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Application of air-source heat pump (ASHP) technology for residential buildings in Canada

2019· article· en· W2982163920 on OpenAlexaffabout
Artur Udovichenko, Lexuan Zhong

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHeat pumpEnvironmental scienceResidence time (fluid dynamics)Air source heat pumpsHeating systemResidenceMeteorologyHeat exchangerMechanical engineeringEngineeringGeography

Abstract

fetched live from OpenAlex

Abstract The air-source heat pump (ASHP) is a relatively new, highly efficient technology which has been proven to be a feasible alternative in mild temperature climates. However, an ASHP’s application tends to be limited by outdoor temperature in cold climate zones. This study was intended to explore the feasibility of ASHPs in conjunction with traditional furnaces to serve as an innovative energy efficient method for heating homes in Canada. The goal was to quantify the cost and emissions savings of an ASHP hybrid heating system as compared with a furnace-only system for a residence in three Canadian cities. A prototypical residential building with a hybrid heating system was generated for heating load calculations. The theoretical analysis included the determination of the outdoor-temperature-dependent heat loss rate from the residence and heat supply rate of ASHPs. The building model has proved to be useful for further analyses, while the feasibility of the hybrid system has been determined to be highly region dependent.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.170
Teacher spread0.167 · 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 designNot applicable
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

Citations7
Published2019
Admission routes2
Has abstractyes

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