IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v334y2025ics0360544225034309.html
   My bibliography  Save this article

Experimental study and artificial neural network modeling of a pulsating heat pipe PV/T module using a low-efficiency photovoltaic panel

Author

Listed:
  • Liang, Qing
  • Fang, Chunliu
  • Ma, Xuechao
  • Zhang, Yibo
  • Xue, Xiaojian
  • Yan, Longlong

Abstract

A pulsating heat pipe photovoltaic/thermal (PV/T) module was proposed to enhance the energy utilization of a low-efficiency PV panel. By integrating a pulsating heat pipe with a monocrystalline silicon PV panel containing internal defects, the module enables simultaneous electricity generation and waste heat recovery. Experimental results demonstrate that the daily average overall energy efficiency of the module reaches 44.69–49.07 %. Furthermore, an artificial neural network model for predicting electrical performance and another model for thermal performance were developed. The two models are linked via a bidirectional coupling mechanism. Coupled predictions using these models for a typical day show that the relative error between the predicted and experimental daily overall energy efficiency is 0.92 %.

Suggested Citation

  • Liang, Qing & Fang, Chunliu & Ma, Xuechao & Zhang, Yibo & Xue, Xiaojian & Yan, Longlong, 2025. "Experimental study and artificial neural network modeling of a pulsating heat pipe PV/T module using a low-efficiency photovoltaic panel," Energy, Elsevier, vol. 334(C).
  • Handle: RePEc:eee:energy:v:334:y:2025:i:c:s0360544225034309
    DOI: 10.1016/j.energy.2025.137788
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225034309
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.137788?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

    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:eee:energy:v:334:y:2025:i:c:s0360544225034309. 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/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.