A method to develop residential archetypes by associating thermophysical building attributes with utility meter data
Bibliographic record
Abstract
This study introduces a new method to develop residential energy archetypes by integrating electricity and natural gas consumption data with thermophysical building characteristics for a medium-sized Canadian city. Eight energy consumption clusters were identified using clustering techniques on high-frequency energy data from over 23,000 single-family homes, capturing diverse usage patterns and seasonal behaviours. The clusters were further subdivided based on physical attributes, such as insulation levels, air leakage, and HVAC efficiencies, to reveal intra-cluster variations. Results demonstrate that houses with similar energy consumption profiles exhibit significantly different physical characteristics, and conversely, houses with comparable physical attributes can display vastly different energy behaviours. These findings challenge the traditional bottom-up modeling assumptions used when segmenting building stocks into archetypes and defining those archetypes based on predetermined templates. The methodology demonstrates the potential for targeted retrofit strategies that take into account both physical properties, behavioural dynamics and socioeconomic status of residents.
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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".