Energy Audits and Energy Modeling as a Tool towards Reducing Energy Consumption in Buildings: The Cases of Two Multi-Unit Residential Buildings (MURBs) in Toronto
Bibliographic record
Abstract
This research is based on an energy audit of two multi-unit residential buildings (MURBs) located in Toronto, Canada. Energy consumption (gas and electricity) data were extracted from the energy bills of the two buildings for a consecutive period of three years. The data were then normalized to account for variations in weather conditions. Conclusions were drawn from correlation analyses between kWh, cooling degree days (CDDs), and heating degree days (HDDs), which were then compared to the energy consumption benchmarks of MURBs within the GTA. An energy simulation using e-Quest v.3.64 was performed, utilizing the advantages of the e-Quest building modeling tool to create a virtual 3D model of the audited buildings. A baseline model was constructed to reflect the actual buildings and was used to simulate the outcomes and calculate the projected energy savings from window replacements with a higher energy efficiency than the existing ones. The simulation results revealed that triple low-E glazing outperformed single- and double-glass windows, achieving reductions of 38% and 34% in gas consumption, respectively. The building envelope simulations showed that enhancing insulation reduced gas consumption by 4%, while an insulation upgrade demonstrated no discernible savings. Reducing the window area by 20% (north/south sides) led to a 6% decrease in gas consumption, while a 30% reduction resulted in approximately 9% of energy savings.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".