A Hybrid Probabilistic Wind Power Prediction Based on An Improved Decomposition Technique and Kernel Density Estimation
Bibliographic record
Abstract
The high uncertainty of non-stationary wind power time series is a challenging issue in optimal operation and planning of power systems. An efficient way to show wind power uncertainty is to use high-quality prediction intervals (PIs). This paper proposes a hybrid probabilistic wind power prediction model based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) technique, extreme learning machine (ELM) and kernel density estimation (KDE). First, using ICEEMDAN, the original wind power time series is decomposed to components with different frequency ranges. Then, sample entropy (SampEn) technique is employed to group components to three main time series trend, cycle, and noise with diverse complexity levels. The first two components are deterministically predicted while the noise component is probabilistically predicted using the combination of KDE technique and direct plug-in as a well-known bandwidth selection technique. The lower and upper bounds of final PI are found using the summation of lower and upper bounds of noise component with trend and cycle predicted points. The efficacy of the proposed prediction model is depicted by generating reliable and sharp PIs for real wind power datasets in Canada and comparing with other conventional PI construction approaches.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".