IDEAS home Printed from https://ideas.repec.org/a/oup/ijlctc/v20y2025ip1223-1241..html

Optimized prediction of electrical and thermal performance in solar PV/T systems using cloud-based data acquisition and advanced ANN models

Author

Listed:
  • Yibing Zhang
  • Congcong Wang
  • Junhui Sun
  • Hongbing Chen
  • Pingjun Nie

Abstract

This study develops a cloud-monitored platform for predicting electrical and thermal performance in photovoltaic/thermal systems using back propagation (BP) and radial basis function (RBF) models. Genetic algorithm (GA) and particle swarm optimization (PSO) were applied to optimize the BP and RBF models. Results show that all four models exhibit high prediction accuracy, with the GA-BP model achieving the best performance. The average prediction errors for electrical power, electrical efficiency, thermal storage tank temperature, and thermal efficiency were 0.74%, 0.97%, 0.47%, and 0.32%, respectively, significantly improving precision over conventional models.

Suggested Citation

  • Yibing Zhang & Congcong Wang & Junhui Sun & Hongbing Chen & Pingjun Nie, 2025. "Optimized prediction of electrical and thermal performance in solar PV/T systems using cloud-based data acquisition and advanced ANN models," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1223-1241.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1223-1241.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf071
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:oup:ijlctc:v:20:y:2025:i::p:1223-1241.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/ijlct .

    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.