The Impact of Variable Wind Shear Coefficients on Risk Reduction of Wind Energy Projects
Bibliographic record
Abstract
Estimation of wind speed at proposed hub heights is typically achieved using a wind shear exponent or wind shear coefficient (WSC), variation in wind speed as a function of height. The WSC is subject to temporal variation at low and high frequencies, ranging from diurnal and seasonal variations to disturbance caused by weather patterns; however, in many cases, it is assumed that the WSC remains constant. This assumption creates significant error in resource assessment, increasing uncertainty in projects and potentially significantly impacting the ability to control gird connected wind generators. This paper contributes to the body of knowledge relating to the evaluation and assessment of wind speed, with particular emphasis on the development of techniques to improve the accuracy of estimated wind speed above measurement height. It presents an evaluation of the use of a variable wind shear coefficient methodology based on a distribution of wind shear coefficients which have been implemented in real time. The results indicate that a VWSC provides a more accurate estimate of wind at hub height, ranging from 41% to 4% reduction in root mean squared error (RMSE) between predicted and actual wind speeds when using a variable wind shear coefficient at heights ranging from 33% to 100% above the highest actual wind measurement.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".