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 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.

[1]

Omar Saber Qasim, Ahmed Entesar, Waleed Al-Hayani. Solving nonlinear differential equations using hybrid method between Lyapunov's artificial small parameter and continuous particle swarm optimization. Numerical Algebra, Control and Optimization, 2021, 11 (4) : 633-644. doi: 10.3934/naco.2021001

[2]

Yubo Yuan, Weiguo Fan, Dongmei Pu. Spline function smooth support vector machine for classification. Journal of Industrial and Management Optimization, 2007, 3 (3) : 529-542. doi: 10.3934/jimo.2007.3.529

[3]

Yubo Yuan. Canonical duality solution for alternating support vector machine. Journal of Industrial and Management Optimization, 2012, 8 (3) : 611-621. doi: 10.3934/jimo.2012.8.611

[4]

K. Schittkowski. Optimal parameter selection in support vector machines. Journal of Industrial and Management Optimization, 2005, 1 (4) : 465-476. doi: 10.3934/jimo.2005.1.465

[5]

Hong-Gunn Chew, Cheng-Chew Lim. On regularisation parameter transformation of support vector machines. Journal of Industrial and Management Optimization, 2009, 5 (2) : 403-415. doi: 10.3934/jimo.2009.5.403

[6]

Miao Yu. A solution of TSP based on the ant colony algorithm improved by particle swarm optimization. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 979-987. doi: 10.3934/dcdss.2019066

[7]

Junyuan Lin, Timothy A. Lucas. A particle swarm optimization model of emergency airplane evacuations with emotion. Networks and Heterogeneous Media, 2015, 10 (3) : 631-646. doi: 10.3934/nhm.2015.10.631

[8]

Pooja Louhan, S. K. Suneja. On fractional vector optimization over cones with support functions. Journal of Industrial and Management Optimization, 2017, 13 (2) : 549-572. doi: 10.3934/jimo.2016031

[9]

Ying Lin, Qi Ye. Support vector machine classifiers by non-Euclidean margins. Mathematical Foundations of Computing, 2020, 3 (4) : 279-300. doi: 10.3934/mfc.2020018

[10]

Jian Luo, Shu-Cherng Fang, Yanqin Bai, Zhibin Deng. Fuzzy quadratic surface support vector machine based on fisher discriminant analysis. Journal of Industrial and Management Optimization, 2016, 12 (1) : 357-373. doi: 10.3934/jimo.2016.12.357

[11]

Xin Li, Ziguan Cui, Linhui Sun, Guanming Lu, Debnath Narayan. Research on iterative repair algorithm of Hyperchaotic image based on support vector machine. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1199-1218. doi: 10.3934/dcdss.2019083

[12]

Fatemeh Bazikar, Saeed Ketabchi, Hossein Moosaei. Smooth augmented Lagrangian method for twin bounded support vector machine. Numerical Algebra, Control and Optimization, 2021  doi: 10.3934/naco.2021027

[13]

Xin Yan, Hongmiao Zhu. A kernel-free fuzzy support vector machine with Universum. Journal of Industrial and Management Optimization, 2021  doi: 10.3934/jimo.2021184

[14]

Ahmad Mousavi, Zheming Gao, Lanshan Han, Alvin Lim. Quadratic surface support vector machine with L1 norm regularization. Journal of Industrial and Management Optimization, 2022, 18 (3) : 1835-1861. doi: 10.3934/jimo.2021046

[15]

Chuong Van Nguyen, Phuong Huu Hoang, Hyo-Sung Ahn. Distributed optimization algorithms for game of power generation in smart grid. Numerical Algebra, Control and Optimization, 2019, 9 (3) : 327-348. doi: 10.3934/naco.2019022

[16]

Qifeng Cheng, Xue Han, Tingting Zhao, V S Sarma Yadavalli. Improved particle swarm optimization and neighborhood field optimization by introducing the re-sampling step of particle filter. Journal of Industrial and Management Optimization, 2019, 15 (1) : 177-198. doi: 10.3934/jimo.2018038

[17]

Min Zhang, Gang Li. Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1413-1426. doi: 10.3934/dcdss.2019097

[18]

Mohamed A. Tawhid, Kevin B. Dsouza. Hybrid binary dragonfly enhanced particle swarm optimization algorithm for solving feature selection problems. Mathematical Foundations of Computing, 2018, 1 (2) : 181-200. doi: 10.3934/mfc.2018009

[19]

Xia Zhao, Jianping Dou. Bi-objective integrated supply chain design with transportation choices: A multi-objective particle swarm optimization. Journal of Industrial and Management Optimization, 2019, 15 (3) : 1263-1288. doi: 10.3934/jimo.2018095

[20]

Abdulrazzaq T. Abed, Azzam S. Y. Aladool. Applying particle swarm optimization based on Padé approximant to solve ordinary differential equation. Numerical Algebra, Control and Optimization, 2022, 12 (2) : 321-337. doi: 10.3934/naco.2021008

2020 Impact Factor: 2.425

Metrics

  • PDF downloads (125)
  • HTML views (0)
  • Cited by (3)

Other articles
by authors

[Back to Top]