Strategies for reducing energy & carbon intensity of a Vancouver townhouse complex
Bibliographic record
Abstract
A townhouse building in Vancouver, Canada was studied to see what energy and emissions savings were possible through implementation of practical energy improvement measures. It was found that a 60 – 65% reduction in energy consumption was possible through measures that have no impact on household comfort or function. These measures yield a levelized savings of roughly $650 per year and a simple payback of five years. These measures have the potential to make these townhouses 5 times more efficient than the average Canadian residential building and 4 times more efficient than a typical British Columbia town-home. The suggested measures will introduce a mix of improved efficiency, behaviour change and new energy supply. This comprehensive approach is required to achieve maximum potential savings. All suggested improvement measures are recommended to be implemented at the unit level as opposed to the building level. This is because expectations vary significantly amongst the home owners, and energy use in this building is already separated by unit. Energy and emissions savings may be eroded by the behaviour of the residents by up to 26%. Clear understanding of the available savings and disciplined behaviour will be key to maximizing the potential of the suggested measures. These results are most relevant to other town-homes in British Columbia, particularly in the Metro Vancouver area and on Vancouver Island. In conjunction with University of British Columbia. Clean Energy Research Center. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".