Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 939305, 14 pages
http://dx.doi.org/10.1155/2015/939305
Research Article

Time Series Analysis and Forecasting for Wind Speeds Using Support Vector Regression Coupled with Artificial Intelligent Algorithms

1School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
2MOE Key Laboratory of Western China’s Environmental Systems, Research School of Arid Environment & Climate Change, Lanzhou University, Lanzhou 730000, China
3School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
4China Water Resources Beifang Investigation Design and Research Co. Ltd., Tianjin 300222, China

Received 8 October 2014; Accepted 16 December 2014

Academic Editor: Erol Egrioglu

Copyright © 2015 Ping Jiang 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. J. Wu, J. Wang, and D. Chi, “Wind energy potential assessment for the site of Inner Mongolia in China,” Renewable and Sustainable Energy Reviews, vol. 21, pp. 215–228, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Wang, S. Qin, Q. Zhou, and H. Jiang, “Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China,” Renewable Energy, vol. 76, pp. 91–101, 2015. View at Publisher · View at Google Scholar
  3. Z. Guo, W. Zhao, H. Lu, and J. Wang, “Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model,” Renewable Energy, vol. 37, no. 1, pp. 241–249, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. P. Kou, F. Gao, and X. Guan, “Sparse online warped Gaussian process for wind power probabilistic forecasting,” Applied Energy, vol. 108, pp. 410–428, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. P. S. Georgilakis, “Technical challenges associated with the integration of wind power into power systems,” Renewable and Sustainable Energy Reviews, vol. 12, no. 3, pp. 852–863, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Hu, J. Wang, and G. Zeng, “A hybrid forecasting approach applied to wind speed time series,” Renewable Energy, vol. 60, pp. 185–194, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Chen, X. Xue, M. Ha, D. Yu, and L. Ma, “Support vector regression method for wind speed prediction incorporating probability prior knowledge,” Mathematical Problems in Engineering, vol. 2014, Article ID 410489, 10 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. Guo, D. Chi, J. Wu, and W. Zhang, “A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm,” Energy Conversion and Management, vol. 84, pp. 140–151, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. Z. Guo, Y. Dong, J. Wang, and H. Lu, “The forecasting procedure for long-term wind speed in the Zhangye area,” Mathematical Problems in Engineering, vol. 2010, Article ID 684742, 17 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. 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
  11. 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
  12. A. Tascikaraoglu and M. Uzunoglu, “A review of combined approaches for prediction of short-term wind speed and power,” Renewable and Sustainable Energy Reviews, vol. 34, pp. 243–254, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. 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
  14. 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
  15. 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, IEEE, November 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Zhang, J. Wu, J. Wang, W. Zhao, and L. Shen, “Performance analysis of four modified approaches for wind speed forecasting,” Applied Energy, vol. 99, pp. 324–333, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. J. L. Torres, A. García, M. De Blas, and A. De Francisco, “Forecast of hourly average wind speed with ARMA models in Navarre (Spain),” Solar Energy, vol. 79, no. 1, pp. 65–77, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. E. Cadenas and W. Rivera, “Wind speed forecasting in the South Coast of Oaxaca, México,” Renewable Energy, vol. 32, no. 12, pp. 2116–2128, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. G. Li and J. Shi, “On comparing three artificial neural networks for wind speed forecasting,” Applied Energy, vol. 87, no. 7, pp. 2313–2320, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. I. G. Damousis, M. C. Alexiadis, J. B. Theocharis, and P. S. Dokopoulos, “A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation,” IEEE Transactions on Energy Conversion, vol. 19, no. 2, pp. 352–361, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. 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
  22. J. M. Morales, R. Mínguez, and A. J. Conejo, “A methodology to generate statistically dependent wind speed scenarios,” Applied Energy, vol. 87, no. 3, pp. 843–855, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. G. Li, J. Shi, and J. Zhou, “Bayesian adaptive combination of short-term wind speed forecasts from neural network models,” Renewable Energy, vol. 36, no. 1, pp. 352–359, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Monfared, H. Rastegar, and H. M. Kojabadi, “A new strategy for wind speed forecasting using artificial intelligent methods,” Renewable Energy, vol. 34, no. 3, pp. 845–848, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Salcedo-Sanz, E. G. Ortiz-García, A. Portilla-Figueras, L. Prieto, and D. Paredes, “Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction,” Renewable Energy, vol. 34, no. 6, pp. 1451–1457, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. E. Cadenas and W. Rivera, “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model,” Renewable Energy, vol. 35, no. 12, pp. 2732–2738, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. http://en.wikipedia.org/wiki/Cross-correlation#Time_delay_analysis.
  28. L. Liu and Y. Wang, “Cross-correlations between spot and futures markets of nonferrous metals,” Physica A: Statistical Mechanics and its Applications, vol. 400, pp. 20–30, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. J. L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, no. 2, pp. 179–211, 1990. View at Publisher · View at Google Scholar · View at Scopus
  30. Q. He, Neural Network and Its Application in IR, Graduate School of Library and Information Science, University of Illinois at Urbana, Champaign, Ill, USA, 1999.
  31. J. Hertz, Introduction to the Theory of Neural Computation.Vol. 1. Basic Books, 1991.
  32. V. Vapnik, S. E. Golowich, and A. Smola, “Support vector method for function approximation, regression estimation, and signal processing,” in Advances in Neural Information Processing Systems, pp. 281–287, 1997. View at Google Scholar
  33. W.-C. Hong, Y. Dong, W. Y. Zhang, L.-Y. Chen, and B. K. Panigrahi, “Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm,” International Journal of Electrical Power and Energy Systems, vol. 44, no. 1, pp. 604–614, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. J. Wang, W. Zhu, W. Zhang, and D. Sun, “A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand,” Energy Policy, vol. 37, no. 11, pp. 4901–4909, 2009. View at Publisher · View at Google Scholar · View at Scopus
  35. J. Wang, S. Zhu, W. Zhang, and H. Lu, “Combined modeling for electric load forecasting with adaptive particle swarm optimization,” Energy, vol. 35, no. 4, pp. 1671–1678, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. X.-S. Yang and S. Deb, “Engineering optimisation by cuckoo search,” International Journal of Mathematical Modelling and Numerical Optimisation, vol. 1, no. 4, pp. 330–343, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. Y. Shi, “An optimization algorithm based on brainstorming process,” International Journal of Swarm Intelligence Research, vol. 2, no. 4, pp. 35–62, 2011. View at Publisher · View at Google Scholar
  38. J. Ahmed and Z. Salam, “A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability,” Applied Energy, vol. 119, pp. 118–130, 2014. View at Publisher · View at Google Scholar · View at Scopus
  39. T. G. Barbounis and J. B. Theocharis, “Locally recurrent neural networks for wind speed prediction using spatial correlation,” Information Sciences, vol. 177, no. 24, pp. 5775–5797, 2007. View at Publisher · View at Google Scholar · View at Scopus
  40. Z. Guo, J. Zhao, W. Zhang, and J. Wang, “A corrected hybrid approach for wind speed prediction in Hexi Corridor of China,” Energy, vol. 36, no. 3, pp. 1668–1679, 2011. View at Publisher · View at Google Scholar · View at Scopus
  41. J. Wang, S. Qin, S. Jin, and J. Wu, “Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources,” Renewable and Sustainable Energy Reviews, vol. 42, pp. 26–42, 2015. View at Publisher · View at Google Scholar