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Record W1979649094 · doi:10.1142/s0218539314500119

RELIABILITY ANALYSIS OF HYBRID ENERGY SYSTEM

2014· article· en· W1979649094 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.

Bibliographic record

VenueInternational Journal of Reliability Quality and Safety Engineering · 2014
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsConcordia UniversityUniversity of Alberta
Fundersnot available
KeywordsRenewable energyHybrid powerIntermittent energy sourceHybrid systemWind powerReliability (semiconductor)Reliability engineeringComputer scienceEnergy sourceEnergy storageElectric power systemDistributed generationAutomotive engineeringPower (physics)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

A hybrid energy system integrates renewable energy sources like wind, solar, micro-hydro and biomass, fossil fuel power generators such as diesel generators and energy storage. Hybrid energy system is an excellent option for providing electricity for remote and rural locations where access to grid is not feasible or economical. Reliability and cost-effectiveness are the two most important objectives when designing a hybrid energy system. One challenge is that the existing methods do not consider the time-varying characteristics of the renewable sources and the energy demand over a year, while the distributions of a power source or demand are different over the period, and multiple power sources can often times complement one another. In this paper, a reliability analysis method is developed to address this challenge, where wind and solar are the two renewable energy sources that are considered. The cost evaluation of hybrid energy systems is presented. A numerical example is used to demonstrate the proposed method.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.246
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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