IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v215y2023ics0960148123008017.html
   My bibliography  Save this article

Using artificial intelligence for global solar radiation modeling from meteorological variables

Author

Listed:
  • Zaim, Salma
  • El Ibrahimi, Mohamed
  • Arbaoui, Asmae
  • Samaouali, Abderrahim
  • Tlemcani, Mouhaydine
  • Barhdadi, Abdelfettah

Abstract

Long-term quantification of solar energy variables at ground level is not easily achievable in many locations. In order to overcome this limitation, use of artificial intelligence such as the application of machine learning methods is commonly used for solar irradiance prediction.

Suggested Citation

  • Zaim, Salma & El Ibrahimi, Mohamed & Arbaoui, Asmae & Samaouali, Abderrahim & Tlemcani, Mouhaydine & Barhdadi, Abdelfettah, 2023. "Using artificial intelligence for global solar radiation modeling from meteorological variables," Renewable Energy, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008017
    DOI: 10.1016/j.renene.2023.118904
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123008017
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.118904?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., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    Full references (including those not matched with items on IDEAS)

    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. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    2. Hemmatabady, Hoofar & Welsch, Bastian & Formhals, Julian & Sass, Ingo, 2022. "AI-based enviro-economic optimization of solar-coupled and standalone geothermal systems for heating and cooling," Applied Energy, Elsevier, vol. 311(C).
    3. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.
    4. Rosiek, S. & Batlles, F.J., 2010. "Modelling a solar-assisted air-conditioning system installed in CIESOL building using an artificial neural network," Renewable Energy, Elsevier, vol. 35(12), pages 2894-2901.
    5. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
    6. Jani, D.B. & Mishra, Manish & Sahoo, P.K., 2017. "Application of artificial neural network for predicting performance of solid desiccant cooling systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 352-366.
    7. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    8. Philippopoulos, Kostas & Deligiorgi, Despina, 2012. "Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography," Renewable Energy, Elsevier, vol. 38(1), pages 75-82.
    9. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    10. Samet, Haidar & Hashemi, Farid & Ghanbari, Teymoor, 2015. "Minimum non detection zone for islanding detection using an optimal Artificial Neural Network algorithm based on PSO," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1-18.
    11. Μichalena, Evanthie & Hills, Jeremy M., 2012. "Renewable energy issues and implementation of European energy policy: The missing generation?," Energy Policy, Elsevier, vol. 45(C), pages 201-216.
    12. 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.
    13. 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.
    14. Muhsen, Dhiaa Halboot & Khatib, Tamer & Nagi, Farrukh, 2017. "A review of photovoltaic water pumping system designing methods, control strategies and field performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 70-86.
    15. Kuk Yeol Bae & Han Seung Jang & Bang Chul Jung & Dan Keun Sung, 2019. "Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.
    16. Li, Danny H.W. & Lou, Siwei, 2018. "Review of solar irradiance and daylight illuminance modeling and sky classification," Renewable Energy, Elsevier, vol. 126(C), pages 445-453.
    17. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    18. Haddad, S. & Benghanem, M. & Mellit, A. & Daffallah, K.O., 2015. "ANNs-based modeling and prediction of hourly flow rate of a photovoltaic water pumping system: Experimental validation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 635-643.
    19. Mojtaba Alborzi & Faramarz Sarhaddi & Fatemeh Sobhnamayan, 2019. "Optimization of the thermal lag Stirling engine performance," Energy & Environment, , vol. 30(1), pages 156-175, February.
    20. Islam, Md. Parvez & Morimoto, Tetsuo, 2018. "Advances in low to medium temperature non-concentrating solar thermal technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2066-2093.

    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:215:y:2023:i:c:s0960148123008017. 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.