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Record W3210010384 · doi:10.32920/ryerson.14660742.v1

Investigating The Impact Of Thermal Mass Towards Energy Efficient Housing In Canada

2021· preprint· en· W3210010384 on OpenAlexaboutno aff
Tasnuva Ahmed

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicScientific Research and Discoveries
Canadian institutionsnot available
Fundersnot available
KeywordsThermal massEnergy consumptionGlazingEfficient energy useEnvironmental scienceThermalEnergy conservationThermal insulationThermal energyEnergy (signal processing)Consumption (sociology)Architectural engineeringCivil engineeringEnvironmental economicsEnvironmental engineeringEngineeringMeteorologyMaterials scienceGeographyEconomicsPhysicsThermodynamicsElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

The building industry is striving for environmental friendly and energy efficient facility developments, as we have used most of our natural resources for comfortable living. Therefore energy efficient houses are very significant to reduce energy consumption. Thermal mass can be used as one of the many techniques of energy efficiency in the housing industry. Thermal mass can store heat in it which can be released at a later time. This behaviour of thermal mass can play a significant role in heating and cooling energy consumption of houses. This study investigates the impact of thermal mass on heating and cooling energy performance of a detached house and a row attached house, which are two main types of housing in Canada. Energy Plus simulation software has been used in the study. Also the study includes two different climatic conditions in Canada, such as Toronto and Vancouver, to envision how thermal mass behaviour changes with climates. All these different studies show thermal mass has significant impact on reduced energy consumption (15% savings in Vancouver for CCHT house) and lowering indoor air temperature. Other strategies such as insulation high R value, increased south face glazing and reduced glazing U value have been integrated with thermal mass to see energy performance in both climates. It hows more energy reduction than only thermal mass strategy. For instance, in CCHT house insulation high R value with concrete high mass reduces maximum 27% of total energy for Vancouver location.

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 score0.380
Threshold uncertainty score0.995

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 science0.0000.001
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 head0.022
GPT teacher head0.280
Teacher spread0.258 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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