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Evaluating the Need and Potential of Equipping North American Houses with Multi-Zone VAV Systems

2011· article· en· W2035282367 on OpenAlexafffundabout
Ka Long Ringo Ng, Zai Yi Liao

Bibliographic record

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOverheating (electricity)ControllabilityThermal comfortEnergy performanceArchitectural engineeringEnvironmental scienceEngineeringCivil engineeringEfficient energy useMeteorologyGeography

Abstract

fetched live from OpenAlex

To identify potential energy savings and improvements to thermal comfort in the Canadian residential sector, a survey on occupant behaviour and control of thermal environment was conducted from April-June 2009 in low-rise dwellings in Ontario, Canada. A total of 396 completed responses were received. Survey results show that approximately 20% of the respondents were not satisfied with their room temperature in the winter. Inadequate level of controllability to room temperature is perceived as the most serious problem. Problems associated with overheating during the winter and overcooling during the summer was also identified. Observations from the survey results helped identify the deficiencies of the heating equipments built today and suggest improvements should be made to increase the controllability of current systems. Thermal simulation was then conducted to identify the problems with single-zone systems commonly built today, and to investigate the potential retrofit alternative. Simulation results show that a multi-zone system can effectively mitigate the deficiencies suffered by existing systems and can drastically improve the energy performances of houses.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.255

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 head0.100
GPT teacher head0.346
Teacher spread0.246 · 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
Published2011
Admission routes3
Has abstractyes

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