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Maximum power output prediction of HCPV FLATCON® module using an ANN approach

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  • Said, Mohamed Islam
  • Steiner, Marc
  • Siefer, Gerald
  • Arab, Amar Hadj

Abstract

The estimation of the electrical power output and energy yield of high concentration photovoltaic (HCPV) modules is a hard task because of the many parameters involved. In this work, we propose the usage of an Artificial Neural Networks (ANN) method to estimate the maximum power output of a FLATCON® CPV-module manufactured by Fraunhofer ISE. The advantage of the ANN is that it is a part of the Artificial Intelligence Deep Learning domain, which makes it easy to model complex systems. A detailed knowledge about the underlying module technology is not necessary. In this work, various meteorological parameters are tested as inputs of the ANN including spectral matching ratios for considering the spectral variation of the solar irradiance to evaluate the maximum power output of the HCPV module under study. Eleven scenarios considering different combinations of input parameters have been investigated. It is demonstrated that the ANN gives excellent results and allows for an accurate prediction of the HCPV module’s instantaneous power output with only a few amount of data used on training the ANN. The model has been tested using the measured data set of a FLATCON® CPV-module located on the rooftop of the Fraunhofer ISE in Freiburg, Germany. The module has been electrically characterized outdoors in the period from April 2013 to April 2014. The accuracy of the proposed ANN approach was analyzed using this measurement data in combination with error metrics. It was found that the best agreement between the measured and the predicted maximum power output was achieved when using direct normal irradiance, wind speed, ambient temperature and spectral matching ratios as input. The normalized root mean square error for the maximum power output over one year is found to be between 2.2 and 4.6%. The deviation of the modelled energy yield to the measured one is in the range of 0.2–2.2%.

Suggested Citation

  • Said, Mohamed Islam & Steiner, Marc & Siefer, Gerald & Arab, Amar Hadj, 2020. "Maximum power output prediction of HCPV FLATCON® module using an ANN approach," Renewable Energy, Elsevier, vol. 152(C), pages 1274-1283.
    • Handle: RePEc:eee:renene:v:152:y:2020:i:c:p:1274-1283
    DOI: 10.1016/j.renene.2020.01.106
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    References listed on IDEAS

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    1. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    2. García-Domingo, B. & Aguilera, J. & de la Casa, J. & Fuentes, M., 2014. "Modelling the influence of atmospheric conditions on the outdoor real performance of a CPV (Concentrated Photovoltaic) module," Energy, Elsevier, vol. 70(C), pages 239-250.
    3. Almonacid, F. & Fernández, E.F. & Mallick, T.K. & Pérez-Higueras, P.J., 2015. "High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature," Energy, Elsevier, vol. 84(C), pages 336-343.
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