A multi-objective optimization analysis of passive energy conservation measures in a Toronto house
Bibliographic record
Abstract
This study presents a multi-objective optimization environment in which passive energy conservations measures of a high performance house in Toronto are evaluated. The optimization environment was created using the jEPlus software suite where the case study house acted as the reference building. The study house simulation model was calibrated using a data-driven procedure, and acceptable CV(RSME) and NMBE tolerances were reached in accordance with ASHRAE calibration requirements. The optimization varied passive energy efficiency parameters in search of configurations yielding optimal building performance and life cycle cost. The optimization results showed that energy savings of 33% relative to building code minimum were justified at the point of minimal life cycle cost via passive energy saving measures alone before considering active systems. These results suggest that improved thermal envelopes are economically advantageous with good building practice. However, they suggest that the current Passive House standard does not coincide with the economic minimum for the local economic and environmental climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".