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Record W2210662871 · doi:10.1109/tste.2015.2483489

Long-Term Renewable Energy Planning Model for Remote Communities

2015· article· en· W2210662871 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueIEEE Transactions on Sustainable Energy · 2015
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Waterloo
FundersNatural Resources CanadaUniversity of WaterlooCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsRenewable energySubsidyTerm (time)Energy planningElectricityWind powerEnvironmental economicsBusinessComputer scienceOperations researchEnvironmental resource managementEngineeringEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

This paper presents a novel long-term renewable energy (RE) planning model for remote communities (RCs), considering the characteristics of diesel-based RCs in Canada and other parts of the world such as Alaska and northern Chile. Over the past few years, there has been a significant increase in assessing and deploying RE projects in northern remote locations. The model proposed in this paper adds to such efforts by creating a multiple-year community planning tool that can be used to determine economic and technically feasible RE solutions, considering the current operating structures, electricity pricing systems, subsidy frameworks, and project funding alternatives under which RE can be deployed in RCs. The proposed model is implemented in a case study for the Kasabonika Lake First Nation community in northern Ontario. The case study shows that RE projects can be feasible under current operating conditions, for a set of funding alternatives that share the economic risks.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.045
GPT teacher head0.273
Teacher spread0.228 · 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