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Record W2151296251 · doi:10.1109/ghtc.2013.6713648

Design of a hybrid renewable energy system for a northern ontario community

2013· article· en· W2151296251 on OpenAlexaffabout
Marielle Magtibay, Diana Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRenewable energyDiesel generatorCapital costEnvironmental economicsEnvironmental scienceHydropowerElectric power systemMicro hydroDiesel fuelAutomotive engineeringEngineeringPower (physics)Electrical engineeringEconomics

Abstract

fetched live from OpenAlex

Renewable energy alternatives for off-grid northern communities are investigated via a specific case study of Fort Hope, Ontario. The current energy systems in most off-grid communities consist of diesel generators, which can be both financially and environmentally unsustainable. In lieu of this, a hybrid system comprised of a 636 kW run-of-the-river hydropower, existing 650 and 375 kW diesel generators, and 150 kg hydrogen storage and 100 kW fuel cell system is proposed. The design achieves a renewable energy penetration of 54%, a levelised cost of energy 41% less than the baseline model, and a reduction of 2,000 tonnes in annual diesel generator emissions. The HOMER model developed for the design is compared to RETScreen results; total power output deviated by 2%, however, renewable energy penetration varied largely due to monthly versus hourly modelling techniques and different controls on the dispatch strategy in each modeling software. Total capital costs and annual operating costs of the proposed design are $4.6 million and $990,000, respectively. Renewable energy systems can be considered a solution to humanitarian concerns due to increasing utility costs, emissions and load constraints. Renewable energy systems in remote communities require further research and development to lower equipment costs and enhance policy to achieve financial viability.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.206
Teacher spread0.180 · 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.

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

Citations2
Published2013
Admission routes2
Has abstractyes

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