IDEAS home Printed from https://ideas.repec.org/a/oup/ijlctc/v20y2025ip1136-1142..html

A deep learning approach for energy management systems in smart buildings towards a low-carbon economy

Author

Listed:
  • Dongfei Gao

Abstract

Addressing the issue of cold load prediction in building energy systems, a multi-modal fusion deep learning approach is proposed. This method constructs input feature sets of three different modalities: sequence-like, image-like, and video-like, and employs bidirectional gated recurrent units, spatiotemporal neural networks, and 3D convolutional neural networks. Additionally, this paper introduces a multi-modal late fusion strategy based on stacking ensemble learning. Experimental results demonstrate that this method performs exceptionally well in cold load prediction tasks, achieving an MAPE of 5.45%, and R2 of 95.25, which is crucial for the practical implementation of low - carbon building energy management.

Suggested Citation

  • Dongfei Gao, 2025. "A deep learning approach for energy management systems in smart buildings towards a low-carbon economy," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1136-1142.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1136-1142.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf063
    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:1136-1142.. 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.