IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v326y2025ics0360544225014641.html
   My bibliography  Save this article

Self-triggered model predictive control for the thermal comfort and energy saving of office buildings

Author

Listed:
  • Li, Yanxin
  • He, Ning
  • He, Lile
  • Li, Ruoxia
  • Gao, Feng
  • Cheng, Fuan

Abstract

In this article, we aim to develop an intelligent and optimized control system for the indoor thermal environment of office buildings to guarantee thermal comfort for indoor staff while reducing energy consumption. Model predictive control (MPC) has confirmed its ability to improve thermal comfort while reducing energy consumption. However, traditional MPC methods require high computing power for the controller to solve each control input, resulting in excessive computing resource waste. To this end, we propose a self-triggered mechanism (STM) that optimizes the solution only when the triggering rule is satisfied. The proposed STM is based on the P-norm to obtain the next solution moment in advance as a way to reduce computational burden. In addition, the indoor and outdoor heat gains are also solved by expressing the thermophysical equations and further combined with the resistance–capacitance (RC) model to make it more accurate. Based on the established model, a self-triggered model predictive control (ST-MPC) system is developed, and the feasibility and convergence of the system are analyzed through rigorous mathematical proof. The results show that the proposed ST-MPC can reduce the computational burden by 95% compared to the traditional MPC and improve thermal comfort by 16.1%.

Suggested Citation

  • Li, Yanxin & He, Ning & He, Lile & Li, Ruoxia & Gao, Feng & Cheng, Fuan, 2025. "Self-triggered model predictive control for the thermal comfort and energy saving of office buildings," Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:energy:v:326:y:2025:i:c:s0360544225014641
    DOI: 10.1016/j.energy.2025.135822
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225014641
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135822?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:energy:v:326:y:2025:i:c:s0360544225014641. 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.