EMD-based gray combined forecasting model - Application to long-term forecasting of wind power generation

Area of Science:

• Renewable Energy Systems
• Climate Science
• Data Analytics

Background:

• Wind power is a key renewable energy source with significant carbon reduction potential.
• Accurate long-term wind power generation forecasts are crucial for energy policy and grid management.
• China's wind power data presents challenges due to monsoon climate effects, nonlinear cycles, and small volumes.

Purpose of the Study:

• To develop and apply a novel prediction model for long-term wind power generation in China.
• To provide data-driven policy recommendations for advancing China's wind power industry.

Main Methods:

• Development of a new, specialized prediction model.
• Application of the model to long-term wind power generation data from China.
• Analysis of prediction results to inform policy.

Main Results:

• The developed model demonstrates efficacy in predicting long-term wind power generation.
• The study identifies specific policy recommendations based on the forecasting outcomes.
• The findings aim to support the growth of China's wind power sector.

Conclusions:

• The new prediction model effectively addresses the complexities of forecasting wind power in China.
• Targeted policy recommendations can foster the sustainable development of wind energy.
• Accurate forecasting is essential for optimizing renewable energy integration and policy formulation.