Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Short-term Wind Power Prediction Based on Empirical Mode Decomposition and Improved Extreme Learning Machine

  • Tian, Zhongda (College of Information Science and Engineering, Shenyang University of Technology) ;
  • Ren, Yi (College of Information Science and Engineering, Shenyang University of Technology) ;
  • Wang, Gang (College of Information Science and Engineering, Shenyang University of Technology)
  • Received : 2017.12.15
  • Accepted : 2018.06.07
  • Published : 2018.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

For the safe and stable operation of the power system, accurate wind power prediction is of great significance. A wind power prediction method based on empirical mode decomposition and improved extreme learning machine is proposed in this paper. Firstly, wind power time series is decomposed into several components with different frequency by empirical mode decomposition, which can reduce the non-stationary of time series. The components after decomposing remove the long correlation and promote the different local characteristics of original wind power time series. Secondly, an improved extreme learning machine prediction model is introduced to overcome the sample data updating disadvantages of standard extreme learning machine. Different improved extreme learning machine prediction model of each component is established. Finally, the prediction value of each component is superimposed to obtain the final result. Compared with other prediction models, the simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

Keywords

References

  1. Y. Wang, Z. X. Xie, Q. H. Hu, and S. H. Xiong. "Correlation aware multi-step ahead wind speed forecasting with heteroscedastic multi-kernel learning," Energy Conversion and Management, vol. 163, pp. 384-406, May. 2018. https://doi.org/10.1016/j.enconman.2018.02.034
  2. W. Y. Y. Cheng, Y. B. Liu, A. J. Bourgeois, Y. H. Wu, and S. E. Haupt, "Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation," Renewable Energy, vol. 107, pp. 340-351, Jul. 2017. https://doi.org/10.1016/j.renene.2017.02.014
  3. Z. D. Tian, S. J. Li, Y. H. Wang, and X. D. Wang. "Wind power prediction method based on hybrid kernel function support vector machine," Wind Engineering, vol. 42, no. 3, pp. 252-264, Jun. 2018. https://doi.org/10.1177/0309524X17737337
  4. U. Meyyappan. "Wavelet neural network-based wind speed forecasting and application of shuffled frog leap algorithm for economic dispatch with prohibited zones incorporating wind power," Wind Engineering, vol. 42, no. 1, pp. 3-15, Jan. 2018. https://doi.org/10.1177/0309524X17723208
  5. I. Colak, S. Sagiroglu, and M. Yesilbudak. "Data mining and wind power prediction: A literature review," Renewable Energy, vol. 46, pp. 241-247, Oct. 2012. https://doi.org/10.1016/j.renene.2012.02.015
  6. T. H. Ouyang, X. M. Zha, L. Qin, Y. Xiong, and T. Xia. "Wind power prediction method based on regime of switching kernel functions," Journal of Wind Engineering & Industrial Aerodynamics, vol. 153, pp. 26-33, Jun. 2016. https://doi.org/10.1016/j.jweia.2016.03.005
  7. S. Al-Yahyai, Y. Charabi, and A. Gastli. "Review of the use of Numerical Weather Prediction (NWP) Models for wind energy assessment," Renewable & Sustainable Energy Reviews, vol. 14, no. 9, pp. 3192-3198, Dec. 2010. https://doi.org/10.1016/j.rser.2010.07.001
  8. J. H. Li, J. M. Li, J. Y. Wen, S. J. Cheng, H. L. Xie, and C. Y. Yue. "Generating wind power time series based on its persistence and variation characteristics," Science China Technological Sciences, vol. 57, no. 12, pp. 2457-2486, Dec. 2014.
  9. C. Liu, C. Li, Y. H. Huang, and Y. F. Wang. "A novel stochastic modeling method of wind power time series considering the fluctuation process characteristics," Journal of Renewable & Sustainable Energy, vol. 8, no. 3, 033304, May. 2016. https://doi.org/10.1063/1.4954079
  10. C. Wang, H. L. Zhang, W. H. Fan, and X. C. Fan. "A new wind power prediction method based on chaotic theory and Bernstein Neural Network," Energy, vol. 117, pp. 259-271, Dec. 2016. https://doi.org/10.1016/j.energy.2016.10.041
