The Effect of Zone Level Occupancy Characteristics on Adaptive Controls
Bibliographic record
Abstract
The objective of this paper is to examine the energy and comfort impact of the HVAC equipment granularity in offices through building performance simulation. To this end, the occupancy data gathered from 37 private offices in Ottawa, Canada were analysed. For each occupant, four parameters that play an important role over the HVAC operation were extracted. These parameters are the earliest expected arrival time, the latest expected departure time, the latest expected arrival time, and the longest expected duration of intermediate vacancy. Through random sampling from the 37 occupants, hypothetical zones with varying numbers of occupants were created, and EnergyPlus simulations were conducted. Results indicate that the earliest expected arrival time in one-person zones is on average two hours later than it is in twelve-person zones. Similarly, the latest expected departure time in one-person zones is on average two hours earlier than it is in twelve-person zones. Heating and cooling energy use with adaptive occupancy-based temperature setback scheduling in oneperson zones is estimated to be 20% less than it is in twelve-person zones. Keywords: Occupancy; HVAC; Energy use; Adaptive controls
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".