Ensemble of artifical neural networks for seasonal forecasting of wind speed in eastern Canada
Bibliographic record
Abstract
Efficient harnessing of wind energy resources, including advancements in weather and seasonal forecasting and climate projections, is imperative for the sustainable progress of wind power generation. While temperature and precipitation data receive considerable attention in interannual variability and seasonal forecasting studies, there is a notable gap in exploring correlations between climate indices and wind speeds. This paper proposes the use of an ensemble of artificial neural networks to forecast wind speeds based on climate oscillation indices and assesses its performance. An initial examination indicates a correlation signal between climate indices and wind speeds of ERA5 for the selected case study in eastern Canada. Forecasts are made for the season April-May-June and based on most correlated climate indices of preceding seasons. \nA pointwise forecast is conducted with a 20 member ensemble, which is verified by leave-on-out cross-validation. The results obtained are analyzed in terms of root mean squared error, bias, and skill score and show a competitive performance with state-of-the-art numerical wind predictions from SEAS5, outperforming them in several regions. A relatively basic model with a single unit in the hidden layer and regularization rate of 10−2 provides promising results, especially in areas with a higher number of indices considered. This study adds to global efforts to enable more accurate forecasting by introducing a novel approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".