Wind Power Prediction Interval Based on Predictive Density Estimation Within a New Hybrid Structure
Bibliographic record
Abstract
Wind power prediction interval (WPPI) is the most common technique to represent wind power (WP) uncertainty. This article proposes a novel WPPI approach developed based on predictive density estimation (DE). Unlike most WPPI models in the literature, the proposed model does not need to solve a high-dimensional optimization problem for model training. It optimizes the WPPIs using a single control variable—the bandwidth (BW) of DE—and trains the model directly and noniteratively using the quantiles extracted from the WP predictive density. For predictive DE, a novel application-specific method has been developed based on generalized cross-entropy (GCE). A precise but straightforward technique is designed to determine the optimal BW that results in the optimal WPPIs. The original GCE-based DE problem is also transformed into a convex quadratic programming formulation that can be solved quickly and uniquely. The WPPI model is employed in a new hybrid deterministic/probabilistic WPP (HDPWP) framework. Different from the conventional HDPWP approach that constructs WPPIs based on the point prediction error, the proposed framework incorporates WP point prediction among the predictor variables in the WPPI model, thereby improving performance. The effectiveness of the proposed methods is confirmed through extensive simulations and comparisons using real-world WP generation datasets.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".