A Hybrid Wind Power Forecasting System Integrating Dynamic Model Selection and Data Augmentation
Bibliographic record
Abstract
Accurate wind power forecasting (WPF) is critical for integrating renewable energy into power grids; yet challenges persist due to the stochastic nature of weather variability. In recent years, machine learning methods have shown superior performance in WPF. This paper proposes a novel hybrid forecasting system that combines dynamic model selection (Wind-DS) with data augmentation techniques to enhance forecasting accuracy. The Wind-DS framework dynamically selects optimal models from a pool of candidates based on similarity metrics and competence regions, while leveraging three categories of data augmentation—time-domain, frequency-domain, and large language model (LLM)-based methods. These methods are systematically evaluated to demonstrate their effectiveness and robustness. Experimental results on multiple wind power datasets demonstrate that the proposed system achieves superior performance, with time-domain augmentation (e.g., noise injection) yielding the most significant improvements. The hybrid approach reduces reliance on costly energy storage systems and addresses the limitations of single-model forecasting, offering a scalable solution to grid stability.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".