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A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

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  • Bonanno, F.
  • Capizzi, G.
  • Graditi, G.
  • Napoli, C.
  • Tina, G.M.

Abstract

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I–V and P–V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:97:y:2012:i:c:p:956-961
    DOI: 10.1016/j.apenergy.2011.12.085
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    13. 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.
    14. Konstantinos Mira & Francesca Bugiotti & Tatiana Morosuk, 2023. "Artificial Intelligence and Machine Learning in Energy Conversion and Management," Energies, MDPI, vol. 16(23), pages 1-36, November.
    15. Ghani, F. & Rosengarten, G. & Duke, M. & Carson, J.K., 2014. "The numerical calculation of single-diode solar-cell modelling parameters," Renewable Energy, Elsevier, vol. 72(C), pages 105-112.
    16. Karabacak, Kerim & Cetin, Numan, 2014. "Artificial neural networks for controlling wind–PV power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 804-827.
    17. Lo Brano, Valerio & Ciulla, Giuseppina, 2013. "An efficient analytical approach for obtaining a five parameters model of photovoltaic modules using only reference data," Applied Energy, Elsevier, vol. 111(C), pages 894-903.
    18. Yaser I. Alamin & Mensah K. Anaty & José Domingo Álvarez Hervás & Khalid Bouziane & Manuel Pérez García & Reda Yaagoubi & María del Mar Castilla & Merouan Belkasmi & Mohammed Aggour, 2020. "Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network," Energies, MDPI, vol. 13(13), pages 1-16, July.
    19. Oliva, Diego & Cuevas, Erik & Pajares, Gonzalo, 2014. "Parameter identification of solar cells using artificial bee colony optimization," Energy, Elsevier, vol. 72(C), pages 93-102.
    20. Zhao, Yong-Ping & Hu, Qian-Kun & Xu, Jian-Guo & Li, Bing & Huang, Gong & Pan, Ying-Ting, 2018. "A robust extreme learning machine for modeling a small-scale turbojet engine," Applied Energy, Elsevier, vol. 218(C), pages 22-35.
    21. Yadav, Amit Kumar & Chandel, S.S., 2017. "Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 955-969.
    22. Luo, Yongqiang & Zhang, Ling & Wu, Jing & Liu, Zhongbing & Wu, Zhenghong & He, Xihua, 2017. "Dynamical simulation of building integrated photovoltaic thermoelectric wall system: Balancing calculation speed and accuracy," Applied Energy, Elsevier, vol. 204(C), pages 887-897.
    23. Yadav, Amit Kumar & Sharma, Vikrant & Malik, Hasmat & Chandel, S.S., 2018. "Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based Radial Basis Function Neural Network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2115-2127.

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