Calibration and Evaluation of Building Energy Models to Assess and Mitigate Canadian Building Overheating Risks in Current and Future Climates
Bibliographic record
Abstract
Climate change continues to impact weather conditions globally, requiring cities to adapt to new environments. In Canada, indoor summer overheating in buildings is problematic due to their primary design for cold winters, making them susceptible to extreme heatwave events. This research focuses on calibrating building models and employing model calibration methodologies to evaluate and address overheating risks in various building types across Canada using current and future weather data. The study's objectives are to accurately assess summertime overheating risks in selected buildings in Montreal, Quebec, and evaluate effective mitigation strategies. Bayesian calibration and multi-objective genetic algorithms are used as calibration methods, with the latter showing superiority in producing highly accurate calibrated models based on five performance criteria. By incorporating field measurements and novel methodologies, the research ensures precise assessment and mitigation of summertime overheating risks. After calibrating several buildings, including schools, a hospital, and a residential building, which demonstrates the reliability and repeatability of the calibration process, the assessment of overheating is conducted on calibrated models to determine the number of overheating hours during the summer. In conclusion, this thesis demonstrates the repeatability of the calibration methods on a variety of existing Canadian buildings and the effective use of passive cooling techniques, such as external shading, night cooling, and high albedo surfaces, that are implemented in the building models, to mitigate overheating based on current and future weather data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".