A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting

Wind energy is a clean and an abundant renewable energy source. Accurate wind speed forecasting is essential for power dispatch planning, unit commitment decision, maintenance scheduling, and regulation. However, wind is intermittent and wind speed is difficult to predict. This brief proposes a novel wind speed forecasting method by integrating empirical mode decomposition (EMD) and support vector regression (SVR) methods. The EMD is used to decompose the wind speed time series into several intrinsic mode functions (IMFs) and a residue. Subsequently, a vector combining one historical data from each IMF and the residue is generated to train the SVR. The proposed EMD-SVR model is evaluated with a wind speed data set. The proposed EMD-SVR model outperforms several recently reported methods with respect to accuracy or computational complexity.

[1]  Zhong-he Han,et al.  Training Set of Support Vector Regression Extracted by Empirical Mode Decomposition , 2011, 2011 Asia-Pacific Power and Energy Engineering Conference.

[2]  Haiyan Lu,et al.  Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model , 2012 .

[3]  Jing Shi,et al.  Evaluation of hybrid forecasting approaches for wind speed and power generation time series , 2012 .

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Tao Wu,et al.  A Novel Approach for Wind Speed Forecasting Based on EMD and Time-Series Analysis , 2009, 2009 Asia-Pacific Power and Energy Engineering Conference.

[6]  Ran Li,et al.  Short-term wind speed forecasting for wind farm based on empirical mode decomposition , 2008, 2008 International Conference on Electrical Machines and Systems.

[7]  D. Basak,et al.  Support Vector Regression , 2008 .

[8]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[9]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[10]  Liu Peng.  Combined Model Based on EMD-SVM for Short-term Wind Power Prediction , 2011 .

[11]  Wang Xiaolan,et al.  One-Month Ahead Prediction of Wind Speed and Output Power Based on EMD and LSSVM , 2009, 2009 International Conference on Energy and Environment Technology.

[12]  V M F Mendes,et al.  Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Wind Power Forecasting in Portugal , 2011, IEEE Transactions on Sustainable Energy.

[13]  Chao Chen,et al.  A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks , 2012 .

[14]  Bo Zhao,et al.  Short-Term Wind Power Forecasting Using Empirical Mode Decomposition and RBFNN , 2013 .

[15]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

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

[17]  Li Peng,et al.  Study on the multi-step forecasting for wind speed based on EMD , 2009, 2009 International Conference on Sustainable Power Generation and Supply.