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Record W1985168609 · doi:10.1109/pesgm.2012.6345495

Intra-hour wind power characteristics for flexible operations

2012· article· en· W1985168609 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsWind powerLaplace transformWind speedSkewWind profile power lawMeteorologyProbability density functionSpectral densityEnvironmental scienceMathematicsStatisticsComputer scienceEngineeringPhysicsMathematical analysisTelecommunications

Abstract

fetched live from OpenAlex

In this paper we analyze variability over time in wind energy, focusing primarily on intra-hour variations. We model the time duration dependency and other conditional aspects of wind variability. We analyze variability both in the time and frequency domains and relate this analysis with empirical variability probability distributions using historical wind power from the Bonneville Power Authority. Distribution fitting of in-tra-hour variability was done, considering normal, Laplace and skew-Laplace distributions. We found that of the variability is skewed and peaky indicating that skew-Laplace distributions are superior to other probability density functions to describe wind variability in time intervals of less than an hour. The time domain interpretation of the power spectral density estimates of the wind power variability indicate that for time duration of 30 minutes to 1 hour wind fluctuation can be quite considerable. We discuss the results of our analysis in relation to its potential short-term power system operations implications with high wind power penetration.

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)0.000
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 score0.960
Threshold uncertainty score0.340

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 head0.010
GPT teacher head0.224
Teacher spread0.213 · 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

Quick stats

Citations24
Published2012
Admission routes1
Has abstractyes

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