Wind power prediction using wavelet transform and chaotic characteristics

In the electricity system, supply and demand must be equal at all times. Wind power generation is fluctuating due to the variation of wind. As more and more wind power generation is integrated into the power system, it is very important to predict the wind power production to contribute the system reserve reduction and the operational costs of the power plants. This paper brings wavelet transform into the time series of wind power and verifies that the decomposed series all have chaotic characteristic, so a new method of wind power prediction in short-term with Artificial Neural Network (ANN) model based on wavelet transform is presented. To test the approach, the wind power data from the Fujin wind farm and Saihanba wind farm of China are used for this study. The prediction results are presented and compared to the no wavelet transform method and ARMA method. The results show that the new method based on wavelet transform neural networks will be a useful tool in wind power prediction.