A Fuzzy Adaptive Probabilistic Wind Power Prediction Framework Using Diffusion Kernel Density Estimators
Why this work is in the frame
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Bibliographic record
Abstract
The inherent uncertainty in predicting wind power generation makes the operation and control of power systems very challenging. Probabilistic measurement of wind power uncertainty in the form of a reliable and sharp interval is of utmost importance, but construction of such high-quality prediction intervals (PIs) is difficult because wind power time series are nonstationary. In this paper, a framework based on the concept of bandwidth selection for a new and flexible kernel density estimator is proposed. Unlike previous related works, the proposed framework uses neither a cost function-based optimization problem nor point prediction results; rather, a diffusion-based kernel density estimator (DiE) is utilized to achieve high-quality PIs for nonstationary wind power time series. Moreover, to adaptively capture the uncertainties of both the prediction model and wind power time series in different seasons, the DiE is equipped with a fuzzy inference system and a tri-level adaptation function. The proposed framework is also founded based on a parallel computing procedure to promote the computational efficiency for practical applications in power systems. Simulation results demonstrate the efficiency of the proposed framework compared to well-known conventional benchmarks using real wind power datasets from Canada and Spain.
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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