Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2014, Article ID 437592, 6 pages
http://dx.doi.org/10.1155/2014/437592
Research Article

Day-Ahead Wind Speed Forecasting Using Relevance Vector Machine

1Research Center for Renewable Energy Generation Engineering, Ministry of Education, Hohai University, Nanjing 210098, China
2ALSTOM GRID Technology Center Co., Ltd., Shanghai 201114, China
3ALSTOM Grid Inc., Redmond, WA 98052, USA

Received 31 December 2013; Revised 5 May 2014; Accepted 22 May 2014; Published 12 June 2014

Academic Editor: Hongjie Jia

Copyright © 2014 Guoqiang Sun 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. Global Wind Energy Council and Greenpeace, Global Wind Power Development Outlook in 2012, Global Wind Energy Council and Greenpeace, Bejing, China, 2012.
  2. M. S. Roulston, D. T. Kaplan, J. Hardenberg, and L. A. Smith, “Using medium-range weather forcasts to improve the value of wind energy production,” Renewable Energy, vol. 28, no. 4, pp. 585–602, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. L. Lazić, G. Pejanović, and M. Živković, “Wind forecasts for wind power generation using the Eta model,” Renewable Energy, vol. 35, no. 6, pp. 1236–1243, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Kamal and Y. Z. Jafri, “Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan,” Solar Energy, vol. 61, no. 1, pp. 23–32, 1997. View at Publisher · View at Google Scholar · View at Scopus
  5. R. G. Kavasseri and K. Seetharaman, “Day-ahead wind speed forecasting using f-ARIMA models,” Renewable Energy, vol. 34, no. 5, pp. 1388–1393, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Bilgili, B. Sahin, and A. Yasar, “Application of artificial neural networks for the wind speed prediction of target station using reference stations data,” Renewable Energy, vol. 32, no. 14, pp. 2350–2360, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. R. L. Welch, S. M. Ruffing, and G. K. Venayagamoorthy, “Comparison of feedforward and feedback neural network architectures for short term wind speed prediction,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '09), pp. 3335–3340, Atlanta, Ga, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. M. A. Mohandes, T. O. Halawani, S. Rehman, and A. A. Hussain, “Support vector machines for wind speed prediction,” Renewable Energy, vol. 29, no. 6, pp. 939–947, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. K. A. Larson and K. Westrick, “Short-term wind forecasting using off-site observations,” Wind Energy, vol. 9, no. 1-2, pp. 55–62, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. M. E. Tipping, “The relevance vector machine,” in Advances in Neural Information Processing Systems, vol. 12, pp. 652–658, 2000. View at Google Scholar
  11. M. E. Tipping and A. C. Faul, “Fast marginal likelihood maximization for sparse Bayesian models,” in Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics, vol. 1, Key West, Fla, USA, January 2003.
  12. P. K. Wong, Q. Xu, C. M. Vong, and H. C. Wong, “Rate-dependent hysteresis modeling and control of a piezostage using online support vector machine and relevance vector machine,” IEEE Transactions on Industrial Electronics, vol. 59, no. 4, pp. 1988–2001, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. M. E. Tipping, “Sparse Bayesian learning and the relevance vector machine,” Journal of Machine Learning Research, vol. 1, no. 3, pp. 211–244, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  14. J. O. Berger, Statistical Decision Theory and Bayesian Analysis, Springer Series in Statistics, Springer, New York, NY, USA, 1985. View at Publisher · View at Google Scholar · View at MathSciNet
  15. G. Wahba, “A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem,” The Annals of Statistics, vol. 13, no. 4, pp. 1378–1402, 1985. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  16. D. J. MacKay, Bayesian methods for adaptive models [Ph.D. thesis], California Institute of Technology, 1992.
  17. Q. Duan, J. G. Zhao, and Y. Ma, “Relevance vector machine based on particle swarm optimization of compounding kernels in electricity load forecasting,” Electric Machines and Control, vol. 14, no. 6, pp. 33–38, 2010. View at Google Scholar · View at Scopus