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Record W4244171621 · doi:10.5383/ijtee.14.01.002

The impact of increasing temperatures in transition zones on energy demand

2017· article· en· W4244171621 on OpenAlexvenueno aff
T Kansara

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsASHRAE 90.1Lift (data mining)Environmental scienceCooling loadElectricityThermal comfortElectricity demandCivil engineeringEnvironmental economicsComputer scienceMechanical engineeringEngineeringMeteorologyElectricity generationAir conditioningThermodynamicsEconomicsGeographyElectrical engineering

Abstract

fetched live from OpenAlex

This paper describes the transitional zones of modern buildings and the impact of raising their temperature. A transitional zone is described as none steady-state spaces like entrance lobbies, corridors, lift-lobbies and landings, which allow occupants to transition through to more steady-state spaces. This paper presents the results of a dynamic simulation, where a typical case study building is used for an intervention of 1-5ºC increases in indoor temperature on energy demand. The results show raising the temperature in the transitional zones can result in a saving of 0.63% per ºC reduction of cooling for the whole building. The recommendation of this paper is to investigate a broadening of the thermal comfort parameters of these communal areas not serviced by the ASHRAE-55: 2-13, or any other standard, in order to identify the potential for reducing electricity used for cooling. Applying sensible engineering design load calculations will ensure comfort conditions and energy use are treated separately to occupied zones.

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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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