Advanced renewable energy forecasting under uncertainty using empirical mode decomposition and machine learning for resilient microgrid optimization

Key Points

• Forecasting methods reduce uncertainty in renewable energy output, improving grid management.
• Empirical mode decomposition combined with machine learning achieved a 20% increase in prediction accuracy.
• Analysis across various microgrid settings demonstrated the effectiveness of the proposed approach.
• This method supports resilient energy systems by optimizing resource allocation and reducing costs.