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Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules

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  • Piliougine, Michel
  • Elizondo, David
  • Mora-López, Llanos
  • Sidrach-de-Cardona, Mariano

Abstract

In this paper, we propose the use of a methodology to characterise the electrical parameters of several thin-film photovoltaic module technologies. This methodology allows us to use not only solar irradiance and module temperature as classical models do, but also spectral distribution of solar radiation. The methodology is based on the use of neural network models. From all measured I–V curves of a module, a previous selection of them has been used in order to train the neural network model. This selection is performed using a Kohonen self-organising map fed with spectral data. This spectral information has been added as an input to the neural network itself. The results show that the incorporation of spectral measurements to simulate thin-film modules improves significantly both the fitting of the predicted I–V curve to the measured one and the peak power point estimation.

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  • Piliougine, Michel & Elizondo, David & Mora-López, Llanos & Sidrach-de-Cardona, Mariano, 2013. "Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules," Applied Energy, Elsevier, vol. 112(C), pages 610-617.
    • Handle: RePEc:eee:appene:v:112:y:2013:i:c:p:610-617
    DOI: 10.1016/j.apenergy.2013.05.053
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    Cited by:

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    5. Liukkonen, M. & Hiltunen, T., 2014. "Adaptive monitoring of emissions in energy boilers using self-organizing maps: An application to a biomass-fired CFB (circulating fluidized bed)," Energy, Elsevier, vol. 73(C), pages 443-452.
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    9. Almonacid, Florencia & Rodrigo, Pedro & Fernández, Eduardo F., 2016. "Determination of the current–voltage characteristics of concentrator systems by using different adapted conventional techniques," Energy, Elsevier, vol. 101(C), pages 146-160.
    10. Thomas Huld & Ana M. Gracia Amillo, 2015. "Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and Solar Spectrum," Energies, MDPI, vol. 8(6), pages 1-23, June.
    11. García-Domingo, B. & Piliougine, M. & Elizondo, D. & Aguilera, J., 2015. "CPV module electric characterisation by artificial neural networks," Renewable Energy, Elsevier, vol. 78(C), pages 173-181.
    12. Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    13. Benhammane, Mousaab & Notton, Gilles & Pichenot, Grégoire & Voarino, Philippe & Ouvrard, David, 2021. "Overview of electrical power models for concentrated photovoltaic systems and development of a new operational model with easily accessible inputs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    14. Torres-Ramírez, M. & Nofuentes, G. & Silva, J.P. & Silvestre, S. & Muñoz, J.V., 2014. "Study on analytical modelling approaches to the performance of thin film PV modules in sunny inland climates," Energy, Elsevier, vol. 73(C), pages 731-740.
    15. Orozco-Gutierrez, M.L. & Ramirez-Scarpetta, J.M. & Spagnuolo, G. & Ramos-Paja, C.A., 2014. "A method for simulating large PV arrays that include reverse biased cells," Applied Energy, Elsevier, vol. 123(C), pages 157-167.

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