Very Short-Term Wind Speed Prediction Techniques Using Machine Learning
Bibliographic record
Abstract
Wind speed prediction plays an essential role in planning and operation of wind power systems. An accurate wind speed prediction can reduce costs and enhance the proper use of resources. Wind speed series have high nonlinearity and volatility. In this paper, data-driven models using machine learning (ML) algorithms have been developed to predict a very short-term wind speed. Historical wind speeds lagging up to 20 minutes with 1 minute time interval are used to predict the current and future (up to 5 minutes with L-minnte interval) wind speed. A performance comparative analysis of four ML algorithms including Multiple-Layer Perception Regressor (MLPR), Random Forest Regressor (RFR), K-nearest Neighbors Regressor (KNNR), and Decision Tree Regressor (DTR), is conducted, and their accuracy is evaluated by their R2 values, mean absolute error (MAE), standard deviation (SD) of MAE, mean absolute percentage error (MAPE), SD of MAPE and root mean square error (RMSE). It is found that MLPR gives the best prediction accuracy of 95.3%.
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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".