Prediction of wind power generation based on time series wavelet transform for large Wind Farm

The development of wind generation has rapidly progressed over the last decade, the most important application for wind power prediction is to reduce the need for balancing energy and reserve power, which are needed to integrate wind power into the balancing of supply and demand in the electricity supply system. This paper presents a new method of wind power prediction in short-term with Artificial Neural Network (ANN) prediction model based on wavelet transform of chaotic time series. The data from the wind farm located in the Fujin Wind Farm of China are used for this study. The results reported in this paper show that the new method based on wavelet neural networks has better prediction properties than its similar back-propagation networks for prediction of wind power generation.

[1]  Stéphane Mallat,et al.  Multifrequency channel decompositions of images and wavelet models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[2]  Stéphane Mallat,et al.  Compact image representation from multiscale edges , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[3]  Luís Ferreira Evaluation of short-term wind predictability , 1992 .

[4]  S. Watson,et al.  Application of wind speed forecasting to the integration of wind energy into a large scale power system , 1994 .

[5]  P. S. Dokopoulos,et al.  Wind speed and power forecasting based on spatial correlation models , 1999 .

[6]  Shuhui Li,et al.  Using neural networks to estimate wind turbine power generation , 2001 .

[7]  Georges Kariniotakis,et al.  Wind power forecasting using fuzzy neural networks enhanced with on-line prediction risk assessment , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[8]  Shuhui Li,et al.  Wind power prediction using recurrent multilayer perceptron neural networks , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[9]  Xieping Gao,et al.  Short-term prediction of chaotic time series by wavelet networks , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[10]  J.B. Theocharis,et al.  A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation , 2004, IEEE Transactions on Energy Conversion.

[11]  Din-Chang Tseng,et al.  Wavelet-based medical image compression with adaptive prediction , 2005 .

[12]  Chen Di,et al.  Multi-scale Internet Traffic Prediction Using Wavelet Neural Network Combined Model , 2006 .

[13]  Wang Lijie,et al.  Prediction of Wind Power Generation based on Chaotic Phase Space Reconstruction Models , 2007, 2007 7th International Conference on Power Electronics and Drive Systems.

[14]  T. Lobos,et al.  Prony and nonlinear regression methods used for determination of transient parameters in wind energy conversion system , 2007, 2007 IEEE Lausanne Power Tech.