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Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network

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  • Almonacid, F.
  • Fernández, Eduardo F.
  • Rodrigo, P.
  • Pérez-Higueras, P.J.
  • Rus-Casas, C.

Abstract

The peculiarities of High Concentrator Photovoltaic (HCPV) modules make it very difficult to estimate their output. There are some methods to estimate the maximum power of an HCPV module which provide good results, but they are not easy to apply for the following reasons: a) they do not offer a comprehensive explanation of the entire procedure, b) they need some intrinsic parameters of MJ (multi-junction) solar cells which are difficult to obtain, c) it is necessary to have either complex and expensive devices for indoor measurements, and/or an accurate and complete set of outdoor measurements. This paper is intended to propose a model based on Artificial Neural Networks (ANNs) to predict the maximum power of an HCPV module using easily measurable atmospheric parameters. To this end, a group of atmospheric parameters together with the maximum power of an HCPV module have been measured throughout a whole year at a research centre located in the south of Spain. The results showed that using atmospheric parameters, the proposed ANN is capable of estimating the maximum power of an HCPV module with a mean square root error of 3.29%. This model could be extended to other modules and other places.

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  • 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.
    • Handle: RePEc:eee:energy:v:53:y:2013:i:c:p:165-172
    DOI: 10.1016/j.energy.2013.02.024
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    2. 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.
    3. Rodrigo, P. & Fernández, Eduardo F. & Almonacid, F. & Pérez-Higueras, P.J., 2013. "Outdoor measurement of high concentration photovoltaic receivers operating with partial shading on the primary optics," Energy, Elsevier, vol. 61(C), pages 583-588.
    4. 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.
    5. 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.
    6. 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.
    7. Taghavifar, Hamid & Mardani, Aref, 2014. "Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices," Energy, Elsevier, vol. 68(C), pages 651-657.
    8. Adewole, Bamiji Z. & Abidakun, Olatunde A. & Asere, Abraham A., 2013. "Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner," Energy, Elsevier, vol. 61(C), pages 606-611.
    9. Taghavifar, Hamid & Mardani, Aref, 2014. "A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles," Energy, Elsevier, vol. 66(C), pages 569-576.
    10. Manuel Angel Gadeo-Martos & Antonio Jesús Yuste-Delgado & Florencia Almonacid Cruz & Jose-Angel Fernandez-Prieto & Joaquin Canada-Bago, 2019. "Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems," Energies, MDPI, vol. 12(3), pages 1-22, February.
    11. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    12. Renno, C. & Perone, A., 2021. "Experimental modeling of the optical and energy performances of a point-focus CPV system applied to a residential user," Energy, Elsevier, vol. 215(PA).
    13. Fernández, Eduardo F. & Almonacid, Florencia & Garcia-Loureiro, Antonio J., 2015. "Multi-junction solar cells electrical characterization by neuronal networks under different irradiance, spectrum and cell temperature," Energy, Elsevier, vol. 90(P1), pages 846-856.
    14. Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Pérez-Higueras, P.J., 2014. "Review of methods for the calculation of cell temperature in high concentration photovoltaic modules for electrical characterization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 478-488.
    15. 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.
    16. Leloux, Jonathan & Lorenzo, Eduardo & García-Domingo, Beatriz & Aguilera, Jorge & Gueymard, Christian A., 2014. "A bankable method of assessing the performance of a CPV plant," Applied Energy, Elsevier, vol. 118(C), pages 1-11.
    17. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    18. 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.
    19. 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.
    20. Rajesh, R. & Carolin Mabel, M., 2015. "A comprehensive review of photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 231-248.
    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.

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