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Integrated Lighting Efficiency Analysis in Large Industrial Buildings to Enhance Indoor Environmental Quality

2017· preprint· en· W4241136629 on OpenAlexafffund
Dušan Katunský, Erika Dolníková, Saeed Doroudiani

Bibliographic record

VenuePreprints.org · 2017
Typepreprint
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsNorth York General Hospital
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAVUniversity of TorontoSlovenská Akadémia Vied
KeywordsArchitectural engineeringMicroclimateDaylightingQuality (philosophy)ArchitectureIndoor air qualityEnvironmental qualityEnvironmental scienceCivil engineeringEngineeringEnvironmental engineeringGeography

Abstract

fetched live from OpenAlex

The physical parameters related to indoor lighting in large industrial halls in winter and summer periods were analyzed using in situ measurements and computational methods. Here, we present part of our observations from a comprehensive research on indoor environmental quality of industrial halls with the aims of saving energy and providing a comfortable environment for the workers. The results showed that the procedures used for evaluation of residential or office buildings may not be used for industrial buildings. We also observed that the criteria for occupants’ comforts for indoor industrial buildings may differ from those of other kinds of buildings. Based on these results, an adequate attention is required while designing the industrial buildings. For this reason, appropriate evaluation methods and criteria should be created. Manufacturing halls are integral parts of industrial architecture, including buildings for light industries. Workers spend a substantial part of the time indoor; therefore, it is necessary to pay attention to design, construction, and evaluation of internal spaces of buildings and the occupants’ comfort. The focus must be given particularly to heating and cooling, moisture, and lighting microclimate. We present some observations from evaluation of internal environmental quality of industrial halls with priority on daylighting in combination with the integral lighting.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.064
GPT teacher head0.327
Teacher spread0.264 · 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.

Study designObservational
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

Citations10
Published2017
Admission routes2
Has abstractyes

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