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Advances in Meteorology
Volume 2017, Article ID 6856139, 22 pages
https://doi.org/10.1155/2017/6856139
Research Article

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

1State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
2School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
3School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China

Correspondence should be addressed to Xiaohui Yuan; nc.ude.tsuh@17hxy and Xiaohui Lei; moc.361@yks_iuhoaixiel

Received 1 August 2017; Revised 6 November 2017; Accepted 14 November 2017; Published 12 December 2017

Academic Editor: Ilan Levy

Copyright © 2017 Aiqing Kang 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. http://www.wwindea.org/wp-content/uploads/filebase/market_reports/Wind_Energy_Installations_2016.pdf.
  2. B. Ji, X. Yuan, X. Li, Y. Huang, and W. Li, “Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration,” Energy Conversion and Management, vol. 87, pp. 589–598, 2014. View at Publisher · View at Google Scholar · View at Scopus
  3. X. Yuan, B. Ji, S. Zhang, H. Tian, and Z. Chen, “An improved artificial physical optimization algorithm for dynamic dispatch of generators with valve-point effects and wind power,” Energy Conversion and Management, vol. 82, pp. 92–105, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. Chen, X. Yuan, B. Ji, P. Wang, and H. Tian, “Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II,” Energy Conversion and Management, vol. 84, pp. 390–404, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. X. Yuan, B. Ji, S. Zhang, H. Tian, and Y. Hou, “A new approach for unit commitment problem via binary gravitational search algorithm,” Applied Soft Computing, vol. 22, pp. 249–260, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Liu, E. Erdem, and J. Shi, “Comprehensive evaluation of ARMA-GARCH(-M) approaches for modeling the mean and volatility of wind speed,” Applied Energy, vol. 88, no. 3, pp. 724–732, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. X. Yuan, Y. Zhang, and Y. Yuan, “Improved self-adaptive chaotic genetic algorithm for hydrogeneration scheduling,” Journal of Water Resources Planning and Management, vol. 134, no. 4, pp. 319–325, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Jung and R. P. Broadwater, “Current status and future advances for wind speed and power forecasting,” Renewable & Sustainable Energy Reviews, vol. 31, pp. 762–777, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Xiao, J. Wang, Y. Dong, and J. Wu, “Combined forecasting models for wind energy forecasting: A case study in China,” Renewable & Sustainable Energy Reviews, vol. 44, pp. 271–288, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. 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
  11. E. Cadenas and W. Rivera, “Wind speed forecasting in the South Coast of Oaxaca, México,” Journal of Renewable Energy, vol. 32, no. 12, pp. 2116–2128, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. R. G. Kavasseri and K. Seetharaman, “Day-ahead wind speed forecasting using f-ARIMA models,” Journal of Renewable Energy, vol. 34, no. 5, pp. 1388–1393, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. E. Erdem and J. Shi, “ARMA based approaches for forecasting the tuple of wind speed and direction,” Applied Energy, vol. 88, no. 4, pp. 1405–1414, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Ambach and W. Schmid, “Periodic and long range dependent models for high frequency wind speed data,” Energy, vol. 82, pp. 277–293, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Salcedo-Sanz, E. G. Ortiz-García, Á. M. Pérez-Bellido, A. Portilla-Figueras, and L. Prieto, “Short term wind speed prediction based on evolutionary support vector regression algorithms,” Expert Systems with Applications, vol. 38, no. 4, pp. 4052–4057, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. 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
  17. X. H. Yuan, C. Chen, Y. B. Yuan, Y. H. Huang, and Q. X. Tan, “Short-term wind power prediction based on LSSVM-GSA model,” Energy Conversion and Management, vol. 101, pp. 393–401, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Li, J. Shi, and J. Zhou, “Bayesian adaptive combination of short-term wind speed forecasts from neural network models,” Journal of Renewable Energy, vol. 36, no. 1, pp. 352–359, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Jiang, Z. Song, and A. Kusiak, “Very short-term wind speed forecasting with Bayesian structural break model,” Journal of Renewable Energy, vol. 50, pp. 637–647, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. E. Cadenas and W. Rivera, “Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks,” Journal of Renewable Energy, vol. 34, no. 1, pp. 274–278, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. 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
