A Surrogate Modelling Methodology To Predict Energy Use For Multiple Single-Family Century Home Archetypes In Toronto
Bibliographic record
Abstract
<div>This research investigates the application of surrogate modelling to improve the energy performance of single-family homes. EnergyPlus was used to simulate 6000 energy models for four different semi-detached and detached century archetypes in Toronto, ON. Multivariate regression and a novel forward stepwise selection methodology were used to develop the surrogate models for each archetype. These models predicted energy use between 7.02%- 7.54% error. A combined model that contained all four archetypes was developed to determine if a single model can replace multiple models. This model predicted annual energy use with 7.03% error and the number of samples required per archetype was reduced by a factor of 3-4. Elastic net regression was tested and found to be equally as effective as the proposed stepwise selection methodology. The findings of this research support the future application of surrogate modelling as a powerful tool to develop bottom-up archetype models for century homes in Toronto, ON.</div>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".