Data-Driven Wind Speed Forecasting Techniques Using Hybrid Neural Network Methods
Bibliographic record
Abstract
Wind power generation is a dominant form of renewable energy sources with significant technical progress over the past decades. One of the major challenges in wind power generation is the intermittent nature of wind speed. In this paper, wind speed prediction techniques are investigated using wind speed measurement data in Saskatchewan, Canada. Despite excellent wind power potential in Saskatchewan, currently, only 6.5 % of total electricity demand is supplied by wind power in this province. In this paper, three hybrid Neural Network methods (Wavelet Neural Network (WNN) trained by Improved Clonal Selection Algorithm (ICSA); WNN trained by Particle Swarm Optimization (PSO); and Extreme Learning Machine (ELM)-based Neural Network) are implemented and compared for wind speed forecasting using actual recorded wind speed data of Saskatchewan, which paves the way for economical operation, planning, and optimization of the current and future wind farms in Saskatchewan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".