Wind power generation forecasting system based on multi-model intelligent fusion strategy and probabilistic...

Area of Science:

• Renewable Energy Systems
• Power Systems Engineering
• Computational Intelligence

Background:

• Growing reliance on fossil fuels necessitates clean energy solutions.
• Wind energy is a key renewable source, but its integration faces challenges due to intermittency.
• Accurate wind power prediction is vital for stable grid operation and turbine scheduling.

Purpose of the Study:

• To develop an integrated wind power forecasting system for improved accuracy and uncertainty quantification.
• To address the challenges of large-scale wind power grid integration and stable power system operation.
• To provide deterministic predictions and uncertainty analyses for 24, 48, and 72-hour ahead wind power.

Main Methods:

• Adaptive decomposition reconstruction combined with fuzzy theory for data preprocessing.
• Integration of optimization algorithms for parameter fine-tuning and structure optimization.
• Quantile regression and kernel density estimation for constructing the forecasting system.

Main Results:

• Significant reduction in noise and fluctuations in experimental data.
• Improved forecast accuracy compared to traditional single models.
• Successful quantification of wind forecast uncertainty.

Conclusions:

• The proposed integrated system enhances wind power forecasting accuracy and stability.
• The system effectively quantifies prediction uncertainty, aiding grid management.
• This approach supports the reliable integration of wind energy into power systems.