Mapping wind speed distribution across large regions using machine learning and asymmetric kernel estimators.
Bibliographic record
Abstract
Wind resource assessment studies over large regions provide the basis for the preliminary identification of locations with promising wind energy prospects. In past studies, several authors have mapped the mean wind speed across large regions using spatial interpolation methods or machine learning models. In recent studies, more emphasis has been placed on mapping the entire wind speed distribution to evaluate the wind resource variability at unsampled locations. Most of these studies have assumed that the wind speed distribution across the entire region belongs to a single family of probability distribution functions and then processed to map the distribution parameters. A flexible non-parametric approach for wind speed distribution mapping is proposed in this study. The new approach is based on mapping various wind speed quantiles at some fixed percentile points in the region using a machine learning model. Then, at any unsampled location, these quantiles are used as input of an asymmetric kernel estimator of cumulative distribution function to recover the whole wind speed distribution. Asymmetric kernel estimators solve the probability leakage problem that appears when fitting symmetric kernels to bounded variables such as wind speed. The non-parametric approach for wind speed distribution mapping was more effective than a traditional approach based on mapping the parameters of a distribution function. In the best scenario, an improvement was observed between 6% (test samples) and 9% (cross-validation) of the Kolmogorov-Smirnov statistic between the observed and estimated wind speed distribution. The non-parametric approach is recommended for regions with highly variable wind regimes that cannot be captured by a single family of distribution functions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".