Optimization scheme of wind energy prediction based on artificial intelligence.

Area of Science:

• Renewable Energy Systems
• Computational Intelligence
• Time Series Forecasting

Background:

• Wind energy is a vital renewable source, but its inherent volatility challenges grid integration.
• Accurate wind speed prediction is essential for ensuring the stability and reliability of wind power generation.

Purpose of the Study:

• To develop a novel, robust wind speed prediction scheme.
• To enhance the accuracy and reliability of wind power forecasting models.
• To facilitate better grid integration and management of wind energy.

Main Methods:

• Hybrid mode decomposition (variational mode decomposition and wavelet analysis) for data decomposition.
• Optimized long and short-term memory (LSTM) neural networks and autoregressive moving average (ARMA) models for sequence training.
• A combined forecasting approach integrating decomposed components for final prediction.

Main Results:

• Improved mode decomposition effectively extracts wind speed data characteristics.
• Hybrid prediction models leverage component-specific algorithms for superior forecasting.
• Optimized neural networks overcome parameter setting challenges, improving model performance.
• The proposed combined model demonstrates accurate prediction results on real-world wind farm data.

Conclusions:

• The developed hybrid forecasting model offers a structured and accurate approach to wind speed prediction.
• This research contributes to advancing wind energy prediction methodologies and supporting green energy infrastructure.
• Accurate forecasting aids in formulating effective wind power regulation strategies and promoting renewable energy adoption.