Exploring the adequacy of mechanical ventilation for acceptable indoor air quality in office buildings
Bibliographic record
Abstract
This paper explores how effectively building variable air volume air handling unit (VAV AHU) configurations supply required outdoor air to building spaces using data analysis and building performance simulation. One year’s worth of zone-level occupancy data from a floor of an institutional office building was estimated using Wi-Fi device count and concurrent motion detector and CO2 sensor data. Zone-level ventilation rates were compared to these data to calculate per person ventilation rates. The results indicated that some spaces experienced under-ventilation for up to 34% of occupied hours. For the simulation-based investigation, a 27-zone energy model was used. Occupancy data from the building were used in the simulation. Four operation modes were simulated: default operation, occupancy-based demand-controlled ventilation (DCV), occupancy-based VAV control, and a combination of the latter strategies. The fewest instances of under-ventilation occurred with occupancy-based VAV control. The under-ventilation instances were a result of inefficient distribution of outdoor air across building zones. The supply of standard (10 L/s per person) ventilation with occupancy-based VAV control, instead of a default constant minimum ventilation rate, reduced the number of under-ventilation instances by ∼80% while reducing the energy use by ∼30%.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".