IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v333y2025ics0360544225030476.html

A generic framework and strategies for integrating AI into building automation systems for field-level optimization of HVAC systems

Author

Listed:
  • Xie, Lingyun
  • Shan, Kui
  • Wang, Shengwei

Abstract

With the growing demand for energy-efficient and optimized building operations, AI-based control optimization for HVAC systems has garnered increasing attention. However, most existing approaches remain confined to academic research due to challenges in practical deployment and the operational reliability of AI-driven strategies. This paper, presents a generic framework and associated strategies for integrating AI-driven online control optimization into Building Automation Systems (BAS) by adopting AI-enabling smart control stations at the BAS field level. The proposed framework comprises two core functional modules: (1) an AI operating environment that supports lightweight and, real-time execution of AI models, and (2) a comprehensive suite of AI functional boxes that ensure effective and reliable execution of AI algorithms. The framework's functionalities are validated by integrating two smart control stations with a BAS testbed. Control robustness and energy performance are evaluated through hardware-in-the-loop testing using a simulated dynamic HVAC system. The test results demonstrate that the proposed framework and AI-driven strategies can maintain robust and stable control under various critical conditions, while achieving a 7.66 % reduction in energy consumption.

Suggested Citation

  • Xie, Lingyun & Shan, Kui & Wang, Shengwei, 2025. "A generic framework and strategies for integrating AI into building automation systems for field-level optimization of HVAC systems," Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:energy:v:333:y:2025:i:c:s0360544225030476
    DOI: 10.1016/j.energy.2025.137405
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225030476
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.137405?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.

    References listed on IDEAS

    as
    1. Zhang, Yingnan & Hu, Wei & Tao, Yirui & Zhang, Bin, 2025. "How does smart artificial intelligence influence energy system resilience? Evidence from energy vulnerability assessments in G20 countries," Energy, Elsevier, vol. 314(C).
    2. Lu, Ruyuan & Li, Xin & Chen, Ronghao & Lei, Aimin & Ma, Xiaoming, 2024. "An Alternative Reinforcement Learning (ARL) control strategy for data center air-cooled HVAC systems," Energy, Elsevier, vol. 308(C).
    3. Chen, Zhe & Zhang, Jing & Xiao, Fu & Xu, Kan & Chen, Yongbao, 2025. "Development of a probabilistic cooling load prediction-based robust chiller sequencing strategy and its real-world implementation," Applied Energy, Elsevier, vol. 382(C).
    4. Li, Wenzhuo & Wang, Shengwei, 2020. "A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use," Applied Energy, Elsevier, vol. 275(C).
    5. Dai, Mingkun & Li, Hangxin & Wang, Shengwei, 2024. "Development and implementation of supply-based feedback controls for energy-efficient and grid-interactive cooling management over entire building daily cycle," Energy, Elsevier, vol. 313(C).
    6. Dai, Mingkun & Li, Hangxin & Wang, Shengwei, 2023. "A reinforcement learning-enabled iterative learning control strategy of air-conditioning systems for building energy saving by shortening the morning start period," Applied Energy, Elsevier, vol. 334(C).
    7. Hong, Xiaoxi & Yao, Ye & Wang, Kui & Yang, Jianzhong & Liu, Qimei, 2025. "Energy-saving optimal control of secondary district cooling system based on tribal intelligent evolution optimization algorithm," Energy, Elsevier, vol. 316(C).
    8. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dai, Mingkun & Li, Hangxin & Wang, Shengwei, 2025. "Cooling management and control of buildings for unlocking demand flexibility: A comprehensive review on the objectives, objects and methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
    2. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    3. Wang, Xiangwei & Wang, Peng & Huang, Renke & Zhu, Xiuli & Arroyo, Javier & Li, Ning, 2025. "Safe deep reinforcement learning for building energy management," Applied Energy, Elsevier, vol. 377(PA).
    4. Lin, Xiaoyu & Shan, Kui & Li, Hangxin & Wang, Shengwei, 2026. "Energy demand-based optimal start control for multi-chiller plants empowered by physics-guided AI," Applied Energy, Elsevier, vol. 404(C).
    5. Hong, Xiaoxi & Yao, Ye & Wang, Kui, 2026. "Energy-efficient optimal control of the high-dimensional Air–Water coupled HVAC systems," Energy, Elsevier, vol. 344(C).
    6. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    7. Liu, Xiaoqi & Lee, Seungjae & Bilionis, Ilias & Karava, Panagiota & Joe, Jaewan & Sadeghi, Seyed Amir, 2021. "A user-interactive system for smart thermal environment control in office buildings," Applied Energy, Elsevier, vol. 298(C).
    8. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    9. Wang, Yi & Qiu, Dawei & Sun, Mingyang & Strbac, Goran & Gao, Zhiwei, 2023. "Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach," Applied Energy, Elsevier, vol. 335(C).
    10. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    11. Hu, Guoqing & You, Fengqi, 2024. "AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory," Applied Energy, Elsevier, vol. 356(C).
    12. Amir Faraji & Maria Rashidi & Fatemeh Rezaei & Payam Rahnamayiezekavat, 2023. "A Meta-Synthesis Review of Occupant Comfort Assessment in Buildings (2002–2022)," Sustainability, MDPI, vol. 15(5), pages 1-36, February.
    13. Giacomo Segala & Roberto Doriguzzi-Corin & Claudio Peroni & Matteo Gerola & Domenico Siracusa, 2023. "EECO: An AI-Based Algorithm for Energy-Efficient Comfort Optimisation," Energies, MDPI, vol. 16(21), pages 1-28, October.
    14. Zhang, Yingnan & Wu, Guanqi & Zhang, Bin, 2025. "Costs and CO2 emissions of technological transformation in China's power industry: The impact of market regulation and assistive technologies," Structural Change and Economic Dynamics, Elsevier, vol. 73(C), pages 211-222.
    15. Jie Yan & Hailing Wang, 2025. "Does the Development of Digital Finance Enhance Urban Energy Resilience? Evidence from Machine Learning," Sustainability, MDPI, vol. 17(14), pages 1-25, July.
    16. Zou, Wenke & Li, Hangxin & Gao, Dian-ce & Wang, Shengwei, 2025. "A physics-guided self-adaptive chiller sequencing controller of enhanced robustness and energy efficiency accommodating measurement uncertainties," Applied Energy, Elsevier, vol. 389(C).
    17. Qing Yin & Chunmiao Han & Ailin Li & Xiao Liu & Ying Liu, 2024. "A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks," Sustainability, MDPI, vol. 16(17), pages 1-30, September.
    18. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2021. "Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control," Applied Energy, Elsevier, vol. 288(C).
    19. Rana Loubani & Didier Defer & Ola Alhaj-Hasan & Julien Chamoin, 2025. "Optimization of Hydronic Heating System in a Commercial Building: Application of Predictive Control with Limited Data," Energies, MDPI, vol. 18(9), pages 1-25, April.
    20. Xue, Lin & Wang, Jianxue & Li, Haotian & Yong, Weizhen & Zhang, Yao, 2025. "Online energy conservation scheduling for geo-distributed data centers with hybrid data-driven and knowledge-driven approach," Energy, Elsevier, vol. 322(C).

    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:333:y:2025:i:c:s0360544225030476. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.