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Record W2226966177 · doi:10.1016/j.egypro.2015.11.657

Daylight Performance of Perimeter Office Façades utilizing Semi-transparent Photovoltaic Windows: A Simulation Study

2015· article· en· W2226966177 on OpenAlexafffund
Konstantinos Kapsis, Vasken Dermardiros, Andreas Athienitis

Bibliographic record

VenueEnergy Procedia · 2015
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDaylightingDaylightPhotovoltaic systemPerimeterOpacityTransmittanceEngineeringOpticsFacadeWindow (computing)OptoelectronicsMaterials scienceComputer scienceStructural engineeringArchitectural engineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents the potential impact of semi-transparent photovoltaic windows on the daylighting performance of commercial building façades. The performance of three façade configurations is examined, integrating Si-based, opaque spaced cells and transparent thin film technologies. Simulation results suggest that a semi-transparent photovoltaic module with visible effective transmittance of 30%, integrated as the outer glass layer of a double-glazed window, provides sufficient daylight within the perimeter zone throughout the year, with sDA 300lx/50% =1 and DGI=5%. Moreover, a three-section façade configuration integrating Si-based spaced PV cells on the upper section and thin film PV on the middle section of the façade has the potential to maximize daylight utilization and the view to the outdoors.

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)0.000
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.033
GPT teacher head0.241
Teacher spread0.207 · 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

Citations40
Published2015
Admission routes2
Has abstractyes

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