Energy Use Variation Among Tenants Occupying the Same Dwelling: A Study of 600+ Cases in Montreal and Quebec
Bibliographic record
Abstract
Objective - This study investigates the variability in electricity consumption among tenants residing in the same dwelling in Montreal and Quebec City. The primary goals are to assess the extent to which occupant behavior and lifestyle influence electricity usage—particularly for heating—and to quantify the magnitude of variation in energy use between tenants.Methodology - The analysis proceeded in three phases: data extraction and preparation, data processing and calculation of key indicators, and results analysis. Four data sources were utilized:- A survey of residential electricity use- A registry of tenants with move-in and move-out dates- Monthly electricity consumption records- Historical weather dataKey indicators calculated for each dwelling-tenant pair included monthly baseload, weather-adjusted heating energy use, and weather-adjusted total energy use. Outlier data were removed, resulting in a final sample of 652 dwellings. For each dwelling, statistical measures—mean, standard deviation, and coefficient of variation—were computed to evaluate the degree of energy use variation among tenants.Results - The findings reveal substantial variability in energy consumption among tenants occupying the same dwelling. Average variations for total energy use, baseload, and heating were 22%, 33%, and 34%, respectively, with distributions that are both skewed and dispersed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".