An Effective Very Short-Term Wind Speed Prediction Approach Using Multiple Regression Models
Bibliographic record
Abstract
As one of the dominant forms of renewable energy sources, wind power generation plays an increasingly important role in modern energy landscape. A very short-term wind speed prediction is essential for monitoring and control of power systems with high wind power penetration to improve system stability and reliability. In this article, an accurate five-minute horizon wind speed prediction method is proposed by integrating and comparing four machine learning regression algorithms, including multiple-layer perception regressor (MLPR), random forest regressor (RFR), K-nearest neighbors regressor (KNNR), and decision tree regressor (DTR). Twenty minutes historical data of wind speed in a one-minute interval is used for wind speed predictions, which are actual wind speed data provided by the National Renewable Energy Laboratory (NREL), Golden, CO, USA. The proposed method is intended to offer an effective and low-cost way for very short-term wind speed prediction. Pearson's correlation coefficient (PCC) is adopted for feature selection. The four algorithms are evaluated through statistic error indices and Bland-Altman method, and the MLPR algorithm shows the best performance.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".