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Record W3150999108 · doi:10.1051/e3sconf/202124606004

Simulation based assessment on representativeness of a new performance rating procedure for cold climate air source heat pumps

2021· article· en· W3150999108 on OpenAlexaffabout
Alex Lachance, Justin Tamasauskas, Stéphanie Breton, Solange Prud’homme

Bibliographic record

VenueE3S Web of Conferences · 2021
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsRepresentativeness heuristicEnvironmental scienceCold climateSubarctic climateRange (aeronautics)Reliability engineeringMeteorologyComputer scienceEngineeringStatisticsEcology

Abstract

fetched live from OpenAlex

Cold climate air-to-air heat pumps (CCHP) offer a strong potential for energy use reductions in Canadian homes. Proper selection of the unit is critical in order to take advantage of the improved efficiency and increased heat capacity at low ambient temperatures and ability to modulate to meet a wide range of heating loads. A new performance rating procedure (CSA EXP07) was developed to better represent the seasonal energy efficiency of CCHP systems versus current test procedures that do not always accurately characterize the response to dynamic loads in a colder climate zones, thus resulting in inaccurate equipment rating. To validate the representativeness of the new performance rating procedure and quantify the potential over-or underestimation of energy savings using current performance rating procedures, a CCHP data-driven model is developed and simulated in a code-compliant single-detached Canadian home for different climate regions: Marine, Cold-Humid, Cold-Dry, Very-Cold and Subarctic. These energy models then serve as the basis for comparing the seasonal heating coefficient of performance of a CCHP system, which can be compared to the current and newly proposed performance rating procedure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.333
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

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

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 head0.031
GPT teacher head0.303
Teacher spread0.272 · 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 teacher head, 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

Citations1
Published2021
Admission routes2
Has abstractyes

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