Application of support vector machine model in wind power prediction based on particle swarm optimization

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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

Ning Lu ^1, and Ying Liu ^2,

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

Abstract
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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.

Keywords: prediction., particle swarm, Wind power generation, parameter optimization, support vector machine (SVM).

Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C3.

Citation: Ning Lu, Ying Liu. Application of support vector machine model in wind power prediction based on particle swarm optimization. Discrete and 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), 1143-1172. doi: 10.3934/dcdsb.2004.4.1143.
[2]	N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK, 2000.
[3]	J. Kennedy and R. Eberhart, Swarm Intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001.
[4]	J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings., IEEE International Conference on Neural Networks, 1995, Vol. 4, IEEE, 1995, 1942-1948. 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), 62-66.
[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), 25-28.
[7]	L. Qin, F. Z. Peng and I. J. Balaguer, Islanding control of DG in microgrids, in Power Electronics and Motion Control Conference, 2009, 450-455. doi: 10.1109/IPEMC.2009.5157430.
[8]	M. Settles, An Introduction to Particle Swarm Optimization, University of Idaho, Moscow, November 2005, 1-8.
[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 time-mesh refinement in optimal control problems with state constraints, Discrete and Continuous Dynamical Systems, 32 (2015), 4553-4572. doi: 10.3934/dcds.2015.35.4553.
[11]	V. N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
[12]	V. N. Vapnik, The Nature of Statistical Learning Theory, Springer Press, New York, 1995. doi: 10.1007/978-1-4757-2440-0.
[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), 281-287.
[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), 1-4.
[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), 6-12.
[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), 1134-1138.
[17]	J. P. Yang, Short-term Wind Speed and Power Forecasting in Wind Farm Based on ANN Combination Forecasting, Chongqing University, Chongqing, 2012.
[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), 853-858. 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), 1143-1172. doi: 10.3934/dcdsb.2004.4.1143.
[2]	N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK, 2000.
[3]	J. Kennedy and R. Eberhart, Swarm Intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001.
[4]	J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings., IEEE International Conference on Neural Networks, 1995, Vol. 4, IEEE, 1995, 1942-1948. 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), 62-66.
[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), 25-28.
[7]	L. Qin, F. Z. Peng and I. J. Balaguer, Islanding control of DG in microgrids, in Power Electronics and Motion Control Conference, 2009, 450-455. doi: 10.1109/IPEMC.2009.5157430.
[8]	M. Settles, An Introduction to Particle Swarm Optimization, University of Idaho, Moscow, November 2005, 1-8.
[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 time-mesh refinement in optimal control problems with state constraints, Discrete and Continuous Dynamical Systems, 32 (2015), 4553-4572. doi: 10.3934/dcds.2015.35.4553.
[11]	V. N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
[12]	V. N. Vapnik, The Nature of Statistical Learning Theory, Springer Press, New York, 1995. doi: 10.1007/978-1-4757-2440-0.
[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), 281-287.
[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), 1-4.
[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), 6-12.
[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), 1134-1138.
[17]	J. P. Yang, Short-term Wind Speed and Power Forecasting in Wind Farm Based on ANN Combination Forecasting, Chongqing University, Chongqing, 2012.
[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), 853-858. doi: 10.1109/TPWRS.2008.922249.

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