Alternative Approaches for Assessing the Energy Performance of Houses in Real Estate Transactions
Bibliographic record
Abstract
Currently, there is no mandatory requirement to disclose energy performance of houses that are offered for resale in Real Estate Transactions in Southern Ontario. Homebuyers, therefore, do not have any idea of the energy performance of the houses they are aiming to buy. This Major Research Project (MRP) proposed two methods to homebuyers as alternatives to a full home energy audit that provide a good measure of the energy performance of houses on sale. These approaches are cheaper, simpler, and provide the energy rating of houses more quickly. In the first method, the house's utility billing data were used. A linear regression model was employed to split the bills into energy consumptions by end-uses, which were then compared to an appropriate EnerGuide Rating benchmark for determining the energy rating of the house. The second approach was based on the HOT 2000 energy simulation tool for construction of the energy model of the house. The simulated energy consumptions by end-uses were then compared to the EnerGuide Rating benchmark to determine the house's energy rating. These approaches were implemented using two case studies located in the Peel Region in Southern Ontario with satisfactory results.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".