Long-term experimental evaluation and comparison of advanced controls for HVAC systems

Xuezheng Wang and Bing Dong

Applied Energy, 2024, vol. 371, issue C, No S0306261924010894

Abstract: The tremendous energy usage from buildings leads to research studies on their improvement, among which advanced building control plays an important role. In advanced building controls, data-driven predictive control (DDPC), differentiable predictive control (DPC), and reinforcement learning (RL) have shown advantages, but their comparison often lacks in existing studies. The simulation-based prior comparison studies have inconsistent results due to different assumptions and simplifications. Therefore, to comprehensively compare the three advanced strategies for real-time building HVAC controls, we implemented DDPC, specifically, hierarchical DDPC (HDDPC), DPC, and RL in a real building testbed for more than 5 months. The results show that all three advanced controls maintained the indoor environmental quality (IEQ) cost-effectively. Overall, HDDPC outperformed the baseline control with more than 50% energy savings, followed by RL with 48%, and DPC with 30.6%. Most control failures were related to API communication issues. Besides, the information gaps between room and system level controllers and non-optimal control decisions will degrade HDDPC's performance. Such degradation did not happen in DPC and RL, which led to better performance of agent-based control over HDDPC. Moreover, HDDPC needs minutes to make control decisions whereas DPC and RL need milliseconds, indicating higher online computing resources required by HDDPC. For agent training, DPC is faster than RL, as DPC training needs minutes and RL needs hours, but its performance is not as good as RL. This study provides a comprehensive understanding and assessment of the pros and cons of advanced building controls and sheds light on future research on building controls.

Keywords: Physics-informed learning; Reinforcement learning; Model predictive control; Differentiable predictive control; Optimal building control; Long-term experiment (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261924010894
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:371:y:2024:i:c:s0306261924010894

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2024.123706

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().