IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0326576.html
   My bibliography  Save this article

A novel twin time series network for building energy consumption predicting

Author

Listed:
  • Zhixin Sun
  • Han Cui
  • Xiangxiang Mei
  • Hailei Yuan

Abstract

Energy consumption prediction in buildings is crucial for optimizing energy management. The latest research faces three critical challenges: (1) Insufficient temporal correlation extraction and prediction accuracy, hindering widespread adoption and application; (2) The positive impact of timestamp embedding in time series prediction under multi-mode decomposition; and (3) The issue of adaptive coupling with multi-source data. To overcome these issues, the study proposes Twin Time-Series Networks (T2SNET), which incorporates a time-embedding layer and a Temporal Convolutional Network (TCN) to extract patterns from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), along with an adaptive fusion gate to combine energy consumption and meteorological data. The model was evaluated on datasets from university dormitories, office buildings, and school classrooms, showing significant improvements over the optimal baseline method. For instance, on the university classroom dataset, T2SNET reduced MAE by 4.56%, RMSE by 9.45%, and MAPE by 3.16% compared to the CEEMDAN-RF-LSTM model. These results highlight T2SNET’s effectiveness in predicting building energy consumption, providing a robust solution for energy management systems. The proposed method, along with baseline model code and data, has been updated and is available at https://github.com/HaileiYuan/T2SNET-Pro.git.

Suggested Citation

  • Zhixin Sun & Han Cui & Xiangxiang Mei & Hailei Yuan, 2025. "A novel twin time series network for building energy consumption predicting," PLOS ONE, Public Library of Science, vol. 20(6), pages 1-20, June.
  • Handle: RePEc:plo:pone00:0326576
    DOI: 10.1371/journal.pone.0326576
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0326576
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0326576&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0326576?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
    ---><---

    More about this item

    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:plo:pone00:0326576. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.