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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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    1. Bahgat, A.B.G & Helwa, N.H & Ahamd, G.E & El Shenawy, E.T, 2004. "Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks," Renewable Energy, Elsevier, vol. 29(3), pages 443-457.
    2. Mellit, A. & Benghanem, M. & Arab, A. Hadj & Guessoum, A., 2005. "An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria," Renewable Energy, Elsevier, vol. 30(10), pages 1501-1524.
    3. Al-Alawi, S.M. & Al-Hinai, H.A., 1998. "An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation," Renewable Energy, Elsevier, vol. 14(1), pages 199-204.
    4. Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Kanit, E. Galip, 2004. "Use of artificial neural networks for mapping of solar potential in Turkey," Applied Energy, Elsevier, vol. 77(3), pages 273-286, March.
    5. de Blas, M.A & Torres, J.L & Prieto, E & Garcı́a, A, 2002. "Selecting a suitable model for characterizing photovoltaic devices," Renewable Energy, Elsevier, vol. 25(3), pages 371-380.
    6. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    7. Alam, Shah & Kaushik, S.C. & Garg, S.N., 2009. "Assessment of diffuse solar energy under general sky condition using artificial neural network," Applied Energy, Elsevier, vol. 86(4), pages 554-564, April.
    8. Patra, Jagdish C. & Maskell, Douglas L., 2012. "Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks," Renewable Energy, Elsevier, vol. 44(C), pages 7-16.
    9. 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.
    10. Almonacid, F. & Rus, C. & Hontoria, L. & Muñoz, F.J., 2010. "Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods," Renewable Energy, Elsevier, vol. 35(5), pages 973-980.
    11. Jiang, Yingni, 2008. "Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models," Energy Policy, Elsevier, vol. 36(10), pages 3833-3837, October.
    12. 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.
    13. Kalogirou, Soteris A. & Florides, Georgios A. & Pouloupatis, Panayiotis D. & Panayides, Ioannis & Joseph-Stylianou, Josephina & Zomeni, Zomenia, 2012. "Artificial neural networks for the generation of geothermal maps of ground temperature at various depths by considering land configuration," Energy, Elsevier, vol. 48(1), pages 233-240.
    14. Dorvlo, Atsu S. S. & Jervase, Joseph A. & Al-Lawati, Ali, 2002. "Solar radiation estimation using artificial neural networks," Applied Energy, Elsevier, vol. 71(4), pages 307-319, April.
    15. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    16. 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.
    17. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    Full references (including those not matched with items on IDEAS)

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    1. Rajesh, R. & Mabel, M. Carolin, 2016. "Design and real time implementation of a novel rule compressed fuzzy logic method for the determination operating point in a photo voltaic system," Energy, Elsevier, vol. 116(P1), pages 140-153.
    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. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. Rajesh, R. & Carolin Mabel, M., 2015. "A comprehensive review of photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 231-248.

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