Wavelet Neural Network Based Multiobjective Interval Prediction for Short-Term Wind Speed
Why this work is in the frame
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Bibliographic record
Abstract
As a source of clean and pollution-free renewable energy, wind power has attracted much attention and has been increasingly integrated into the existing power system. However, the uncertain and volatile wind speed makes the utilization of wind power a challenging task. Hence, it is essential to design an accurate forecast method to deal with the uncertainty caused by wind speed. This paper proposes a multiobjective interval prediction method based on wavelet neural network (WNN) for short-term wind speed forecast. This method can generate a set of Pareto optimal solutions which represents a set of prediction models that can directly construct the prediction intervals. An advanced multiobjective evolutionary algorithm, preference inspired co-evolutionary algorithm using goal vectors, is investigated to train the WNN model. Two case studies are carried out with real wind speed data of Victoria and Edmonton in Canada to justify the effectiveness of the proposed method. The numerical results also show the superiority of the proposed forecast approach compared with some benchmark methods.
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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