IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v217y2023ics0960148123011394.html
   My bibliography  Save this article

Development of a novel solar PV module model for reliable power prediction under real outdoor conditions

Author

Listed:
  • Kumar, Manish
  • Malik, Prashant
  • Chandel, Rahul
  • Chandel, Shyam Singh

Abstract

Accurately predicting and validating the power output of commercial solar PV power plants, remains an important research topic despite numerous studies already conducted. The precision and reliability of power prediction depends on the accuracy of the solar cell parameter values used in the model. A novel analytical technique has been developed in this study for PV power prediction, which employs one and two diode models with 3, 5, and 7 parameters. This new model only requires the manufacturer sheet data and has been validated through indoor and outdoor experiments. The performance of an experimental PV system is evaluated using the proposed solar cell models under varying irradiance and temperature levels. Additionally, the predicted output solar power was experimentally validated under real outdoor conditions in India with higher accuracy. The 7-parameter solar cell model is found to be the most accurate with the least RMSE of 0.02, followed by the 5 and 3-parameter models with RMSEs of 0.04 and 0.07, respectively. Compared to previous methods, the present new model predicts PV power with higher accuracy and lower percentage error. Finally, the study also identifies follow-up photovoltaic research areas.

Suggested Citation

  • Kumar, Manish & Malik, Prashant & Chandel, Rahul & Chandel, Shyam Singh, 2023. "Development of a novel solar PV module model for reliable power prediction under real outdoor conditions," Renewable Energy, Elsevier, vol. 217(C).
  • Handle: RePEc:eee:renene:v:217:y:2023:i:c:s0960148123011394
    DOI: 10.1016/j.renene.2023.119224
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123011394
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2023.119224?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.

    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:217:y:2023:i:c:s0960148123011394. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.