Wind speed and direction variability evaluation in a multiscale perspective
Bibliographic record
Abstract
A comprehensive and effective evaluation of wind pattern variability can offer valuable information for important purposes, such as decreasing uncertainties related to wind energy availability, designing systems based on the integration of multiple wind farms to address power intermittency, or assessing implications for yaw error minimization. This paper presents a multiscale approach to wind pattern analysis taking into account wind speed as well as wind direction. First, wind speed time series are analyzed using a multiscale approach (Detrended Fluctuation Analysis). Based on the results of this step, isopersistence diagrams are constructed to reflect the scale-by-scale behaviour of the wind pattern, which offers a nuanced and comprehensive perspective on pattern variability and on the temporal change in the way in which variability depends on the time scale range. Next, wind speed patterns are analyzed by assessing orientation dependent time series obtained by projecting wind speed values for every sample on a plane that is rotated step by step by a small angle. The outcome consists of a set of orientation‐time scale‐persistence diagrams. The proposed methodological framework is applied to data streams of wind speed and direction. It is illustrated with application examples using data recorded in different areas in Canada and the United States.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".