkaciali

Authors
  1. Mawloud Guermoui
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  2. Kacem Gairaa
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  3. Abdelaziz Rabehi
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  4. Djelloul Djafer
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  5. Said Benkaciali
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Correspondence to Abdelaziz Rabehi.

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Guermoui, M., Gairaa, K., Rabehi, A. et al. Estimation of the daily global solar radiation based on the Gaussian process regression methodology in the Saharan climate. Eur. Phys. J. Plus 133, 211 (2018). https://doi.org/10.1140/epjp/i2018-12029-7

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  • DOI: https://doi.org/10.1140/epjp/i2018-12029-7

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