Selection of Energy Upgrades for Canadian Single-Detached Residential Households Based on Occupancy Profile
Bibliographic record
Abstract
Abstract The use of energy efficient building systems can play a key role in reducing energy consumption and the adverse impacts of greenhouse gas (GHG) emission. The occupancy profile of residential dwellings has a notable influence on the effectiveness of selecting appropriate energy upgrade retrofits. Building simulation models can be integrated to determine the impact of independent occupancy profile in realizing a building’s carbon mitigation target. In this paper, the most desirable energy upgrade retrofits are suggested for three different occupancy profiles by considering important economic parameters, such as the initial investment, payback period and environmental parameter such as GHG emissions. The three occupancy profiles considered were a single adult, couple without children and couple with children. For this purpose, a calibrated energy model was developed for a single-detached family household in British Columbia, Canada, which was equipped with power sensors for monitoring the real time energy data. From the calibrated energy model, three different energy upgrade retrofits (solar, window, and wall/roof insulation) were modelled for the occupancy profiles chosen and the most suitable energy upgrades were suggested. The results show that solar panels contributed the most in energy cost reduction and upgraded windows had the least GHG emission. With suitable financial initiative, the combination of all the three energy upgrades can be the best option in terms of environment and economy.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".