Power Law Extrapolation of Wind Measurements for Predicting Wind Energy Production
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.
Bibliographic record
Abstract
This study investigates the level of uncertainty that would be expected if anemometer data from a short tower (less than 40 meters) was used to predict wind speeds and power production at typical utility-scale wind turbine hub-heights. Data from five tall towers was used to predict wind speeds at levels above 70 m based on anemometer data from levels below 40 meters. 1/7 power law, two level power law fit, and hybrids of these methods were applied. Predicted wind speeds were compared to the measured wind speeds at the higher levels to assess the level of error in the predictions. Accuracy of predicting upper level winds varied considerably between sites. Predicting this accuracy at a site without upper level wind measurements or prior knowledge of the upper level wind climate is very difficult, and significant uncertainty in the predicted results must be accepted.
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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 | 0.000 | 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 it