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Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology

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  • Almonacid, Florencia
  • Fernandez, Eduardo F.
  • Mellit, Adel
  • Kalogirou, Soteris

Abstract

Concentrator photovoltaics (CPV) is considered to be one of the most promising renewable energy components that could lead to a reduction on the dependence on fossil fuels. The aim of CPV technology is to lower the cost of the system by reducing the semiconductor material, and replacing it by cheap optical devices that concentrate the light received from the sun on a small-size solar cell. The electrical characterization of devices based on this technology however, is inherently different and more complex than that of the traditional PV devices. Due to the advantages offered by the Artificial Neuron Networks (ANNs) to solve complex and non-linear problems, and the great level of complexity of electrical modelling of CPV devices, in recent years, several authors have applied a variety of ANNs to solve issues related to CPV technology. In this paper, a review of the ANNs developed to address various topics related with both, low and high concentrator photovoltaics, is presented. Moreover, a review of the ANN-based models to predict the main environmental parameters that affect the performance of CPV systems operating outdoors is also provided. Published papers presented show the potential of the ANNs as a powerful tool for modelling the CPV technology.

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  • 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.
    • Handle: RePEc:eee:rensus:v:75:y:2017:i:c:p:938-953
    DOI: 10.1016/j.rser.2016.11.075
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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. 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.
    3. 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.
    4. 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.
    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. Nishimura, A. & Hayashi, Y. & Tanaka, K. & Hirota, M. & Kato, S. & Ito, M. & Araki, K. & Hu, E.J., 2010. "Life cycle assessment and evaluation of energy payback time on high-concentration photovoltaic power generation system," Applied Energy, Elsevier, vol. 87(9), pages 2797-2807, September.
    7. Almonacid, F. & Pérez-Higueras, P. & Rodrigo, P. & Hontoria, L., 2013. "Generation of ambient temperature hourly time series for some Spanish locations by artificial neural networks," Renewable Energy, Elsevier, vol. 51(C), pages 285-291.
    8. López, G. & Batlles, F.J. & Tovar-Pescador, J., 2005. "Selection of input parameters to model direct solar irradiance by using artificial neural networks," Energy, Elsevier, vol. 30(9), pages 1675-1684.
    9. Altan Dombaycı, Ömer & Gölcü, Mustafa, 2009. "Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey," Renewable Energy, Elsevier, vol. 34(4), pages 1158-1161.
    10. Almonacid, Florencia & Rodrigo, Pedro & Fernández, Eduardo F., 2016. "Determination of the current–voltage characteristics of concentrator systems by using different adapted conventional techniques," Energy, Elsevier, vol. 101(C), pages 146-160.
    11. Tasadduq, Imran & Rehman, Shafiqur & Bubshait, Khaled, 2002. "Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia," Renewable Energy, Elsevier, vol. 25(4), pages 545-554.
    12. Pérez-Higueras, P. & Muñoz, E. & Almonacid, G. & Vidal, P.G., 2011. "High Concentrator PhotoVoltaics efficiencies: Present status and forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1810-1815, May.
    13. Zahedi, A., 2011. "Review of modelling details in relation to low-concentration solar concentrating photovoltaic," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1609-1614, April.
    14. 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.
    15. Chemisana, Daniel, 2011. "Building Integrated Concentrating Photovoltaics: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 603-611, January.
    16. 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.
    17. 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.
    18. Alam, Shah & Kaushik, S.C. & Garg, S.N., 2006. "Computation of beam solar radiation at normal incidence using artificial neural network," Renewable Energy, Elsevier, vol. 31(10), pages 1483-1491.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. Menoufi, Karim & Chemisana, Daniel & Rosell, Joan I., 2013. "Life Cycle Assessment of a Building Integrated Concentrated Photovoltaic scheme," Applied Energy, Elsevier, vol. 111(C), pages 505-514.
    24. 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.
    25. 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.
    26. Xie, W.T. & Dai, Y.J. & Wang, R.Z. & Sumathy, K., 2011. "Concentrated solar energy applications using Fresnel lenses: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2588-2606, August.
    27. Fernández, Eduardo F. & Talavera, D.L. & Almonacid, Florencia M. & Smestad, Greg P., 2016. "Investigating the impact of weather variables on the energy yield and cost of energy of grid-connected solar concentrator systems," Energy, Elsevier, vol. 106(C), pages 790-801.
    28. Talavera, D.L. & Pérez-Higueras, P. & Ruíz-Arias, J.A. & Fernández, E.F., 2015. "Levelised cost of electricity in high concentrated photovoltaic grid connected systems: Spatial analysis of Spain," Applied Energy, Elsevier, vol. 151(C), pages 49-59.
    29. 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.
    30. Shanks, Katie & Senthilarasu, S. & Mallick, Tapas K., 2016. "Optics for concentrating photovoltaics: Trends, limits and opportunities for materials and design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 394-407.
    31. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    32. Fernández, Eduardo F. & Pérez-Higueras, P. & Almonacid, F. & Ruiz-Arias, J.A. & Rodrigo, P. & Fernandez, J.I. & Luque-Heredia, I., 2015. "Model for estimating the energy yield of a high concentrator photovoltaic system," Energy, Elsevier, vol. 87(C), pages 77-85.
    33. 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.
    34. 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.
    35. 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.
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