MétaCan
Menu
Back to cohort
Record W2783802548 · doi:10.1109/ipact.2017.8245079

A feasibility study of low voltage DC distribution system for LED lighting in building

2017· article· en· W2783802548 on OpenAlexaff
Sujoy Barua, Md. Shahadat Hossain, Md. Sultan Mahmud, Md. Wazedur Rahman

Bibliographic record

Venue2017 Innovations in Power and Advanced Computing Technologies (i-PACT) · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectricityPhotovoltaic systemElectrical engineeringVoltageLow voltagePhotodiodeComputer scienceAutomotive engineeringEngineeringPhysicsOptoelectronics

Abstract

fetched live from OpenAlex

An analysis is presented to show the techno-economical benefit of low voltage DC (LVDC) distribution to the lighting system in building through Solar Photovoltaic (PV) system. The proposed DC distribution system is considered by replacing conventional T8 linear fluorescent light (LFL) to LED light achieving improved efficiency powered by solar energy. The result shows that 60% capacity of load is reduced, saving 1051.2 MWh electricity which is mitigated 704.304 ton CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission by paying back at 9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> year of the project cost for the 20 year period Life Cycle Cost (LCC) analysis.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.011
GPT teacher head0.274
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

Same venue2017 Innovations in Power and Advanced Computing Technologies (i-PACT)Same topicElectric Vehicles and InfrastructureFrench-language works237,207