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A GRNN Based Algorithm for Output Power Prediction of a PV Panel

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Artificial Intelligence in Renewable Energetic Systems (ICAIRES 2017)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 35))

Abstract

In this paper we investigated the reliability of a GRNN algorithm for the power prediction of a PV panel in order to minimize the effect of fast changing of the meteorological conditions. An experimental database of meteorological data (irradiance and module temperature) as input and electrical measure (power delivered by PV Panel) as output has been used. A database composed of two sets 97 values each one is used for training and validating the proposed GRNN model. The data used to develop the proposed algorithm are attained during two separated days from a PV panel within the MIS-Lab of UPJV, France. According to the gained results the algorithm can help to predict real instantaneous power even during temporary change in meteorological data.

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Acknowledgment

The authors would like to thank MIS-Lab of UPJV, France, for providing the facilities and databases to conduct this research.

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Authors and Affiliations

  1. Energetic Systems Modeling Laboratory, University of Biskra, Biskra, Algeria

    Kamal Kerbouche

  2. Renewable Energy Laboratory, Department of Electronics, University of Jijel, Jijel, Algeria

    S. Haddad & A. Mellit

  3. Modeling, Information and Systems Laboratory, UPJV, Amiens, France

    A. Rabhi, M. Hassan & A. El Hajjaji

Authors
  1. Kamal Kerbouche
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  2. S. Haddad
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  3. A. Rabhi
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  4. A. Mellit
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  5. M. Hassan
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  6. A. El Hajjaji
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Corresponding author

Correspondence to Kamal Kerbouche .

Editor information

Editors and Affiliations

  1. EPST-CDER, UDES, Unité de Développement des Equipements Solaires, Bou Ismaïl, Algeria

    Mustapha Hatti

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Kerbouche, K., Haddad, S., Rabhi, A., Mellit, A., Hassan, M., El Hajjaji, A. (2018). A GRNN Based Algorithm for Output Power Prediction of a PV Panel. In: Hatti, M. (eds) Artificial Intelligence in Renewable Energetic Systems. ICAIRES 2017. Lecture Notes in Networks and Systems, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-73192-6_30

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  • DOI: https://doi.org/10.1007/978-3-319-73192-6_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73191-9

  • Online ISBN: 978-3-319-73192-6

  • eBook Packages: EngineeringEngineering (R0)

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