December  2015, 8(6): 1267-1276. doi: 10.3934/dcdss.2015.8.1267

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

Received  June 2015 Revised  September 2015 Published  December 2015

Wind energy is a kind of renewable and clean energy, and wind power is a non-hydropower renewable energy which has the best technical and economic conditions for large-scale development. It is characterized by fluctuation, intermittency, low energy density, etc., so wind power is also fluctuating. When a large-scale wind farm is connected to a power grid, great fluctuation in wind power will cause adverse effect to the power balance and frequency adjustment of the power grid. If the generation power of the wind farm can be prediction, the electricity dispatch department can arrange dispatch plans in advance according to the change in wind power and better protect the power balance and operation safety of the power grid. In this article, a SVM model is used to predict wind power and modified PSO is used to optimize SVM parameters, realizing the optimized selection of the SVM model parameters, which makes such prediction more close to actual law. Actual calculation examples shows that the prediction method used in the article has good convergence, high prediction precision and actual application value.
Citation: Ning Lu, Ying Liu. Application of support vector machine model in wind power prediction based on particle swarm optimization. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1267-1276. doi: 10.3934/dcdss.2015.8.1267
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.  Google Scholar

[2]

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines,, Cambridge University Press, (2000).   Google Scholar

[3]

J. Kennedy and R. Eberhart, Swarm Intelligence,, Morgan Kaufmann Publishers Inc., (2001).   Google Scholar

[4]

J. Kennedy and R. Eberhart, Particle swarm optimization,, in Proceedings., (1995), 1942.  doi: 10.1109/ICNN.1995.488968.  Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[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.  Google Scholar

[8]

M. Settles, An Introduction to Particle Swarm Optimization,, University of Idaho, (2005), 1.   Google Scholar

[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.  Google Scholar

[10]

P. Luís Tiago and A. C. C. F. Fernando, Adaptive time-mesh refinement in optimal control problems with state constraints,, Discrete and Continuous Dynamical Systems, 32 (2015), 4553.  doi: 10.3934/dcds.2015.35.4553.  Google Scholar

[11]

V. N. Vapnik, Statistical Learning Theory,, Wiley, (1998).   Google Scholar

[12]

V. N. Vapnik, The Nature of Statistical Learning Theory,, Springer Press, (1995).  doi: 10.1007/978-1-4757-2440-0.  Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[15]

S. Wang, J. P. Yang and F. B. Li, et al., Short-term wind speed forecasting based on EMD and ANN,, Power System Protection and Control, 40 (2012), 6.   Google Scholar

[16]

G. Q. Wang, S. Wang and H. Y. Liu, et al., Research of short-term wind speed prediction method,, Renewable Energy Resources, 32 (2014), 1134.   Google Scholar

[17]

J. P. Yang, Short-term Wind Speed and Power Forecasting in Wind Farm Based on ANN Combination Forecasting,, Chongqing University, (2012).   Google Scholar

[18]

Y. Zhang, Q, Zhou, C. X. Sun, S. L. Lei, Y. M. Liu and Y. Song, RBF neural network and anfis-based short-term load forecasting approach in real-time price environment,, IEEE Transaction on Power Systems, 23 (2008), 853.  doi: 10.1109/TPWRS.2008.922249.  Google Scholar

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.  Google Scholar

[2]

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines,, Cambridge University Press, (2000).   Google Scholar

[3]

J. Kennedy and R. Eberhart, Swarm Intelligence,, Morgan Kaufmann Publishers Inc., (2001).   Google Scholar

[4]

J. Kennedy and R. Eberhart, Particle swarm optimization,, in Proceedings., (1995), 1942.  doi: 10.1109/ICNN.1995.488968.  Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[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.  Google Scholar

[8]

M. Settles, An Introduction to Particle Swarm Optimization,, University of Idaho, (2005), 1.   Google Scholar

[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.  Google Scholar

[10]

P. Luís Tiago and A. C. C. F. Fernando, Adaptive time-mesh refinement in optimal control problems with state constraints,, Discrete and Continuous Dynamical Systems, 32 (2015), 4553.  doi: 10.3934/dcds.2015.35.4553.  Google Scholar

[11]

V. N. Vapnik, Statistical Learning Theory,, Wiley, (1998).   Google Scholar

[12]

V. N. Vapnik, The Nature of Statistical Learning Theory,, Springer Press, (1995).  doi: 10.1007/978-1-4757-2440-0.  Google Scholar

[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.   Google Scholar

[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.   Google Scholar

[15]

S. Wang, J. P. Yang and F. B. Li, et al., Short-term wind speed forecasting based on EMD and ANN,, Power System Protection and Control, 40 (2012), 6.   Google Scholar

[16]

G. Q. Wang, S. Wang and H. Y. Liu, et al., Research of short-term wind speed prediction method,, Renewable Energy Resources, 32 (2014), 1134.   Google Scholar

[17]

J. P. Yang, Short-term Wind Speed and Power Forecasting in Wind Farm Based on ANN Combination Forecasting,, Chongqing University, (2012).   Google Scholar

[18]

Y. Zhang, Q, Zhou, C. X. Sun, S. L. Lei, Y. M. Liu and Y. Song, RBF neural network and anfis-based short-term load forecasting approach in real-time price environment,, IEEE Transaction on Power Systems, 23 (2008), 853.  doi: 10.1109/TPWRS.2008.922249.  Google Scholar

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