A Nonparametric Probability Distribution Model for Short-Term Wind Power Prediction Error
Bibliographic record
Abstract
Accurate wind power prediction error (WPPE) modeling is of high importance in power systems with large scale wind power generation containing high level of uncertainty. Since WPPE cannot be entirely removed, providing its accurate probability distribution model can assist power system operators in mitigating its negative effects on decision making conditions. In this paper, unlike previous related works, a nonparametric model is presented using kernel density estimation (KDE) with an efficient bandwidth (BW) selection technique called “advanced plug-in” technique. The utilized BW selection technique enables KDE to accurately estimate important features of WPPE distribution, e.g., fat tails, high skewness and kurtosis. The proposed WPPE modeling approach is simulated using one-year time series of real wind power and corresponding predicted values for 1-hour look-ahead time. The efficacy of the proposed WPPE model is depicted using Centennial wind farm dataset in south of Saskatchewan province in Canada. Results show that parametric distribution models like Normal, Stable, and so on may not properly model the uncertainty of WPPE.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".