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Estimation of the energy of a PV generator using artificial neural network

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  • Almonacid, F.
  • Rus, C.
  • Pérez, P.J.
  • Hontoria, L.

Abstract

The integration of grid-connected photovoltaic (GCPVS) systems into urban buildings is very popular in industrialized countries. Many countries enhance the international collaboration efforts which accelerate the development and deployment of photovoltaic solar energy as a significant and sustainable renewable energy option. A previous method, based on artificial neural networks (ANNs), has been developed to electrical characterisation of PV modules. This method was able to generate V–I curves of si-crystalline PV modules for any irradiance and module cell temperature. The results showed that the proposed ANN introduced a good accurate prediction for si-crystalline PV modules performance when compared with the measured values. Now, this method, based on ANNs, is going to be applied to obtain a suitable value of the power provided by a photovoltaic installation. Specifically this method is going to be applied to obtain the power provided by a particular installation, the “Univer generator”, since modules used in these works were the same as the ones used in this photovoltaic generator.

Suggested Citation

  • Almonacid, F. & Rus, C. & Pérez, P.J. & Hontoria, L., 2009. "Estimation of the energy of a PV generator using artificial neural network," Renewable Energy, Elsevier, vol. 34(12), pages 2743-2750.
    • Handle: RePEc:eee:renene:v:34:y:2009:i:12:p:2743-2750
    DOI: 10.1016/j.renene.2009.05.020
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    References listed on IDEAS

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    1. 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.
    2. Drif, M. & Pérez, P.J. & Aguilera, J. & Aguilar, J.D., 2008. "A new estimation method of irradiance on a partially shaded PV generator in grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 33(9), pages 2048-2056.
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    5. Mirian Jiménez-Torres & Catalina Rus-Casas & Lenin Guillermo Lemus-Zúiga & Leocadio Hontoria, 2017. "The Importance of Accurate Solar Data for Designing Solar Photovoltaic Systems—Case Studies in Spain," Sustainability, MDPI, vol. 9(2), pages 1-14, February.
    6. Jinpeng Liu & Yun Long & Xiaohua Song, 2017. "A Study on the Conduction Mechanism and Evaluation of the Comprehensive Efficiency of Photovoltaic Power Generation in China," Energies, MDPI, vol. 10(5), pages 1-22, May.
    7. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    8. Sommerfeldt, Nelson & Madani, Hatef, 2017. "Revisiting the techno-economic analysis process for building-mounted, grid-connected solar photovoltaic systems: Part one – Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1379-1393.
    9. Lazrak, Amine & Boudehenn, François & Bonnot, Sylvain & Fraisse, Gilles & Leconte, Antoine & Papillon, Philippe & Souyri, Bernard, 2016. "Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation," Renewable Energy, Elsevier, vol. 86(C), pages 1009-1022.
    10. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
    11. Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Pérez-Higueras, P.J., 2013. "Models for the electrical characterization of high concentration photovoltaic cells and modules: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 752-760.
    12. 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.
    13. Ouammi, Ahmed & Zejli, Driss & Dagdougui, Hanane & Benchrifa, Rachid, 2012. "Artificial neural network analysis of Moroccan solar potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4876-4889.
    14. Samuel R. Fahim & Hany M. Hasanien & Rania A. Turky & Shady H. E. Abdel Aleem & Martin Ćalasan, 2022. "A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction," Energies, MDPI, vol. 15(23), pages 1-56, November.
    15. Ruiz-Arias, J.A. & Terrados, J. & Pérez-Higueras, P. & Pozo-Vázquez, D. & Almonacid, G., 2012. "Assessment of the renewable energies potential for intensive electricity production in the province of Jaén, southern Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2994-3001.
    16. Almonacid, F. & Rus, C. & Pérez-Higueras, P. & Hontoria, L., 2011. "Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks," Energy, Elsevier, vol. 36(1), pages 375-384.
    17. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.
    18. Kwak, Younghoon & Mun, Sun-Hye & Park, Chang-Dae & Lee, Sang-Moon & Huh, Jung-Ho, 2022. "Statistical analysis of power generation of semi-transparent photovoltaic (STPV) for diversity in building envelope design: A mock-up test by azimuth and tilt angles," Renewable Energy, Elsevier, vol. 188(C), pages 651-669.
    19. 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.
    20. Fernández, Eduardo F. & Almonacid, Florencia, 2014. "Spectrally corrected direct normal irradiance based on artificial neural networks for high concentrator photovoltaic applications," Energy, Elsevier, vol. 74(C), pages 941-949.

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    PV generator; Artificial neural network;

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