Experimental investigation of building mock-ups and air source heat pumps in cold climates
Bibliographic record
Abstract
Air source heat pumps (ASHPs) are critical in reducing carbon emissions, particularly in extreme cold climates like Canada. However, less than 10% of residential buildings in Canada utilize heat pumps, underscoring the need for more energy-efficient solutions to achieve net-zero and passive house standards. This study simultaneously evaluates the performance of building mock-ups and a commercial ASHP at extremely cold temperatures of 0 °C, -10 °C, and -25 °C, simulated in a climatic chamber using co-heating and traditional disaggregate methods. Two envelope types are evaluated: one constructed to meet the requirements of the Canadian Building Code (CBC) and another with retrofitted walls aimed at enhancing the insulation. The envelope's thermal performance shows a deviation of no more than 7% between the co-heating and disaggregate methods, while theoretical calculations show deviations of up to 23%. The ASHP data reveal that enhanced insulation not only limits the envelope's heat loss but also significantly improves the ASHP performance by consuming less energy while maintaining a steady thermal output. At -10 °C, the retrofitted envelope increases the ASHP performance by 28% and by 100% at -25 °C, relative to the performance of the CBC. These findings underscore the importance of improved building envelopes in enhancing ASHP performance and delivering sustainable heating solutions for cold climates.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".