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Record W1955850335 · doi:10.1063/1.4919021

Meteorological phenomena associated with wind-power ramps downwind of mountainous terrain

2015· article· en· W1955850335 on OpenAlexafffundabout
Michael Sherry, David E. Rival

Bibliographic record

VenueJournal of Renewable and Sustainable Energy · 2015
Typearticle
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsQueen's UniversityUniversity of Calgary
FundersAlberta Innovates - Technology Futures
KeywordsWind powerEnvironmental scienceMaximum sustained windWind speedMeteorologyAtmospheric instabilityWind profile power lawAtmospheric sciencesTerrainWind directionWind gradientGeologyGeographyEngineering

Abstract

fetched live from OpenAlex

Wind-power ramps are a significant source of uncertainty in wind-energy forecasting and are a challenge to electric-grid stability. In the Canadian province of Alberta, strong westerly winds buffet the Rocky Mountains creating an abundant yet intermittent wind energy resource in the plains of Alberta. In the current study, wind-power ramp events have been detected and correlated to several environmental factors including time-of-day, atmospheric stability, season and a Föhn wind event known locally as a Chinook wind at a field wind measurement station downstream of the Rocky Mountains. Large wind-power ramps (a 50% change in power in less than 4 h) were found to occur on days when a Föhn wind was present over 50% of the time. The result highlights the importance of this meteorological phenomenon to wind energy production locally and also in regions where Föhn winds occur. The detected wind-power ramps were found to vary significantly with season, with the strongest wind-power ramps emanating from the Rocky Mountains in the winter months under stable atmospheric 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.194
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations29
Published2015
Admission routes3
Has abstractyes

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