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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".