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A neural network based computational model to predict the output power of different types of photovoltaic cells

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Listed:
  • WenBo Xiao
  • Gina Nazario
  • HuaMing Wu
  • HuaMing Zhang
  • Feng Cheng

Abstract

In this article, we introduced an artificial neural network (ANN) based computational model to predict the output power of three types of photovoltaic cells, mono-crystalline (mono-), multi-crystalline (multi-), and amorphous (amor-) crystalline. The prediction results are very close to the experimental data, and were also influenced by numbers of hidden neurons. The order of the solar generation power output influenced by the external conditions from smallest to biggest is: multi-, mono-, and amor- crystalline silicon cells. In addition, the dependences of power prediction on the number of hidden neurons were studied. For multi- and amorphous crystalline cell, three or four hidden layer units resulted in the high correlation coefficient and low MSEs. For mono-crystalline cell, the best results were achieved at the hidden layer unit of 8.

Suggested Citation

  • WenBo Xiao & Gina Nazario & HuaMing Wu & HuaMing Zhang & Feng Cheng, 2017. "A neural network based computational model to predict the output power of different types of photovoltaic cells," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-8, September.
    • Handle: RePEc:plo:pone00:0184561
    DOI: 10.1371/journal.pone.0184561
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    1. Stanisław Duer & Jan Valicek & Jacek Paś & Marek Stawowy & Dariusz Bernatowicz & Radosław Duer & Marcin Walczak, 2021. "Neural Networks in the Diagnostics Process of Low-Power Solar Plant Devices," Energies, MDPI, vol. 14(9), pages 1-18, May.
    2. Moreira, M.O. & Balestrassi, P.P. & Paiva, A.P. & Ribeiro, P.F. & Bonatto, B.D., 2021. "Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).

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