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Record W4393103530 · doi:10.56007/arrivet.v1i1.14

Hybrid Wind-Solar Energy System for Remote Locations in Northern Alberta

2023· article· en· W4393103530 on OpenAlexafffundabout
Muhammad Burhan, Yuri Montanholi, Quamrul Huda

Bibliographic record

VenueApplied Research Results in Vocational Education & Training · 2023
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsLakeland CollegeNorthern Alberta Institute of Technology
FundersAlberta Innovates
KeywordsMeteorologyRemote sensingEnvironmental scienceSolar energySolar windGeologyGeographyEngineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Using renewable energy resources as alternatives to fossil fuel systems is encouraged globally by government and other agencies to achieve greenhouse gas emission reduction targets. Solar and Wind energies are the two widely common renewable energy resources and have reached a mature technological phase for various applications in rural and urban settings. However, these energy resources are prone to intermittency with temporal and seasonal availability. In a cold climate region like northern Alberta, the winter conditions limit solar energy availability significantly. On the other hand, wind energy in the region has consistent availability most of the time. In this work, to develop a standalone all-season sustainable energy system at remote locations, such as rural areas and agricultural applications, a hybrid wind-solar system configuration is modeled, simulated, and analyzed to achieve 200 watts of uninterrupted power in Alberta weather conditions. The results show that wind energy production compliments solar energy production during the wintertime. The hybrid configuration can meet a consistent load demand that is difficult to implement in northern Alberta conditions.

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)0.002
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.747
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.051
GPT teacher head0.317
Teacher spread0.266 · 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
Published2023
Admission routes3
Has abstractyes

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