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Record W4385873582 · doi:10.1080/19401493.2023.2245796

Household energy and comfort impacts under teleworking scenarios via a zoned residential HVAC system

2023· article· en· W4385873582 on OpenAlexafffundabout
Melina Sirati, William O’Brien, Cynthia A. Cruickshank

Bibliographic record

VenueJournal of Building Performance Simulation · 2023
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHVACThermal comfortArchitectural engineeringEnergy (signal processing)Environmental scienceEngineeringBuilding energy simulationEfficient energy useCivil engineeringAir conditioningEnergy performanceMeteorologyGeographyMechanical engineering

Abstract

fetched live from OpenAlex

Teleworking is a prevalent example of partial building occupancy and prevents homeowners from using a setback or set up temperature setpoint to save energy. Due to a lack of certainty on how teleworking affects houses, this study aimed to quantify energy consumption and thermal comfort under different teleworking scenarios. Likewise, the effect of HVAC zoning by comparing two-zone houses with one-zone houses is investigated. Three teleworking scenarios across six Canadian climate zones were used to assess two house models with two HVAC zoning configurations. The results of this study indicate that teleworking increases energy consumption by up to 9% in houses. By employing the two-zone house instead of the one-zone house, energy performance benefits from using two thermostats in a house. The results show that two-zone HVAC systems can reduce energy consumption by up to 31% and thermal discomfort by up to 24% compared to traditional one-zone 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 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 score0.085
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.013
GPT teacher head0.221
Teacher spread0.208 · 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

Citations7
Published2023
Admission routes3
Has abstractyes

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