Priori-guided and data-driven hybrid model for wind power forecasting.

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence in Energy
• Wind Power Engineering

Background:

• Wind power generation is inherently uncertain and random, posing challenges for grid stability and optimization.
• Existing forecasting methods often struggle to fully leverage both historical data and physical principles.
• Accurate wind power forecasting is crucial for efficient grid management and integration of renewable energy sources.

Purpose of the Study:

• To develop a robust and accurate hybrid wind power forecasting method.
• To integrate prior knowledge with data-driven techniques for improved prediction.
• To enhance the reliability and interpretability of wind power forecasts.

Main Methods:

• A three-stage hierarchical framework combining Fuzzy C-Means (FCM) clustering, fuzzy inference, and dimension reduction of Numerical Weather Prediction (NWP).
• Utilized accessible wind power generation patterns to guide the mining of actual power curves.
• Employed a data-driven model incorporating a gateway for priori knowledge to steer iterative learning, enabling adaptive learning and Volterra polynomial representation.

Main Results:

• The proposed hybrid method demonstrated robust, accurate, and interpretable wind power forecasting.
• Ablation analyses and comparative experiments confirmed the performance improvements.
• The integration of domain knowledge was shown to significantly enhance forecasting accuracy.

Conclusions:

• The developed hybrid forecasting framework effectively addresses the uncertainty of wind power.
• Incorporating priori knowledge into data-driven models offers a significant advantage in wind power prediction.
• The method provides a reliable tool for grid operators to optimize advance preparation for wind energy integration.