Energy Benchmarking and Energy Saving Assessment in High-Rise Multi-Unit Residential Buildings
Bibliographic record
Abstract
The purpose of energy benchmarking is to promote efficient use of energy. Knowing that the energy used by a building is excessive is the first step in making positive changes. Based on an energy benchmark, one can estimate the potential in energy and cost savings when pursuing better performance. This thesis developed weather normalized energy benchmarking of 45 gas-heated high-rise multi-unit residential buildings (MURBs) in Toronto. The weather normalized annual energy consumption (NAC) was calculated by the PRInceton Scorekeeping Method (PRISM). The NACs are in the range from 242 to 453 kWh/m The NACs, calculated by the simple ratio weather normalization (SRWN) method and ENERGY STAR® Portfolio Management (PM) method were comparable to PRISM results. However, the SRWN method tends to overestimate the energy saving by 23% while PM underestimates it by 21%.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".