Wind Speed Forecasting by Conventional Statistical Methods and Machine Learning Techniques
Bibliographic record
Abstract
Intermittency is the main challenge for wind power integrated in power grids. The intermittent nature of wind speed gives rise to fluctuations of output power from a wind turbine that poses serious concerns over power system stability and reliability. Therefore, accurate wind speed forecasting is essential for planning and operation of wind power generation. In this paper, short term wind speed forecasting methods are investigated using one-year historical data. Conventional time series methods (Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA)) and machine learning methods (Artificial Neural Network (ANN) and three Support Vector Machine (SVM) algorithms (Linear SVM, Polynomial SVM and radial basis function (RBF) SVM)) are considered in this study. The forecasted wind speed data are compared with historical wind speed data in terms of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results obtained in this paper show that machine learning methods outperformed conventional time series methods in short-term wind speed prediction.
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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 | 0.003 |
| 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".