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Artificial neural network-based model for estimating the produced power of a photovoltaic module

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  • Mellit, A.
  • Sağlam, S.
  • Kalogirou, S.A.

Abstract

In this paper, a methodology to estimate the profile of the produced power of a 50 Wp Si-polycrystalline photovoltaic (PV) module is described. For this purpose, two artificial neural networks (ANNs) have been developed for use in cloudy and sunny days respectively. More than one year of measured data (solar irradiance, air temperature, PV module voltage and PV module current) have been recorded at the Marmara University, Istanbul, Turkey (from 1-1-2011 to 24-2-2012) and used for the training and validation of the models. Results confirm the ability of the developed ANN-models for estimating the power produced with reasonable accuracy. A comparative study shows that the ANN-models perform better than polynomial regression, multiple linear regression, analytical and one-diode models. The advantage of the ANN-models is that they do not need more parameters or complicate calculations unlike implicit models. The developed models could be used to forecast the profile of the produced power. Although, the methodology has been applied for one polycrystalline PV module, it could also be generalized for large-scale photovoltaic plants as well as for other PV technologies.

Suggested Citation

  • Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
    • Handle: RePEc:eee:renene:v:60:y:2013:i:c:p:71-78
    DOI: 10.1016/j.renene.2013.04.011
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    References listed on IDEAS

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    1. Bonanno, F. & Capizzi, G. & Graditi, G. & Napoli, C. & Tina, G.M., 2012. "A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module," Applied Energy, Elsevier, vol. 97(C), pages 956-961.
    2. Almonacid, F. & Rus, C. & Hontoria, L. & Muñoz, F.J., 2010. "Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods," Renewable Energy, Elsevier, vol. 35(5), pages 973-980.
    3. Almonacid, F. & Rus, C. & Hontoria, L. & Fuentes, M. & Nofuentes, G., 2009. "Characterisation of Si-crystalline PV modules by artificial neural networks," Renewable Energy, Elsevier, vol. 34(4), pages 941-949.
    4. Mellit, Adel & Kalogirou, Soteris A., 2011. "ANFIS-based modelling for photovoltaic power supply system: A case study," Renewable Energy, Elsevier, vol. 36(1), pages 250-258.
    5. Sandrolini, L. & Artioli, M. & Reggiani, U., 2010. "Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis," Applied Energy, Elsevier, vol. 87(2), pages 442-451, February.
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