Assessment and Mitigation of Overheating Risks in Archetype and Existing Canadian Buildings under Recent and Projected Future Climates
Bibliographic record
Abstract
This research aims to develop a framework to assess and mitigate the overheating risks under projected future climates for both archetype and existing buildings. More specific objectives are to 1) determine the contribution and correlation of individual building envelope parameters to the change in indoor temperature in conjunction with ventilation, therefore, to determine whether high-energy-efficient buildings required by Canadian building codes to reduce heating consumption in new buildings are at lower or greater overheating risk compared to old buildings; 2) develop an automated calibration procedure to calibrate a building simulation model based on the indoor hourly temperature to achieve high accuracy to be used overheating studies in existing buildings; 3) assess overheating risks under current and future extreme years and recommend effective mitigation measures; and 4) provide an optimal design for retrofitting existing buildings to achieve lowest heating energy demand and highest thermal and visual comfort in new building design. To achieve these objectives, a robust sensitivity-analysis (SA) and calibration method, a systematic framework for evaluating overheating and passive mitigation measures, and an optimization methodology are developed and applied to an archetype detached-house and existing-school-buildings. \nThe results showed that the archetype and existing Canadian buildings have experienced overheating under current climates and the overheating risks will increase dramatically under future climates. Due to the positive contribution of lower U-values of windows, walls, and roofs and SHGC, high-energy-efficient houses have a lower overheating risk than old buildings if adequate ventilation (>2.2-ACH) is provided. Natural ventilation in the high-energy-efficient house is sufficient to reduce the overheating risk under the recent climate but will require adding interior and exterior shading under future climates. For existing-school buildings, the calibrated model achieved high accuracy. The results also showed that the use of exterior blind roll or a combination of night cooling and other mitigation measures that reduce solar heat gain is required under the recent climate and adding a cool roof will be required in future extreme years. For optimization design, the applied optimization methodology can generate several optimal building design solutions based on Window-Wall-Ratio.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".