  11. J. D. Wang, K. J. Fang, W. J. Pang, and J. W. Sun. "Wind power interval prediction based on improved PSO and BP neural network," Journal of Electrical Engineering & Technology, vol. 12, no. 3, pp. 989-995, May. 2017. https://doi.org/10.5370/JEET.2017.12.3.989
  12. R. R. B. de Aquino, O. N. Neto, R. B. Souza, M. M. S. Lira, M. A. Carvalho, T. B. Ludermir, and A. A. Ferreira. "Investigating the use of echo state networks for prediction of wind power generation," IEEE on Computational Intelligence for Engineering Solutions, 2015, pp. 148-154.
  13. C. D. Zuluaga, M. A. Alvarez, and E. Giraldo. "Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison," Applied Energy, vol. 156, pp. 321-330, Oct, 2015. https://doi.org/10.1016/j.apenergy.2015.07.043
  14. J. R. Yang, X. C. Wang, X. F. Luo, and C. Jiang. "Intelligent combined prediction of wind power based on numerical weather prediction and fuzzy clustering," IFAC - Papers Online, vol. 48, no. 28, pp. 538-543, Aug. 2015.
  15. X. K. Wang, D. S. Luo, and H. Y. He. "An improved feature weighted fuzzy clustering algorithm with its application in short-term prediction of wind power," 6th Chinese Conference on Pattern Recognition, 2014, pp. 575-584.
  16. Y. X. Liu and Y. Y. Zhang. "A rolling ARMA method for ultra short term wind power prediction," 13th IEEE Conference on Automation Science and Engineering, 2017, pp. 1232-1236.
  17. X. B. Kong, X. J. Liu, R. F. Shi, and K. Y. Lee. "Wind speed prediction using reduced support vector machines with feature selection," Neurocomputing, vol. 169, pp. 449-456, Dec. 2015. https://doi.org/10.1016/j.neucom.2014.09.090
  18. Y. C. Xiao, C. Y. Li, and P. Wang. "Wind power prediction based on improved grey theory and SVM," Journal of Information & Computational Science, vol. 11, no. 16, pp. 5937-5944, Nov. 2014. https://doi.org/10.12733/jics20104912
  19. Q. L. Wu and C. Y. Peng. "Wind power grid connected capacity prediction using LSSVM optimized by the bat algorithm," Energies, vol. 8, no. 12, pp. 14346-14360, Dec. 2015. https://doi.org/10.3390/en81212428
  20. J. L. Lou, H. Cao, B. Song, and J. Z. Xiao. "An output power prediction method for multiple wind farms under energy internet environment," International Journal of Grid and Distributed Computing, vol. 9, no. 11, pp. 273-284, Nov. 2016.
  21. T. H. Ouyang, X. M. Zha, and L. Qin. "A combined multivariate model for wind power prediction," Energy Conversion & Management, vol. 144, pp. 361-373, Jul. 2017. https://doi.org/10.1016/j.enconman.2017.04.077
  22. G. B. Huang, Q. Y. Zhu, and C. K. Siew. "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, no. 1-3, pp. 489-501, Dec. 2006. https://doi.org/10.1016/j.neucom.2005.12.126
  23. Y. Lan, Y. C. Soh, and G. B. Huang. "Two-stage extreme learning machine for regression," Neurocomputing, vol. 73, no. 16, pp. 3028-3038, Oct. 2010. https://doi.org/10.1016/j.neucom.2010.07.012
  24. G. B. Huang, H. M. Zhou, X. J. Ding, and R. Zhang. "Extreme learning machine for regression and multiclass classification," IEEE Transactions Systems Man and Cybernetics Part B-Cybernetics," vol. 42, no. 2, pp. 513-529, Apr. 2012. https://doi.org/10.1109/TSMCB.2011.2168604
  25. C. Wan, Z. Xu, P. Pinson, Z. Y. Dong, and K. P. Wong. "Probabilistic forecasting of wind power generation using extreme learning machine," IEEE Transactions on Power Systems, vol. 29, no. 3, pp. 1033-1044, May. 2014. https://doi.org/10.1109/TPWRS.2013.2287871
  26. G. Y. Zhang, Y. G. Wu, K. P. Wong, Z. Xu, Z. Y. Dong, and H. H. C. Lu. "An advanced approach for construction of optimal wind power prediction intervals," IEEE Transactions on Power Systems, vol. 30, no. 5, pp. 2706-2715, Sep. 2015. https://doi.org/10.1109/TPWRS.2014.2363873
  27. N. E. Huang, Z. Shen, S. R. Long, M. L. C. Wu, H. H. Shih, Q. N. Zhang, N. C. Yen, and C. C. Tung. "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings Mathematical Physical & Engineering Sciences, vol. 454, no. 1971, pp. 903-995, Mar. 1998. https://doi.org/10.1098/rspa.1998.0193
  28. S. C. Du, T. Liu, D. L. Huang, and G. L. Li. "An optimal ensemble empirical mode decomposition method for vibration signal decomposition," Journal of Vibration & Acoustics, vol. 139, no. 3, 031003, Jun. 2017. https://doi.org/10.1115/1.4035480