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Record W3135155481 · doi:10.1080/19401493.2021.1894485

A workflow for evaluating occupant-centric controls using building simulation

2021· article· en· W3135155481 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Building Performance Simulation · 2021
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsUniversity of TorontoCarleton University
FundersNatural Resources Canada
KeywordsWorkflowComputer scienceArchitectural engineeringEngineeringSimulationSystems engineeringDatabase

Abstract

fetched live from OpenAlex

Indoor climate and lighting in office buildings are operated using static and conservative setpoints and schedules that are sub-optimal for real occupancy/occupants’ diverse preferences. In contrast, occupant-centric control (OCC) is an operational strategy whereby occupancy/occupants’ preferences are estimated to improve energy efficiency and comfort. This paper develops a practical workflow for implementing and evaluating OCCs via building simulation. A library of five OCC functions is introduced for use with building sensor data. EnergyPlus simulations of a generic nine-office testbed are performed with combinations of OCCs, climates, and envelope assemblies to demonstrate the workflow; the results showed that the energy use and thermal discomfort (the number of hours spent outside a selected thermal comfort zone) could be reduced by up to 37% and 65%, respectively, when OCCs were implemented in the testbed. This workflow allows for a diverse combination of OCCs, climates, and envelopes that can be expanded upon practically and incrementally.

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.

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)0.001
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 score0.364
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.328
Teacher spread0.284 · 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