A hybrid wavelet transform based short-term wind speed forecasting approach.

Area of Science:

• Renewable Energy Systems
• Computational Intelligence
• Meteorological Forecasting

Background:

• Effective wind speed forecasting is essential for optimizing wind power utilization and managing wind parks.
• Existing forecasting methods often struggle with the complex, non-linear patterns inherent in wind speed data.

Purpose of the Study:

• To propose a novel hybrid approach, Wavelet Transform Technique-Two-Hidden-Layer Neural Network (WTT-TNN), for enhanced wind speed forecasting.
• To improve the accuracy and reliability of wind speed predictions for practical applications.

Main Methods:

• Decomposition of wind speed data into approximate and detailed scales using Wavelet Transform Technique (WTT).
• Prediction of decomposed scales using a Two-Hidden-Layer Neural Network (TNN).
• Optimization of TNN architecture using partial autocorrelation function and experimental simulation.

Main Results:

• The WTT-TNN approach successfully decomposed wind speed into different patterns.
• The hybrid model demonstrated increased accuracy in wind speed forecasting compared to baseline methods.
• Application to Hexi Corridor wind speed data validated the proposed method's effectiveness.

Conclusions:

• The WTT-TNN hybrid approach offers a significant improvement in wind speed forecasting accuracy.
• This method enhances the potential for efficient wind park management and wind power utilization.
• The decomposition and neural network prediction strategy effectively captures complex wind speed dynamics.