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Adaptive hybrid deep learning framework for medium-term wind speed forecasting
Fu-Kwun Wang1, Fantahun Tsegaw1, William Gomez1
1Department of Industrial Management, National Taiwan University of Science and Technology, Taipei City 106335, Taiwan.
Iscience
|July 23, 2026
Summary
Accurate wind speed forecasting is crucial for wind farms. This study introduces a novel hybrid framework that effectively models complex wind dynamics for reliable renewable energy integration.
Area of Science:
- Renewable Energy Systems
- Atmospheric Science and Meteorology
- Data Science and Machine Learning
Background:
- Accurate wind speed forecasting is vital for stable wind farm operation.
- Forecasting is challenged by nonlinear, stochastic, and multi-scale spatiotemporal dynamics.
- Existing models struggle with the complexity of wind dynamics.
Purpose of the Study:
- To propose an adaptive hybrid forecasting framework for accurate wind speed prediction.
- To integrate complementary feature extraction with sequence modeling.
- To enhance the reliability and scalability of wind energy integration.
Main Methods:
- Developed an adaptive hybrid forecasting framework.
- Integrated complementary feature extraction techniques.
- Utilized sequence modeling for spatiotemporal dynamics.
Main Results:
- Achieved coefficients of determination above 0.97.
- Reduced root-mean-square error by over 20% compared to benchmark models.
- Demonstrated low maximum error, indicating high accuracy and reliability.
Conclusions:
- The proposed framework effectively models short-term variations and long-term dependencies in wind dynamics.
- The integration of components facilitates accurate, reliable, and scalable wind forecasting.
- Transparency mechanisms enable interpretability, supporting practical application in renewable energy.