Enhancing Wind Power Forecasting Accuracy in Canada Using a Solar Data-Enhanced Hybrid Machine Learning Model: Integrating ANN, LSTM, and SVR
Bibliographic record
Abstract
As the world moves toward sustainable energy solutions, wind power emerges as a pivotal renewable energy (RE) source due to its accessibility and zero carbon emission. However, its unpredictable nature poses significant forecasting challenges, that impact energy management efficiency. This study tackles this vital challenge by integrating solar power data into advanced machine learning models to enhance the forecast accuracy and quantifying uncertainty of wind power. The MERRA-2 dataset – a comprehensive atmospheric reanalysis from NASA – spanning 2017 to 2019 across three locations of Canadian provinces, British Colombia, Manitoba, and Nova Scotia has been considered for this work. A novel hybrid machine learning framework that combines the strengths of Artificial Neural Networks (ANN), Long Short-Term Memory Networks (LSTM), and Support Vector Machines (SVM), has been used. Utilizing this framework excels in pattern recognition, temporal data processing, and regression analysis, effectively will improve the precision of wind power forecasts. Besides, it provides a robust framework for quantifying forecast uncertainty and enhancing decision-making in renewable energy management. The superiority of this model is demonstrated through comparative evaluations against conventional methods using various metrics to establish its efficacy and applicability in real-world scenarios.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".