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Application of support vector machine model in wind power prediction based on particle swarm optimization
1.  School of Automation, Wuhan University of Technology, Wuhan, China 
2.  Wuhan Electric Power Dispatching and Communication Center, Wuhan, China 
References:
[1] 
W. Cheng, Convergence analysis of the numerical method for the primitive equations formulated in mean vorticity on a Cartesian grid,, Discrete and Continuous Dynamical Systems  Series B, 4 (2004), 1143. doi: 10.3934/dcdsb.2004.4.1143. 
[2] 
N. Cristianini and J. ShaweTaylor, An Introduction to Support Vector Machines,, Cambridge University Press, (2000). 
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J. Kennedy and R. Eberhart, Swarm Intelligence,, Morgan Kaufmann Publishers Inc., (2001). 
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J. Kennedy and R. Eberhart, Particle swarm optimization,, in Proceedings., (1995), 1942. doi: 10.1109/ICNN.1995.488968. 
[5] 
Y. Liu, X. F. Lu and R. M. Fang, et al., A review on wind speed forecast methods in wind power system,, Power System and Clean Energy, 26 (2010), 62. 
[6] 
S. W. Qi, W. Q. Wang and X. Y. Zhang, Model building for wind speed and wind power prediction based on SVM,, Renewable Energy Resources, 28 (2010), 25. 
[7] 
L. Qin, F. Z. Peng and I. J. Balaguer, Islanding control of DG in microgrids,, in Power Electronics and Motion Control Conference, (2009), 450. doi: 10.1109/IPEMC.2009.5157430. 
[8] 
M. Settles, An Introduction to Particle Swarm Optimization,, University of Idaho, (2005), 1. 
[9] 
M. Simoes, Intelligent Based Hierarchical Control Power Electronics for Distributed Generation Systems,, Power Engineering Society General Meeting, (2006). doi: 10.1109/PES.2006.1709628. 
[10] 
P. Luís Tiago and A. C. C. F. Fernando, Adaptive timemesh refinement in optimal control problems with state constraints,, Discrete and Continuous Dynamical Systems, 32 (2015), 4553. doi: 10.3934/dcds.2015.35.4553. 
[11]  
[12] 
V. N. Vapnik, The Nature of Statistical Learning Theory,, Springer Press, (1995). doi: 10.1007/9781475724400. 
[13] 
V. N. Vapnik, S. E. Golowich and A. J. Smola, Support vectormachine for function approximation, regression estimation and signal procession,, Neural Information Procession System, 9 (1996), 281. 
[14] 
C. S. Wang and S. X. Wang, Study on some key problems related to distributed generation systems,, Automation of Electric Power Systems, 32 (2008), 1. 
[15] 
S. Wang, J. P. Yang and F. B. Li, et al., Shortterm wind speed forecasting based on EMD and ANN,, Power System Protection and Control, 40 (2012), 6. 
[16] 
G. Q. Wang, S. Wang and H. Y. Liu, et al., Research of shortterm wind speed prediction method,, Renewable Energy Resources, 32 (2014), 1134. 
[17] 
J. P. Yang, Shortterm Wind Speed and Power Forecasting in Wind Farm Based on ANN Combination Forecasting,, Chongqing University, (2012). 
[18] 
Y. Zhang, Q, Zhou, C. X. Sun, S. L. Lei, Y. M. Liu and Y. Song, RBF neural network and anfisbased shortterm load forecasting approach in realtime price environment,, IEEE Transaction on Power Systems, 23 (2008), 853. doi: 10.1109/TPWRS.2008.922249. 
show all references
References:
[1] 
W. Cheng, Convergence analysis of the numerical method for the primitive equations formulated in mean vorticity on a Cartesian grid,, Discrete and Continuous Dynamical Systems  Series B, 4 (2004), 1143. doi: 10.3934/dcdsb.2004.4.1143. 
[2] 
N. Cristianini and J. ShaweTaylor, An Introduction to Support Vector Machines,, Cambridge University Press, (2000). 
[3] 
J. Kennedy and R. Eberhart, Swarm Intelligence,, Morgan Kaufmann Publishers Inc., (2001). 
[4] 
J. Kennedy and R. Eberhart, Particle swarm optimization,, in Proceedings., (1995), 1942. doi: 10.1109/ICNN.1995.488968. 
[5] 
Y. Liu, X. F. Lu and R. M. Fang, et al., A review on wind speed forecast methods in wind power system,, Power System and Clean Energy, 26 (2010), 62. 
[6] 
S. W. Qi, W. Q. Wang and X. Y. Zhang, Model building for wind speed and wind power prediction based on SVM,, Renewable Energy Resources, 28 (2010), 25. 
[7] 
L. Qin, F. Z. Peng and I. J. Balaguer, Islanding control of DG in microgrids,, in Power Electronics and Motion Control Conference, (2009), 450. doi: 10.1109/IPEMC.2009.5157430. 
[8] 
M. Settles, An Introduction to Particle Swarm Optimization,, University of Idaho, (2005), 1. 
[9] 
M. Simoes, Intelligent Based Hierarchical Control Power Electronics for Distributed Generation Systems,, Power Engineering Society General Meeting, (2006). doi: 10.1109/PES.2006.1709628. 
[10] 
P. Luís Tiago and A. C. C. F. Fernando, Adaptive timemesh refinement in optimal control problems with state constraints,, Discrete and Continuous Dynamical Systems, 32 (2015), 4553. doi: 10.3934/dcds.2015.35.4553. 
[11]  
[12] 
V. N. Vapnik, The Nature of Statistical Learning Theory,, Springer Press, (1995). doi: 10.1007/9781475724400. 
[13] 
V. N. Vapnik, S. E. Golowich and A. J. Smola, Support vectormachine for function approximation, regression estimation and signal procession,, Neural Information Procession System, 9 (1996), 281. 
[14] 
C. S. Wang and S. X. Wang, Study on some key problems related to distributed generation systems,, Automation of Electric Power Systems, 32 (2008), 1. 
[15] 
S. Wang, J. P. Yang and F. B. Li, et al., Shortterm wind speed forecasting based on EMD and ANN,, Power System Protection and Control, 40 (2012), 6. 
[16] 
G. Q. Wang, S. Wang and H. Y. Liu, et al., Research of shortterm wind speed prediction method,, Renewable Energy Resources, 32 (2014), 1134. 
[17] 
J. P. Yang, Shortterm Wind Speed and Power Forecasting in Wind Farm Based on ANN Combination Forecasting,, Chongqing University, (2012). 
[18] 
Y. Zhang, Q, Zhou, C. X. Sun, S. L. Lei, Y. M. Liu and Y. Song, RBF neural network and anfisbased shortterm load forecasting approach in realtime price environment,, IEEE Transaction on Power Systems, 23 (2008), 853. doi: 10.1109/TPWRS.2008.922249. 
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