Investigating the Impact of Space Allocation Strategy on Energy-Comfort Trade-off in Office Buildings
Bibliographic record
Abstract
With the global push to decarbonize the building sector and growing interest in occupant-centric building controls, numerous simulation and field studies have been conducted to explore the trade-off between energy efficiency and occupant comfort. These studies largely disregard individual differences in thermal comfort and assume each zone has a fixed occupancy schedule. In office buildings, there is often some leeway in how occupants are grouped and assigned to different building spaces (e.g., offices and meeting rooms). In this paper we investigate the extent of the impact of the space allocation strategy on the energy-comfort trade-off in office buildings, and whether it depends on specific building characteristics. Our simulation shows that varying the space allocation strategy in a medium office building can lead to over 3.5%/15.1% change in annual/monthly energy consumption, and over 15% change in average thermal comfort when using the personal comfort model. This finding calls for the joint optimization of HVAC operation and space allocation, possibly at different timescales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".