Short-term wind speed forecasting for wind farm based on empirical mode decomposition

As an important renewable energy form, wind power obtains rapid development recently. More advanced accurate and reliable techniques for wind speed forecasting are required. It can reduce the disadvantageous impact to the power system. According to the outstanding feature of EMD algorithm, this paper presents a new technique for wind speed forecasting based on Empirical Mode Decomposition (EMD) and ARMA. EMD is a new method for analyzing nonlinear and non-stationary signal. It is an adaptive wavelet decomposition strategy. We make full use of the characteristic of the EMD and the ARMA in the EMD-ARMA model. Actual wind speed data are used to test the approach. It concludes that the EMD-ARMA model is an effective method in wind speed forecasting.

[1]  E.F. El-Saadany,et al.  One day ahead prediction of wind speed using annual trends , 2006, 2006 IEEE Power Engineering Society General Meeting.

[2]  J.S. Smith,et al.  Empirical Mode Decomposition For Power Quality Monitoring , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[3]  N. Huang,et al.  On certain theoretical developments underlying the Hilbert-Huang transform , 2006, 2006 IEEE Aerospace Conference.

[4]  Li Nan Apply Empirical Mode Decomposition Based Hilbert Transform to Power System Transient Signal Analysis , 2005 .

[5]  Qiang Gai,et al.  Research on Properties of Empirical Mode Decomposition Method , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[6]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[7]  Xiao Yang,et al.  WIND SPEED AND GENERATED POWER FORECASTING IN WIND FARM , 2005 .

[8]  George Stavrakakis,et al.  Wind power forecasting using advanced neural networks models , 1996 .

[9]  Y Ji Short-term load forecasting based on empirical mode decomposition and least square support vector machine , 2007 .

[10]  Zhang Li Wind speed forecast model for wind farms based on time series analysis , 2005 .

[11]  Bai Jie,et al.  Analysis on operation value of wind power resources , 2010, 2010 International Conference On Computer Design and Applications.

[12]  Chen Shou-sun Analysis on Operation Value of Wind Power Resources , 2006 .

[13]  Yang Lihua Discussion and Improvement on Empirical Mode Decomposition Algorithm , 2007 .

[14]  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.

[15]  P. Dokopoulos,et al.  A fuzzy expert system for the forecasting of wind speed and power generation in wind farms , 2001, PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications (Cat. No.01CH37195).