Techno-economic analysis of air-source heat pump (ASHP) technology for single-detached home heating applications in Canada
Bibliographic record
Abstract
The air-source heat pump (ASHP) is a popular system that does not see much use in cold-climates despite its high potential in low carbon footprint. This study was designed to evaluate the techno-economic feasibility of its application to single-detached homes in Canada. First, a set of support vector regression (SVR) models was developed by a housing database for prediction of the exposed surface areas of homes in five Canadian cities: Vancouver, Toronto, Montreal, Edmonton, and Yellowknife. The predicted areas were then used to estimate the heat demands of all homes. As a result, the technical evaluation was conducted by comparison of the heat loss rate with the heat supply rate of ASHPs. Annual energy consumption was calculated using a bin method for furnace-alone and furnace/ASHP hybrid systems. Seasonal operating costs and greenhouse gases (GHG) emissions were estimated by utility costs and emissions factors for each city. Our findings show that Vancouver, Toronto, and Montreal are technically feasible to adopt the ASHP technology for economic and low GHG emission benefits. Although currently Edmonton and Yellowknife could not theoretically gain ASHP’s benefits, the ASHP technology is still a promising technology to be implemented in the future if renewable energy infrastructures are established.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".