  22. C. Ren, N. An, J. Wang, L. Li, B. Hu, and D. Shang, “Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting,” Knowledge-Based Systems, vol. 56, pp. 226–239, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. J. J. Flores, M. Graff, and H. Rodriguez, “Evolutive design of ARMA and ANN models for time series forecasting,” Journal of Renewable Energy, vol. 44, pp. 225–230, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. Y.-Y. Hong, H.-L. Chang, and C.-S. Chiu, “Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs,” Energy, vol. 35, no. 9, pp. 3870–3876, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. X. Wu, Z. Zhu, X. Su et al., “A study of single multiplicative neuron model with nonlinear filters for hourly wind speed prediction,” Energy, vol. 88, pp. 194–201, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. A. Tascikaraoglu and M. Uzunoglu, “A review of combined approaches for prediction of short-term wind speed and power,” Renewable & Sustainable Energy Reviews, vol. 34, pp. 243–254, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Salcedo-Sanz, Á. M. Pérez-Bellido, 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,” Journal of Renewable Energy, vol. 34, no. 6, pp. 1451–1457, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. 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
  29. O. B. Shukur and M. H. Lee, “Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA,” Journal of Renewable Energy, vol. 76, pp. 637–647, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. Z. H. Guo, J. Zhao, W. Y. Zhang, and J. Z. 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
  31. H. Liu, H. Q. Tian, and Y. F. Li, “Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction,” Applied Energy, vol. 98, pp. 415–424, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Yu, K. Chen, J. Mori, and M. M. Rashid, “A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction,” Energy, vol. 61, pp. 673–686, 2013. View at Publisher · View at Google Scholar · View at Scopus
  33. H. Bouzgou and N. Benoudjit, “Multiple architecture system for wind speed prediction,” Applied Energy, vol. 88, no. 7, pp. 2463–2471, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. K. Chen and J. Yu, “Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach,” Applied Energy, vol. 113, pp. 690–705, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. H. Liu, H.-Q. Tian, D.-F. Pan, and Y.-F. Li, “Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks,” Applied Energy, vol. 107, pp. 191–208, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. Z. H. Guo, W. G. Zhao, H. Y. Lu, and J. Wang, “Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model,” Journal of Renewable Energy, vol. 37, no. 1, pp. 241–249, 2012. View at Publisher · View at Google Scholar · View at Scopus
  37. J. M. Hu, J. Z. Wang, and K. L. Ma, “A hybrid technique for short-term wind speed prediction,” Energy, vol. 81, no. 1, pp. 563–574, 2015. View at Publisher · View at Google Scholar
  38. H. Liu, H.-Q. Tian, and Y.-F. Li, “Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms,” Energy Conversion and Management, vol. 100, pp. 16–22, 2015. View at Publisher · View at Google Scholar · View at Scopus
  39. Y. Ren, P. N. Suganthan, and N. Srikanth, “A comparative study of empirical mode decomposition-based short-term wind speed forecasting methods,” IEEE Transactions on Sustainable Energy, vol. 6, no. 1, pp. 236–244, 2015. View at Publisher · View at Google Scholar · View at Scopus
  40. Z. H. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis (AADA), vol. 1, no. 1, pp. 1–41, 2009. View at Publisher · View at Google Scholar · View at Scopus
  41. J. A. K. Suykens and J. Vandewalle, “Recurrent least squares support vector machines,” IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, vol. 47, no. 7, pp. 1109–1114, 2000. View at Publisher · View at Google Scholar · View at Scopus
  42. G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, John Wiley & Sons, Hoboken, NJ, USA, 4th edition, 2008.