Data-driven modelling of pressurized corridor ventilation system performance in a multi-unit residential building
Bibliographic record
Abstract
Pressurized corridor (PC) ventilation systems are a common method used in existing multi-unit residential buildings (MURBs) to deliver make-up air to individual units, and as a means of controlling inter-zonal odour/contaminant transfer. In PC systems, ventilation air is supplied directly to the common corridor and enters the units via intentional undercuts at the unit entry doors. In practice, the amount of ventilation air supplied to each unit is dependent on the air pressure differential between the two zones, which can be affected by occupant behaviours, such as window and unit exhaust fan operation; wind; or large indoor-outdoor temperature differentials. Accurately characterizing the impact of these variables on building pressure differentials is critical to not only identifying conditions when depressurization events may occur (which would result in a lack of ventilation to dwelling unit and the potential for contaminant movement from units to the corridor), but also understanding how operational changes can improve system operation. This paper will describe the development of an XGBoost regression model for predicting inter-zonal pressure differentials in a contemporary MURB with a PC system. The model was trained and validated using measurements collected as part of a 6-month field study in a 17-storey MURB located in Toronto, Canada, including corridor-to-unit and exterior-to-unit differential pressures, window/door operation, corridor supply air flow rates and interior/exterior temperature and relative humidity. Unit exhaust fan operation was inferred from the unit differential pressure data. This paper addresses feature selection, hyperparameter tuning and accuracy assessment, with a specific emphasis on evaluating the potential for the use of the model as a diagnostic tool and testing environment to evaluate ventilation system performance in multi-unit residential buildings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".