Multi-scale wind speed prediction model based on improved escape algorithm for optimizing time-varying filtering empirical modal decomposition
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an Improved Escape Algorithm (IESC) to enhance wind power forecasting by optimizing Time-Varying Filter Empirical Mode Decomposition (TVF-EMD). The novel approach improves grid stability and dispatch reliability through accurate, adaptive multi-frequency wind signal analysis.
Area Of Science
- Renewable Energy Systems
- Computational Intelligence
- Signal Processing
Background
- Wind energy's inherent stochasticity and volatility pose significant challenges to grid stability and reliable power dispatch.
- Existing decomposition-based forecasting methods struggle with mode mixing and non-stationary signal separation, limiting accuracy.
Purpose Of The Study
- To develop an advanced forecasting method for wind energy that enhances grid stability and dispatch reliability.
- To improve the accuracy and robustness of wind power forecasting, particularly for multi-step predictions.
Main Methods
- Proposed an Improved Escape Algorithm (IESC) with chaotic mapping to optimize Time-Varying Filter Empirical Mode Decomposition (TVF-EMD) hyperparameters, mitigating mode mixing.
- Implemented a frequency-adaptive hybrid model utilizing XLSTM for high-frequency, LSTM for medium-frequency, and ELM for low-frequency wind signal components.
- Evaluated the IESC-optimized TVF-EMD and the hybrid forecasting model on a large-scale wind energy dataset.
Main Results
- The IESC algorithm demonstrated superior performance over standard ESC, GWO, and DE optimization methods.
- The hybrid model achieved a 29.8% lower 1-step Mean Absolute Error (MAE) and a 65.6% higher 15-step R² compared to XLSTM alone.
- Error growth over 15 steps was limited to 12%, with R² degradation 35% slower, indicating significant improvements in long-term accuracy and stability.
Conclusions
- The proposed IESC-optimized TVF-EMD and hybrid forecasting model significantly enhance wind power forecasting precision.
- The method effectively bridges multi-step forecasting accuracy with real-time dispatch requirements, ensuring better grid-demand matching.
- This approach contributes to improved operational stability in power grids integrating variable wind energy sources.
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