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Record W2012092955 · doi:10.1115/es2014-6455

Performance Evaluation of an Air Source Heat Pump Coupled With a Building Integrated Photovoltaic/Thermal (BIPV/T) System

2014· article· en· W2012092955 on OpenAlexaff
Getu Hailu, Peter Dash, Alan S. Fung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBuilding-integrated photovoltaicsPhotovoltaic systemRoofHeat pumpThermalAir source heat pumpsEnvironmental scienceAutomotive engineeringNuclear engineeringEngineeringMechanical engineeringMeteorologyElectrical engineeringStructural engineeringPhysicsHeat exchanger

Abstract

fetched live from OpenAlex

A theoretical investigation of a variable capacity air-to-air air source heat pump (VC-ASHP) coupled with a building integrated photovoltaic/thermal (BIPV/T) system is presented in this paper. The BIPV/T system was integrated into the roof and the wall. Air was circulated behind the photovoltaic arrays to recover the thermal energy. The warm air recovered was supplied to the VC-ASHP. The thermal performance of the VC-ASHP was investigated for three scenarios when the heat pump is running in heating mode. The three scenarios are: (A) by feeding the ambient air to the ASHP; (B) by coupling the ASHP to the wall integrated BIPV/T only; and (C) by coupling the ASHP to the roof integrated BIPV/T only. The coefficient of performance (COP) of the VC-ASHP was evaluated for these three separate scenarios and compared. A typical winter day result suggests that the COP of the ASHP can be improved by coupling the VC-ASHP to either of the BIPV/T systems, i.e., either to the roof integrated BIPV/T system or to the wall integrated BIPV/T system.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.193
Teacher spread0.185 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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