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Record W2092081531 · doi:10.1109/tste.2014.2364778

Data Constrained Adequacy Assessment for Wind Resource Planning

2014· article· en· W2092081531 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

VenueIEEE Transactions on Sustainable Energy · 2014
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWind powerWind speedElectric power systemEnvironmental scienceOffshore wind powerMeteorologyElectricity generationComputer sciencePower (physics)EngineeringElectrical engineeringGeography

Abstract

fetched live from OpenAlex

Environmental concerns caused by burning fossil fuel and the safety concerns associated with nuclear power plants have led to increased interest and investment in wind power. Wind penetration in power systems has been rapidly increasing worldwide and has resulted in increased variability and uncertainty in power generation. Proper modeling of the wind resource has, therefore, become increasingly important in modern wind-integrated power systems. The correlation between wind speeds at multiple wind farms considerably affects the overall variability of wind power generation. Many power utilities are considering expansion to multiple wind farms. Analysis of wind power at different sites requires sufficient time-synchronized wind data in order to incorporate their cross-correlations in the evaluation model. Such data are usually not available or very limited for many prospective wind sites that may be considered in energy planning or policy making. This paper proposes a simple analytical method to develop approximate wind models when time-synchronized wind data for two wind sites are not available and further extends the method to incorporate more than two wind sites.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.257
Teacher spread0.240 · 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