A comparative analysis of wind speed probability distributions for wind power assessment of four sites
Bibliographic record
Abstract
In this paper, five probability distribution functions are employed to fit the wind speed data from four different geographical locations in the world in a preliminary analysis. These wind regimes are selected such that they represent wide ranges of mean wind speeds and present different shapes of wind speed histograms. The wind speed data used for modelling consist of 10-min average SCADA data from three US wind farms and hourly averages recorded at a weather station in Canada. Out of the five, three functions, namely Weibull, Rayleigh, and gamma, which provide a better fit to the data, are selected to carry out further analyses. This study investigates the ability of these functions to match different statistical descriptions of wind regimes. Parameter estimation is done by the method of moments, and models are evaluated by root mean square error and R square methods. The suitability of PDFs to predict the wind power densities and annual energy production using manufacturers' power curve data at three of the selected sites is analysed. Power curves extracted from actual data of one wind farm using novel four- and five-parameter logistic approximations are also introduced here for energy analyses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".