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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 410489, 10 pages
http://dx.doi.org/10.1155/2014/410489
Research Article

Support Vector Regression Method for Wind Speed Prediction Incorporating Probability Prior Knowledge

Jiqiang Chen,1,2 Xiaoping Xue,1 Minghu Ha,2 Daren Yu,3 and Litao Ma2

1Department of Mathematics, Harbin Institute of Technology, Harbin 150001, China
2School of Science, Hebei University of Engineering, Handan 056038, China
3School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China

Received 3 December 2013; Accepted 20 January 2014; Published 4 March 2014

Academic Editor: Huaiqin Wu

Copyright © 2014 Jiqiang Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. H. M. El-Fouly, E. F. El-Saadany, and M. M. A. Salama, “One day ahead prediction of wind speed and direction,” IEEE Transactions on Energy Conversion, vol. 23, no. 1, pp. 191–201, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. G. Zhang, H. X. Li, and M. Gan, “Design a wind speed prediction model using probabilistic fuzzy system,” IEEE Transactions on Industrial Informatics, vol. 8, no. 4, pp. 819–827, 2012. View at Google Scholar
  3. F. Cassola and M. Burlando, “Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output,” Applied Energy, vol. 99, pp. 154–166, 2012. View at Google Scholar
  4. S. J. Mitchell, N. Lanquaye-Opoku, H. Modzelewski et al., “Comparison of wind speeds obtained using numerical weather prediction models and topographic exposure indices for predicting windthrow in mountainous terrain,” Forest Ecology and Management, vol. 254, no. 2, pp. 193–204, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Louka, G. Galanis, N. Siebert et al., “Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 96, no. 12, pp. 2348–2362, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. Q. Cao, B. T. Ewing, and M. A. Thompson, “Forecasting wind speed with recurrent neural networks,” European Journal of Operational Research, vol. 221, no. 1, pp. 148–154, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. M. J. Woods, C. J. Russell, and R. J. Davy, “Simulation of wind power at several locations using a measured time-series of wind speed,” IEEE Transactions on Power Systems, vol. 28, no. 1, pp. 219–226, 2013. View at Google Scholar
  8. V. Vapnik, S. E. Golowich, and A. Smola, “Support vector method for function approximation, regression estimation, and signal processing,” in Neural Information Processing Systems, M. Mozer, M. Jordan, and T. Petsche, Eds., vol. 9, MIT Press, Cambridge, Mass, USA, 1997. View at Google Scholar
  9. H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik, “Support vector regression machines,” in Advances in Neural Information Processing Systems, M. Mozer, M. Jordan, and T. Petsche, Eds., vol. 9, pp. 155–161, MIT Press, Cambridge, MA, 1997. View at Google Scholar
  10. P. Guan, D. Huang, and M. He, “Lung cancer gene expression database analysis incorporating prior knowledge with support vector machine-based classification method,” Journal of Experimental and Clinical Cancer Research, vol. 28, no. 1, article 103, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. Zhang, G. Dai, and M. I. Jordan, “Bayesian generalized kernel mixed models,” Journal of Machine Learning Research, vol. 12, pp. 111–139, 2011. View at Google Scholar · View at Scopus
  12. F. Q. Liu and X. P. Xue, “Constructing kernels by fuzzy rules for support vector regressions,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 7, pp. 4811–4822, 2012. View at Google Scholar
  13. M. H. Ha, C. Wang, Z. M. Zhang, and D. Z. Tian, Uncertainty Statistical Learning Theory, Science Press, Beijing, China, 2010, (Chinese).
  14. O. Chapelle, V. Sindhwani, and S. S. Keerthi, “Optimization techniques for semi-supervised support vector machines,” Journal of Machine Learning Research, vol. 9, pp. 203–233, 2008. View at Google Scholar · View at Scopus
  15. N. Y. Deng and Y. J. Tian, Support Vector Machine Theory, Algorithms and Generalization, Science Press, Beijing, China, 2009, (Chinese).
  16. Y. C. Tsang, Y. Q. Zhang, N. V. Chawla, and S. Krasser, “SVMs modeling for highly imbalanced classification,” IEEE Transactions on Systems, Man and Cybernetics B, vol. 39, no. 1, pp. 281–288, 2009. View at Google Scholar
  17. M. Narwaria and W. Lin, “Objective image quality assessment based on support vector regression,” IEEE Transactions on Neural Networks, vol. 21, no. 3, pp. 515–519, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Karasuyama and I. Takeuchi, “Multiple incremental decremental learning of support vector machines,” IEEE Transactions on Neural Networks, vol. 21, no. 7, pp. 1048–1059, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Ji, J. Pang, and H. Qiu, “Support vector machine for classification based on fuzzy training data,” Expert Systems with Applications, vol. 37, no. 4, pp. 3495–3498, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Korth, E. Echer, Q.-G. Zong, F. L. Guarnieri, M. Fraenz, and C. G. Mouikis, “The response of the polar cusp to a high-speed solar wind stream studied by a multispacecraft wavelet analysis,” Journal of Atmospheric and Solar-Terrestrial Physics, vol. 73, no. 1, pp. 52–60, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. O. L. Mangasarian and E. W. Wild, “Nonlinear knowledge in kernel approximation,” IEEE Transactions on Neural Networks, vol. 18, no. 1, pp. 300–306, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. M. El Mokadem, V. Courtecuisse, C. Saudemont, B. Robyns, and J. Deuse, “Fuzzy logic supervisor-based primary frequency control experiments of a variable-speed wind generator,” IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 407–417, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Zhou, J. Shi, and G. Li, “Fine tuning support vector machines for short-term wind speed forecasting,” Energy Conversion and Management, vol. 52, no. 4, pp. 1990–1998, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. E. G. Ortiz-García, S. Salcedo-Sanz, Ã. M. Pérez-Bellido, J. Gascõn-Moreno, J. A. Portilla-Figueras, and L. Prieto, “Short-term wind speed prediction in wind farms based on banks of support vector machines,” Wind Energy, vol. 14, no. 2, pp. 193–207, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Fisz, Probability Theory and Mathematical Statistics, Wiley, New York, NY, USA, 3rd edition, 1963.