IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v388y2025ics0306261925003617.html
   My bibliography  Save this article

Deeply flexible commercial building HVAC system control: A physics-aware deep learning-embedded MPC approach

Author

Listed:
  • Tang, Lingfeng
  • Xie, Haipeng
  • Wang, Yongguan
  • Xu, Zhanbo

Abstract

Heating, ventilation, and air conditioning (HVAC) systems within commercial buildings can serve as flexible resources to promote the integration of renewable energy into power systems. However, the complicated operational characteristic of chiller and multi-zone thermal dynamics in the coupled water and air loops lead to a high model complexity to HVAC system control, limiting its operational flexibility exploitation. To tackle this problem, this paper proposes a physics-aware deep learning-embedded model predictive control (MPC) approach to enable deeply flexible commercial building HVAC system control for demand response. Firstly, the chiller's operational characteristic is captured via a deep learning model with high approximation capability, integrated with a physics-constrained block to enforce operational constraints. The multi-zone thermal dynamics are modeled using a graph convolutional network informed by the prior building structure. Secondly, the proposed deep learning models are equivalently reformulated into mixed integer linear constraints and seamlessly embedded into the MPC framework. To enhance the solution efficiency, the bound forward propagation algorithm and network pruning techniques are both developed for the deep learning-embedded MPC approach. Finally, a high-fidelity commercial building HVAC system consisting of coupled water and air loops, as well as outdoor weather conditions, indoor occupancy behaviors, etc. is built on the EnergyPlus simulation program. Comprehensive experimental results have validated the effectiveness of the proposed method in improving flexibility utilization.

Suggested Citation

  • Tang, Lingfeng & Xie, Haipeng & Wang, Yongguan & Xu, Zhanbo, 2025. "Deeply flexible commercial building HVAC system control: A physics-aware deep learning-embedded MPC approach," Applied Energy, Elsevier, vol. 388(C).
  • Handle: RePEc:eee:appene:v:388:y:2025:i:c:s0306261925003617
    DOI: 10.1016/j.apenergy.2025.125631
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925003617
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2025.125631?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:appene:v:388:y:2025:i:c:s0306261925003617. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.