Simulating the electrical demand variations of air-source heat pumps in Canadian single-family housing
Bibliographic record
Abstract
Heat pumps offer an efficient electrification of space heating but can significantly vary the magnitude and duration of house-level electrical demand, especially when replacing fuel-fired heating systems. This paper uses a simulation-based approach to simulate the electrical demand variations of air-source heat pumps (ASHPs) in six Canadian cities (Halifax, Montreal, Toronto, Winnipeg, Vancouver, Whitehorse) when replacing natural gas or electric baseboard heating. Detailed housing models (including a highly zoned model capturing room-based baseboard controls) are combined with a series of custom non-HVAC load profiles and an enhanced data-driven ASHP model to assess the variation that occupancy, climate, and type of heat pump integration can have on electrical demand. ASHPs are shown to increase the magnitude and duration of electrical demand when replacing natural gas furnaces, although selection of climate-appropriate systems (e.g. cold climate units in colder climates) may reduce the magnitude and coincidence of this demand. Where heat pumps replace electric baseboards, HVAC demand may decrease by up to 3.4 kW during ASHP operating periods and remain equal when the ambient temperatures are below the ASHP cutoff. A study of simple mitigation measures through varying temperature setpoints demonstrates the ability of proper controls to reduce electrical demand during high-demand periods.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".