Post-Occupancy Evaluation of a Passive Multi-Unit Residential Building and a Single-Family Passive House
Bibliographic record
Abstract
Passive buildings are increasing in popularity in Canada. This paper examines two passive buildings initially constructed in the past decade: the Peterborough passive multi-unit residential building (MURB) and the Wolfe Island single-family dwelling. A post-occupancy evaluation was performed on the buildings. The buildings were modelled in HOT2000 and the Passive House Planning Package (PHPP) to ensure the validity of the results. The energy bills were collected from the building owners to acquire the real-time consumption of the buildings. The models have shown a good agreement with the collected data. Furthermore, data loggers were installed in both buildings for indoor temperature monitoring to ensure that they adhere to the passive house explicit criteria. Internal gains, shading, and orientation were analyzed to assess their effect on heating and cooling loads. Peterborough MURB has shown more energy-saving potential compared to the Wolfe Island passive house. Heating load reduction has been compared, more than five times, to the cooling load reduction potential. The reduction in GHG emissions can be up to 39% when passive house parameters are applied to the Wolfe Island house. This paper has shown the potential of the passive house in relation to sustainable buildings in Northern climates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".