IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v112y2013icp610-617.html
   My bibliography  Save this article

Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules

Author

Listed:
  • Piliougine, Michel
  • Elizondo, David
  • Mora-López, Llanos
  • Sidrach-de-Cardona, Mariano

Abstract

In this paper, we propose the use of a methodology to characterise the electrical parameters of several thin-film photovoltaic module technologies. This methodology allows us to use not only solar irradiance and module temperature as classical models do, but also spectral distribution of solar radiation. The methodology is based on the use of neural network models. From all measured I–V curves of a module, a previous selection of them has been used in order to train the neural network model. This selection is performed using a Kohonen self-organising map fed with spectral data. This spectral information has been added as an input to the neural network itself. The results show that the incorporation of spectral measurements to simulate thin-film modules improves significantly both the fitting of the predicted I–V curve to the measured one and the peak power point estimation.

Suggested Citation

  • Piliougine, Michel & Elizondo, David & Mora-López, Llanos & Sidrach-de-Cardona, Mariano, 2013. "Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules," Applied Energy, Elsevier, vol. 112(C), pages 610-617.
    • Handle: RePEc:eee:appene:v:112:y:2013:i:c:p:610-617
    DOI: 10.1016/j.apenergy.2013.05.053
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261913004650
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2013.05.053?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kalogirou, Soteris A., 2004. "Optimization of solar systems using artificial neural-networks and genetic algorithms," Applied Energy, Elsevier, vol. 77(4), pages 383-405, April.
    2. Celik, Ali Naci & Acikgoz, NasIr, 2007. "Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four- and five-parameter models," Applied Energy, Elsevier, vol. 84(1), pages 1-15, January.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    8. Sandrolini, L. & Artioli, M. & Reggiani, U., 2010. "Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis," Applied Energy, Elsevier, vol. 87(2), pages 442-451, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daxini, Rajiv & Sun, Yanyi & Wilson, Robin & Wu, Yupeng, 2022. "Direct spectral distribution characterisation using the Average Photon Energy for improved photovoltaic performance modelling," Renewable Energy, Elsevier, vol. 201(P1), pages 1176-1188.
    2. Daxini, Rajiv & Wilson, Robin & Wu, Yupeng, 2023. "Modelling the spectral influence on photovoltaic device performance using the average photon energy and the depth of a water absorption band for improved forecasting," Energy, Elsevier, vol. 284(C).
    3. Daxini, Rajiv & Wu, Yupeng, 2024. "Review of methods to account for the solar spectral influence on photovoltaic device performance," Energy, Elsevier, vol. 286(C).
    4. 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.
    5. Thomas Huld & Ana M. Gracia Amillo, 2015. "Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and Solar Spectrum," Energies, MDPI, vol. 8(6), pages 1-23, June.
    6. 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.
    7. Benhammane, Mousaab & Notton, Gilles & Pichenot, Grégoire & Voarino, Philippe & Ouvrard, David, 2021. "Overview of electrical power models for concentrated photovoltaic systems and development of a new operational model with easily accessible inputs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. 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.
    9. Chen, Zhicong & Yu, Hui & Luo, Linlu & Wu, Lijun & Zheng, Qiao & Wu, Zhenhui & Cheng, Shuying & Lin, Peijie, 2021. "Rapid and accurate modeling of PV modules based on extreme learning machine and large datasets of I-V curves," Applied Energy, Elsevier, vol. 292(C).
    10. Liukkonen, M. & Hiltunen, T., 2014. "Adaptive monitoring of emissions in energy boilers using self-organizing maps: An application to a biomass-fired CFB (circulating fluidized bed)," Energy, Elsevier, vol. 73(C), pages 443-452.
    11. Torres-Ramírez, M. & Elizondo, D. & García-Domingo, B. & Nofuentes, G. & Talavera, D.L., 2015. "Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology," Energy, Elsevier, vol. 86(C), pages 323-334.
    12. Chen, Ze & Zhang, Xiao-dan & Fang, Jia & Liang, Jun-hui & Liang, Xue-jiao & Sun, Jian & Zhang, De-kun & Wang, Ning & Zhao, Hui-xu & Chen, Xin-liang & Huang, Qian & Wei, Chang-chun & Zhao, Ying, 2014. "Enhancement in electrical performance of thin-film silicon solar cells based on a micro- and nano-textured zinc oxide electrodes," Applied Energy, Elsevier, vol. 135(C), pages 158-164.
    13. Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    14. Torres-Ramírez, M. & Nofuentes, G. & Silva, J.P. & Silvestre, S. & Muñoz, J.V., 2014. "Study on analytical modelling approaches to the performance of thin film PV modules in sunny inland climates," Energy, Elsevier, vol. 73(C), pages 731-740.
    15. Orozco-Gutierrez, M.L. & Ramirez-Scarpetta, J.M. & Spagnuolo, G. & Ramos-Paja, C.A., 2014. "A method for simulating large PV arrays that include reverse biased cells," Applied Energy, Elsevier, vol. 123(C), pages 157-167.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Ma, Tao & Yang, Hongxing & Lu, Lin, 2014. "Solar photovoltaic system modeling and performance prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 304-315.
    3. 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.
    4. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
    5. 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.
    6. 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.
    7. Jordehi, A. Rezaee, 2016. "Parameter estimation of solar photovoltaic (PV) cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 354-371.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Chen, Zhicong & Yu, Hui & Luo, Linlu & Wu, Lijun & Zheng, Qiao & Wu, Zhenhui & Cheng, Shuying & Lin, Peijie, 2021. "Rapid and accurate modeling of PV modules based on extreme learning machine and large datasets of I-V curves," Applied Energy, Elsevier, vol. 292(C).
    15. 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.
    16. Huang, Chao & Bensoussan, Alain & Edesess, Michael & Tsui, Kwok L., 2016. "Improvement in artificial neural network-based estimation of grid connected photovoltaic power output," Renewable Energy, Elsevier, vol. 97(C), pages 838-848.
    17. Jena, Debashisha & Ramana, Vanjari Venkata, 2015. "Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 400-417.
    18. Askarzadeh, Alireza & Rezazadeh, Alireza, 2013. "Artificial bee swarm optimization algorithm for parameters identification of solar cell models," Applied Energy, Elsevier, vol. 102(C), pages 943-949.
    19. Amrouche, Badia & Guessoum, Abderrezak & Belhamel, Maiouf, 2012. "A simple behavioural model for solar module electric characteristics based on the first order system step response for MPPT study and comparison," Applied Energy, Elsevier, vol. 91(1), pages 395-404.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:112:y:2013:i:c:p:610-617. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.