Application of Deep Energy Retrofits to Allow Existing Housing in Toronto to Meet Passive House Certification
Bibliographic record
Abstract
This study researches the development of a multiobjective optimization environment to continue the investigation of the potential for housing in Toronto to meet Passive House certification. BEopt (Building Energy Optimizer) was used to develop various deep energy retrofits for Century detached, Century-semi, and Wartime archetype homes in Toronto, ON. The optimization environment used various design retrofits to develop configurations of variables which minimize energy use and life cycle cost. The developed solutions were input into WUFI Passive to determine which combination of variables can achieve Passive House certification for the lowest life cycle cost. The optimization results demonstrated that for a life cycle cost of $65,053–$92,950 depending on geometry and housing type, an archetype home in Toronto can reduce energy use by 69%–72% and can meet standards for Passive House. Although the developed numbers include average pricing data, assumptions, and generalizations, the findings of this research demonstrate the applicability of high-performance building standards in retrofit strategies. Through the adoption of energy efficient deep energy retrofits in existing homes, sustainability in the built environment can be achieved.
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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 | 0.000 |
| 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 | 0.000 | 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".