Advanced and Optimized Forecasting Techniques for Wind Power Generation: A Comparative Analysis
Bibliographic record
Abstract
Wind power forecasting and subsequently ramp event detection are vital for maintaining the stability and efficiency of modern power grids that increasingly rely on renewable energy sources. This study presents a new hybrid forecasting framework that combines Long Short-Term Memory (LSTM) networks with Extreme Gradient Boosting (XGBoost) to address the limitations of each model individually. While LSTMs are effective at capturing temporal dependencies, they tend to perform poorly in detecting rapid changes such as ramp events. Conversely, XGBoost provides accurate point predictions but lacks inherent temporal awareness. By merging these models, our framework leverages LSTM’s ability to model time sequences and XGBoost’s strength in predicting sharp transitions. This integration markedly improves short-term forecasting accuracy, especially during dynamic ramp conditions. The framework focuses on short-term forecasting with a 12 -hour horizon using 24 hours of historical and forecasted exogenous data. Tested on a four-year meteorological dataset from Atlantic Canada, our results show that this integrated approach, leveraging the complementary strengths of hybrid deep learning (Dual Transformer-LSTM) and tree-based models (XGBoost), achieve a $30 \%$ relative improvement in deterministic and a $40 \%$ improvement in deterministic accuracy when capturing both general and ramp-specific features of wind power signals compared to the other two methods.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".