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Abstract and Applied Analysis
Volume 2014, Article ID 984268, 21 pages
http://dx.doi.org/10.1155/2014/984268
Research Article

Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

1Gansu Meteorological Service Center, Lanzhou, Gansu 730020, China
2School of Mathematics & Statistics, Lanzhou University, Lanzhou, Gansu 730000, China
3Gansu Meteorological Information & Technique Support & Equipment Center, Lanzhou, Gansu 730020, China

Received 18 February 2014; Accepted 18 April 2014; Published 9 June 2014

Academic Editor: Fuding Xie

Copyright © 2014 Xuejun 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. I. Fyrippis, P. J. Axaopoulos, and G. Panayiotou, “Wind energy potential assessment in Naxos Island, Greece,” Applied Energy, vol. 87, no. 2, pp. 577–586, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Fabbri, T. G. S. Román, J. R. Abbad, and V. H. M. Quezada, “Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market,” IEEE Transactions on Power Systems, vol. 20, no. 3, pp. 1440–1446, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. I. J. Ramírez-Rosado and L. A. Fernández-Jiménez, “An advanced model for short-term forecasting of mean wind speed and wind electric power,” Control and Intelligent Systems, vol. 32, no. 1, pp. 21–26, 2004. View at Google Scholar · View at Scopus
  4. A. Sfetsos, “A novel approach for the forecasting of mean hourly wind speed time series,” Renewable Energy, vol. 27, no. 2, pp. 163–174, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Sfetsos, “A comparison of various forecasting techniques applied to mean hourly wind speed time series,” Renewable Energy, vol. 21, no. 1, pp. 23–35, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Lange and U. Focken, Physical Approach to Short-Term Wind Power Prediction, Springer, New York, NY, USA, 2009.
  7. L. Landberg, “Short-term prediction of the power production from wind farms,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 80, no. 1-2, pp. 207–220, 1999. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Negnevitsky and C. W. Potter, “Innovative short-term wind generation prediction techniques,” in Proceedings of the IEEE PES Power Systems Conference and Exposition (PSCE '06), pp. 60–65, Atlanta, Ga, USA, October-November 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. S. J. Wu and S. L. Lin, “Intelligent web-based fuzzy and grey models for hourly wind speed forecast,” International Journal of Computers, vol. 4, pp. 235–242, 2010. View at Google Scholar
  10. J. F. Li, B. H. Zhang, G. L. Xie, Y. Li, and C. X. Mao, “Grey predictor models for wind speed-wind power prediction,” Power System Protection and Control, vol. 38, no. 19, pp. 151–159, 2010. View at Google Scholar · View at Scopus
  11. L. Lin, J. T. Eriksson, H. Vihriala, and L. Soderlund, “Predicting wind behavior with neural networks,” in Proceedings the European Wind Energy Conference, pp. 655–658, 1996.
  12. M. C. Alexiadis, P. S. Dokopoulos, H. S. Sahsamanoglou, and I. M. Manousaridis, “Short-term forecasting of wind speed and related electrical power,” Solar Energy, vol. 63, no. 1, pp. 61–68, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. H. G. Beyer, T. Degner, J. Haussmann, M. Hoffman, and P. Rujan, “Short term forecast of wind speed and power output of a wind turbine with neural networks,” in Proceedings of the 2nd European Congress on Intelligent Techniques and Soft Computing, pp. 349–352, 1994.
  14. G. Kariniotakis, G. S. Stavrakakis, and E. F. Nogaret, “Wind power forecasting using advanced neural network models,” IEEE Transactions on Energy Conversion, vol. 11, no. 4, pp. 762–767, 1996. View at Publisher · View at Google Scholar
  15. A. More and M. C. Deo, “Forecasting wind with neural networks,” Marine Structures, vol. 16, no. 1, pp. 35–49, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Kariniotakis, G. S. Stavrakakis, and E. F. Nogaret, “A fuzzy logic and neural network based wind power model,” in Proceedings of the European Wind Energy Conference, pp. 596–599, 1996.
  17. X. Wang, G. Sideratos, N. Hatziargyriou, and L. H. Tsoukalas, “Wind speed forecasting for power system operational planning,” in Proceedings of the International Conference on Probabilistic Methods Applied to Power Systems, pp. 470–474, Ames, Iowa, USA, September 2004. View at Scopus
  18. S. Makridakis, “Why combining works?” International Journal of Forecasting, vol. 5, no. 4, pp. 601–603, 1989. View at Google Scholar · View at Scopus
  19. F. C. Palm and A. Zellner, “To combine or not to combine? Issues of combining forecasts,” International Journal of Forecasting, vol. 11, no. 8, pp. 687–701, 1992. View at Publisher · View at Google Scholar
  20. R. L. Winkler, “Combining forecasts: a philosophical basis and some current issues,” International Journal of Forecasting, vol. 5, no. 4, pp. 605–609, 1989. View at Google Scholar · View at Scopus
  21. P. Newbold and C. W. J. Granger, “Experience with forecasting univariate time series and the combination of forecasts,” Journal of the Royal Statistical Society, vol. 137, no. 2, pp. 131–165, 1974. View at Publisher · View at Google Scholar · View at MathSciNet
  22. N. An, W. Zhao, J. Wang, D. Shang, and E. Zhao, “Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting,” Energy, vol. 49, pp. 279–288, 2013. View at Publisher · View at Google Scholar
  23. Y. Huang, “Advances in artificial neural networks—methodological development and application,” Algorithms, vol. 2, no. 3, pp. 973–1007, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  24. J. W. Tukey, Exploratory Data Analysis, Addison-Wesley, Reading, Mass, USA, 1977.
  25. P. A. Tukey and J. W. Tukey, “Graphic display of data sets in 3 or more dimensions,” in Interpreting Multivariate Data, Wadsworth, Belmont, Calif, USA, 1981. View at Google Scholar
  26. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis,” Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences, vol. 454, no. 1971, pp. 903–995, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  27. Y.-C. Wei, C.-J. Lee, W.-Y. Hung, and H.-T. Chen, “Application of Hilbert-Huang transform to characterize soil liquefaction and quay wall seismic responses modeled in centrifuge shaking-table tests,” Soil Dynamics and Earthquake Engineering, vol. 30, no. 7, pp. 614–629, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. S. Kizhner, K. Blank, T. Flatley, N. E. Huang, D. Petrick, and P. Hestnes, “On certain theoretical developments underlying the Hilbert-Huang transform,” in Proceedings of the IEEE Aerospace Conference, Big Sky, Mont, USA, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1–41, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1–41, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. J.-R. Yeh, J.-S. Shieh, and N. E. Huang, “Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method,” Advances in Adaptive Data Analysis, vol. 2, no. 2, pp. 135–156, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  32. P. H. Tsui, C. C. Chang, C. C. Chang, N. E. Huang, and M. C. Ho, “An adaptive threshold filter for ultrasound signal rejection,” Ultrasonics, vol. 49, no. 4-5, pp. 413–418, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. P. H. Tsui, C. C. Chang, and N. E. Huang, “Noise-modulated empirical mode decomposition,” Advances in Adaptive Data Analysis, vol. 2, no. 1, pp. 25–37, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, New York, NY, USA, 2nd edition, 1998.
  35. L. Ljung, System Identification: Theory for the User, Prentice Hall PTR, 2nd edition, 1998.
  36. K. Levenberg, “A method for the solution of certain problems in least squares,” Quarterly of Applied Mathematics, vol. 2, pp. 164–168, 1944. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet