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Record W4392042823 · doi:10.32920/25262782

The Efficiency of Air Source Heat Pumps as a Reliable HVAC Option For Homes in Future Climate Scenarios Across Canada

2024· preprint· en· W4392042823 on OpenAlexaboutno aff
Shane Jones

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHVACEnvironmental scienceAir conditioningHeat pumpMeteorologyEngineeringMechanical engineeringHeat exchangerGeography

Abstract

fetched live from OpenAlex

This study investigates the use of air source heat pumps (ASHPs) in a detached residential home. The study aims to assesses the performance of ASHPs to see whether these systems can provide reliable and energy efficient heating and cooling in mild, cold, and very cold climate zones across Canada. This study also investigates how the use of ASHPs in a warming Canadian climate will affect residential TUEI and GHG emission compared to a natural gas furnace within the home. An energy model was constructed in OpenStudio to perform the necessary simulations to compare the ASHP and the natural gas furnace in present and future scenarios. Theoretical analysis was also done to determine whether the ASHP system can provide reliable heating and cooling to detached residential homes inToronto (zone 6A),Vancouver(zone 5A)and QuebecCity(zone7). Findings indicated that ASHP systems would be best suited for residential use inVancouver (zone 5A) across all three weather scenarios (Present 1998-2014, Future 2030-2041 and Future 20562075)due to the milder temperatures annually. The colder climates for Toronto and Quebec City would require supplemental heating to meet the heating demand of the residence in those locations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.213
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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