Ultra-Short-Term Offshore Wind Power Prediction Based on PCA-SSA-VMD and BiLSTM.

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence in Energy
• Time Series Forecasting

Background:

• Offshore wind power forecasting faces challenges due to data randomness and temporal correlations.
• Accurate forecasting is crucial for economic dispatch and grid stability of offshore wind farms.

Purpose of the Study:

• To develop a novel hybrid model for improving the accuracy of offshore wind power prediction.
• To address the limitations of existing forecasting methods in handling complex wind power data.

Main Methods:

• Dimensionality reduction using Principal Component Analysis (PCA).
• Adaptive decomposition of wind power data via SSA-optimized Variational Modal Decomposition (VMD).
• Hyperparameter optimization of Bidirectional Long- and Short-Term Memory (BiLSTM) networks using the SSA algorithm.

Main Results:

• The proposed hybrid model demonstrated significant improvements in prediction accuracy through simulation experiments.
• The integration of PCA, SSA, VMD, and BiLSTM effectively handled data noise and complex temporal patterns.
• The model's effectiveness in offshore wind power forecasting was validated.

Conclusions:

• The developed PCA-SSA-VMD-BiLSTM model offers a robust solution for accurate offshore wind power forecasting.
• This approach enhances the reliability and economic dispatch capabilities of offshore wind farms.
• The study confirms the synergistic benefits of combining dimensionality reduction, adaptive decomposition, and optimized deep learning for wind energy prediction.