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A Closed-Loop Measurement Study of Runtime Governance in AI-Driven Smart Building Climate Control
Norkobil Saydirasulov Saydirasulovich1,2, Dilmurod Abdujalilovich Davronbekov3, Makhmudov Makhsum Mubashirovich4
1Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, Republic of Korea.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
Runtime governance mechanisms like admission control significantly reduce physical risk in learned building climate control systems. Prevention strategies are more effective than recovery, especially under changing conditions.
Area of Science:
- Building energy systems
- Control theory
- Artificial intelligence in building management
Background:
- Learned controllers offer potential for optimizing building climate control.
- Runtime governance is crucial for ensuring safety and mitigating physical risks.
- Distribution shift poses challenges for the reliability of learned control systems.
Purpose of the Study:
- To evaluate runtime governance mechanisms for reducing physical risk in learned building climate control.
- To compare the effectiveness of preventive versus recovery-based safety mechanisms.
- To identify conditions under which different governance strategies are most effective.
Main Methods:
- Development of a closed-loop software-in-the-loop testbed.
- Utilizing a physics-based thermal zone model driven by a learned setpoint model.
- Implementing a declarative governance plane with an independent safety oracle and MQTT stack.
Main Results:
- Admission control reduced unsafe physical exposure by 19.4% under distribution shift.
- Checkpoint rollback provided only a marginal additional reduction (0.2%).
- Learned controllers showed a safety-demand trade-off compared to deterministic thermostats.
Conclusions:
- Preventive runtime governance, specifically admission control, is more effective than recovery for thermal systems.
- The effectiveness of governance depends on factors like plant inertia and the accuracy of occupancy context estimation.
- Further research is needed to optimize learned controllers and their governance for building climate applications.
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