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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".