Optimal Net-Zero Cost Deep Energy Retrofit of Low-Rise Social Housing Townhouses
Bibliographic record
Abstract
Whole building optimization retrofits have been performed for two townhouses in four locations with different climates to find both energy efficiency and cost-effective retrofit solutions across a thirty-year time span analysis. The objective is to find deep energy retrofit packages that can be used for large scale social housing retrofit. The multi-objective optimizations aim to achieve the least annualized related costs, lower initial and operational energy related costs and substantial carbon savings by analyzing one natural gas heated option and four electric heated options (baseboard heating system, central air-source heat pump, ductless mini-split heat pump and ground-source heat pump). Results reveal that prescriptive deep energy retrofit solutions achieved between 78% to 100% site energy reductions through building enclosures improvement, upgrades of HVAC and water heating systems, upgrades of appliances and lighting, and the addition of onsite renewable energy generation. Results also indicate that ductless mini-split heat pump (MSHP) optimized model has lower long-term costs and a shorter modified payback period than the optimized gas-heated model at all locations; thus suggesting that heating electrification is cost effective and can reduce the majority of operational GHG emissions of existing housing stock in locations with low carbon intensity electric grid. (834KB) https://digital.library.ryerson.ca/islandora/object/RULA:8613/datastream/Calc_Lubna/view (284KB) https://digital.library.ryerson.ca/islandora/object/RULA:8613/datastream/AnAl_Lubna/view (4 MB) https://digital.library.ryerson.ca/islandora/object/RULA:8613/datastream/AnHr_Lubna/view (5MB) https://digital.library.ryerson.ca/islandora/object/RULA:8613/datastream/Wind_Lubna/view (6MB) https://digital.library.ryerson.ca/islandora/object/RULA:8613/datastream/Toro_Lubna/view (6MB) https://digital.library.ryerson.ca/islandora/object/RULA:8613/datastream/Thby_Lubna/view (6MB) https://digital.library.ryerson.ca/islandora/object/RULA:8613/datastream/Otta_Lubna/view
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".