Using Calibrated Simulation to Quantify the Energy Savings From Residential Passive Solar Design in Canada
Bibliographic record
Abstract
Energy savings from passive solar design applied to a typical Canadian house were quantified using calibrated whole building energy simulation. A detailed energy simulation model was created for a research house which represents a typical Canadian tract house with basic passive solar measures. The model was calibrated to measured furnace gas consumption data. Eight design scenarios were evaluated for eight climate locations. Design parameters included increased thermal mass, increased south window area, and high performance windows. In addition, an advanced house scenario was evaluated which featured optimized geometry, a further increase in south window area, high thermal mass, advanced glazing, and no north facing windows. For the typical house predicted solar heating fractions ranged from 20% to 34% with basic passive solar measures, and 35% to 52% for more aggressive passive solar measures. For the advanced house predicted solar fractions ranged from 40% to 69%.
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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 | 0.000 |
| 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) | 0.000 | 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".