On the Joint Control of Multiple Building Systems with Reinforcement Learning
Bibliographic record
Abstract
Commercial buildings are comprised of multiple mechanical and electrical systems that work in tandem to provide a healthy, safe, and comfortable environment for occupants. These systems have complex interactions with each other, and consume a large amount of energy. In this paper, we apply three model-free deep reinforcement learning algorithms to jointly control HVAC and blind systems in a multi-zone test building, in scenarios with and without automatic dimming of the lights in response to daylight levels. The control agents are trained through interactions with a building simulator that generates traces for the movement of occupants. We investigate the three-way trade-off between energy use, thermal comfort, and visual comfort, and discuss how the joint control of the building systems could provide a better trade-off compared to when they are controlled separately. We compare the performance of the proposed control algorithms assuming the availability of occupancy data with two spatial resolutions, and confirm through experiments that a better trade-off can be achieved should zone-level occupancy information become available. Incorporating zone-level occupancy information, we show that 11.0% and 31.8% more energy can be saved respectively in heating and cooling seasons over existing rule-based baselines that control the same building systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".