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Record W2061448762 · doi:10.1109/pedg.2014.6878664

Energy cost estimation of small wind power systems - An integrated approach

2014· article· en· W2061448762 on OpenAlexaff
Shuang Xu, Craig Church, Riming Shao, Liuchen Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsWind powerElectric power systemReliability engineeringReliability (semiconductor)Monte Carlo methodTurbineBenchmark (surveying)Computer scienceWind speedPower (physics)GridAutomotive engineeringEngineeringElectrical engineeringMeteorologyAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents a new method for estimation of energy cost for grid-connected small wind power systems (SWPSs), which integrates the wind resources, turbine power curves, system component losses and costs, and reliability into a comprehensive model. For each of these aspects, models were established. Various structural options of small wind power system components at both fixed speed and variable speed operation were considered. Through Monte-Carlo based simulations, a statistical estimation of energy costs of a typical 30kW small wind power system was obtained for the life cycle operation. The simulation results can give consumers an overview of how much they need to invest for a small wind power system during its installed lifetime. Furthermore, the results of different structural options can serve as a lookup table to provide the cost effectiveness of different structural types of small wind power systems, and also provide a benchmark for future research works on energy cost evaluation.

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)0.000
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: none
Teacher disagreement score0.974
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.009
GPT teacher head0.187
Teacher spread0.178 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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