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Hybrid Renewable Energy System with Storage for Electrification – Case Study of Remote Northern Community in Canada

2019· article· en· W2950706599 on OpenAlexafffundabout
Michela Longo, Wahiba Yaà ̄ci, Federica Foiadelli

Bibliographic record

VenueInternational Journal of Smart grid · 2019
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsNatural Resources Canada
FundersNatural Resources CanadaU.S. Department of Energy
KeywordsElectrificationRenewable energyMicrogridElectricityWind powerEnvironmental economicsEnvironmental scienceEnvironmental resource managementBusinessNatural resource economicsEngineeringEconomicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper’s primary objective constitutes addressing accessibility to modern energy as well as examining alternatives for diminishing petroleum derivative independency upon production of electricity for both underserved communities and remote northern populaces that are influenced by the negative effects of climatic changes heavily; an example here is Ontario, Canada’s Red Lake, which is Canadian isolated northern populations’ part. Accordingly, the execution of this microgrid advances improved well-being care as well as instruction while ensuring the green ecological factor in order to battle conditions of global warming within Ontario’s energy sector. Additionally, the electrification is needed to support the isolated communities as well as the nation to accomplish increasingly swift viable and societal goals. This investigation is carried out utilising the Hybrid Optimization Model for Electric Renewables tool referred to as HOMER . Various simulations with different setups were examined. It has been discovered that the microgrid with the utilisation of numerous sustainable power sources blend delivers an optimum result.

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 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 score0.626
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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 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

Citations11
Published2019
Admission routes3
Has abstractyes

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