IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v133y2019icp828-839.html
   My bibliography  Save this article

A data mining system for predicting solar global spectral irradiance. Performance assessment in the spectral response ranges of thin-film photovoltaic modules

Author

Listed:
  • del Campo-Ávila, J.
  • Piliougine, M.
  • Morales-Bueno, R.
  • Mora-López, L.

Abstract

Knowing the spectral distribution of solar radiation is required to estimate the performance of photovoltaic modules, especially for thin-film modules. This is not a trivial problem due to the large number of environmental factors that affect this distribution as solar radiation passes through the atmosphere. The use of techniques of artificial intelligence and data mining can help in the development of models to address this problem. A system based on these techniques is proposed to predict the solar global spectral irradiance requiring only a few meteorological variables as inputs. The evaluation of the proposed system has been carried out for different wavelengths taking into account the spectral response of different technologies of thin-film photovoltaic modules. The errors in predicting solar global spectral irradiance for wavelengths that range between 350 and 900 nm and air mass lower than 2.1 are smaller than 7% on clear-sky days and than 16% for cloudy days, which is a significant improvement on other proposed models. Moreover, an open access implementation of the developed system is available at the URI: http://fvred1.ctima.uma.es. It could be useful for engineers and companies in the fields of the environment and renewable energies.

Suggested Citation

  • del Campo-Ávila, J. & Piliougine, M. & Morales-Bueno, R. & Mora-López, L., 2019. "A data mining system for predicting solar global spectral irradiance. Performance assessment in the spectral response ranges of thin-film photovoltaic modules," Renewable Energy, Elsevier, vol. 133(C), pages 828-839.
    • Handle: RePEc:eee:renene:v:133:y:2019:i:c:p:828-839
    DOI: 10.1016/j.renene.2018.10.083
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118312758
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.10.083?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. Kaskaoutis, D.G. & Kambezidis, H.D., 2008. "The role of aerosol models of the SMARTS code in predicting the spectral direct-beam irradiance in an urban area," Renewable Energy, Elsevier, vol. 33(7), pages 1532-1543.
    2. 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.
    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. Ding, Kun & Chen, Xiang & Jiang, Meng & Yang, Hang & Chen, Xihui & Zhang, Jingwei & Gao, Ruiguang & Cui, Liu, 2024. "Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion," Applied Energy, Elsevier, vol. 353(PB).

    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. Nonnenmacher, Lukas & Kaur, Amanpreet & Coimbra, Carlos F.M., 2016. "Day-ahead resource forecasting for concentrated solar power integration," Renewable Energy, Elsevier, vol. 86(C), pages 866-876.
    2. Benkaciali, Saïd & Haddadi, Mourad & Khellaf, Abdellah, 2018. "Evaluation of direct solar irradiance from 18 broadband parametric models: Case of Algeria," Renewable Energy, Elsevier, vol. 125(C), pages 694-711.
    3. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    4. Koussa, Mustapha & Saheb-Koussa, Djohra & Hadji, Seddik, 2017. "Experimental investigation of simple solar radiation spectral model performances under a Mediterranean Algerian's climate," Energy, Elsevier, vol. 120(C), pages 751-773.
    5. Heras, Carlos & Salinas, Iñigo & Andres, Salvador & Sevilla, Marina & Villafranca, Asier & Martinez, David & Sanchez, Marcelino, 2024. "High-resolution spectral atmospheric attenuation measurement for solar power plants," Renewable Energy, Elsevier, vol. 225(C).
    6. García, R. & Torres-Ramírez, M. & Muñoz-Cerón, E. & de la Casa, J. & Aguilera, J., 2017. "Spectral characterization of the solar resource of a sunny inland site for flat plate and concentrating PV systems," Renewable Energy, Elsevier, vol. 101(C), pages 1169-1179.
    7. Psiloglou, B.E. & Kambezidis, H.D. & Kaskaoutis, D.G. & Karagiannis, D. & Polo, J.M., 2020. "Comparison between MRM simulations, CAMS and PVGIS databases with measured solar radiation components at the Methoni station, Greece," Renewable Energy, Elsevier, vol. 146(C), pages 1372-1391.
    8. Del Hoyo, Mirko & Rondanelli, Roberto & Escobar, Rodrigo, 2020. "Significant decrease of photovoltaic power production by aerosols. The case of Santiago de Chile," Renewable Energy, Elsevier, vol. 148(C), pages 1137-1149.
    9. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2016. "Recent improvements of the Meteorological Radiation Model for solar irradiance estimates under all-sky conditions," Renewable Energy, Elsevier, vol. 93(C), pages 142-158.

    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:renene:v:133:y:2019:i:c:p:828-839. 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.journals.elsevier.com/renewable-energy .

    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.