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Record W4317517523 · doi:10.24018/ejece.2023.7.1.486

Design and Simulation of a Hybrid Power System for St. Lewis in Labrador

2023· article· en· W4317517523 on OpenAlexaffabout
Yuet Sing Li, Mohammad Bahrami, Mohammad Faraji Nejad

Bibliographic record

VenueEuropean Journal of Electrical Engineering and Computer Science · 2023
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRenewable energyLivelihoodElectricityEnergy sourceGreenhouse gasSituatedEnergy supplyEnvironmental scienceBusinessEngineeringNatural resource economicsEnergy (signal processing)GeographyElectrical engineeringComputer scienceEconomicsEcology

Abstract

fetched live from OpenAlex

St. Lewis is one of the isolated communities situated in Newfoundland and Labrador (NL). The easternmost province of Canada includes over seven thousand small islands with scattered populations. It brings various challenges to the electricity power supply companies, such as power outages and long resumption times due to the remote locations. Currently, three sets of diesel generators are supplying the electricity. The site is vulnerable to power outages if there is any malfunction of the generators or failure of the fuel supply through the trucks. Introducing an alternative energy source to the community can increase energy security and ensure livelihood for the residents. By utilization of the renewable energy, Greenhouse Gas Emission of rural communities can be largely reduced. This research aims to demonstrate St. Lewis as the epitome of hybrid renewable energy systems among those remote communities and the feasibility of alternative energy solutions for those communities.

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 score0.913
Threshold uncertainty score0.173

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.204
Teacher spread0.193 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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