Wind Speed Forecasting using ARMA and Boosted Regression Tree Methods: A Case Study
Bibliographic record
Abstract
Accurate wind speed forecasting is essential for power dispatch scheduling and energy commitment of wind farms. As a conventional approach to predict wind speed, the Auto Regressive Moving Average (ARMA) models are only accurate for very short-term/short-term time horizon wind speed forecasts within 0-6 hours. To overcome this issue, in this paper, a machine learning-based approach, known as Boosted Regression Tree (BRT) algorithm, is developed for wind speed forecasting, and is compared with ARMA models at different time horizons. It is found that, as the forecasting time horizons increase, the BRT model outperforms the ARMA model significantly. Historical wind speed data measured from the Meter Station at the Saskatoon International Airport, Saskatoon, Canada in 2022 are used for wind speed forecasting.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".