A spatiotemporal wind power prediction based on wavelet decomposition, feature selection, and localized prediction
Bibliographic record
Abstract
Wind power possesses a high level of non-linearity and non-stationarity which are the main barriers to developing an accurate wind power prediction (WPP). In this regard, a multiresolution wavelet decomposition (WD), based on discrete wavelet transform, is employed to decompose the wind power time series (TS) into several components. Afterward, in a feature selection (FS) stage, which benefits from the spatiotemporal relation among the wind farms, the double input symmetrical relevance (DISR) has been adopted to find the most suitable features in predicting each component. Then, to have a high-accuracy prediction with an affordable computation time, localized prediction engines have been used to predict each component. The final WPP value is obtained by superposition of all the predicted values corresponding to components. The proposed spatiotemporal WPP is evaluated using the wind power generation historical data in Saskatchewan, Canada. The performance of the proposed WPP is compared with other well-developed and widely-used WPP models. Various evaluation indices have been utilized for conducting the performance evaluation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".