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Record W2313564774 · doi:10.1115/es2008-54255

Modelling of the Net Zero Energy Town House in Toronto Using TRNSYS, and an Analysis of the Impact Using Thermal Mass

2008· article· en· W2313564774 on OpenAlexafffundabout
Omar Siddiqui, Alan S. Fung, Humphrey Tse, Dahai Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTRNSYSZero-energy buildingThermal massBuilding envelopeEnvironmental scienceWork (physics)ThermalDynamic insulationBrickThermal insulationMeteorologyEngineeringNuclear engineeringCivil engineeringVacuum insulated panelEnergy consumptionMaterials scienceMechanical engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

A detailed model of the Net Zero Energy Town House in Toronto is developed in TRNSYS, incorporating a ground source heat pump integrated with an in floor radiant heating system. In order to minimize the heating and cooling loads, the building envelope is well insulated with the exterior walls having an R-60 insulation value. Much of the work done previously on the use of thermal mass in buildings has been experimental in nature and has focussed mainly on conventional brick construction in hot climates such as Asia and Africa. This research will analyze the impact of using thermal mass with a building envelope that is highly insulated, and of a light construction, such as that used in Low Energy or Net Zero housing. Furthermore, this analysis would also evaluate the impact of using thermal mass in a cold climate such as that found in Canada. The simulations showed that, for colder climates, thermal mass can replace some of the insulation and still provide superior results. Also the impact of thermal mass was found to be more significant during the winter season than summer for Toronto.

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 score0.220
Threshold uncertainty score0.984

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 head0.025
GPT teacher head0.232
Teacher spread0.207 · 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

Citations9
Published2008
Admission routes3
Has abstractyes

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