The Wind Power Forecast Model Based on Improved EMD and SVM

Guo Chu Chen; Xin Zhang; Zhi Wei Guan

doi:10.4028/www.scientific.net/AMM.694.150

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 694The Wind Power Forecast Model Based on Improved...

The Wind Power Forecast Model Based on Improved EMD and SVM

Abstract:

In order to improve the predictive accuracy of short-term wind power, a prediction model based on improved empirical mode decomposition (EMD) and support vector machine (SVM) is constructed. As to the problems of basic EMD, it is proposed to use the steady point meaning sifting method instead of spline envelope meaning sifting method, to improve the overshoots/undershoots caused by traditional cubic spline interpolation. Wind power series can be decomposed into different series by improved EMD, and then SVM is used to forecast power by each component. The total wind power prediction result is obtained through reconstructing at last. Case study shows that the predictive accuracy has significantly been improved by comparing with other models.

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Periodical:

Applied Mechanics and Materials (Volume 694)

Pages:

150-154

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.694.150

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Online since:

November 2014

Authors:

Guo Chu Chen*, Xin Zhang, Zhi Wei Guan

Keywords:

EMD, Forecast, Model, SVM, Wind Power

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* - Corresponding Author

References

[1] Jin Li, Jiahua Song, Weisheng Wang. Proceedings of the CSEE (in Chinese), Vol. 24 (2004), p.100.

Google Scholar

[2] Yazhou Lei, Weisheng Wang, Huizhu Dai. Power system technology (in Chinese), Vol. 26 (2002), p.10.

Google Scholar

[3] Gaofeng Fan, Haixiang Zhao, Huizhu Dai. Power system and clean energy (in Chinese), Vol. 24 (2008), p.44.

Google Scholar

[4] HUANG N E. The empirical mode decomposition and the Hilbert spectrum for nonlinear an non-stationary time series analysis [J]. Proe R Soc London, Vol. 454 (1998), p.903.

Google Scholar

[5] Jing Ning, Changlin Zhu, Pinxian Gao. Computer engineering and applications (in Chinese), Vol. 47 (2011), p.125.

Google Scholar

[6] Peng Wang, Guochu Chen, Yufa Xu. Control engineering of China (in Chinese), Vol. 18 (2011), p.588.

Google Scholar

[7] Youming Zhong. Theory of Hilbert Huang transform local instantaneous signal analysis[D]. Chongqing University, (2002).

Google Scholar

[8] Rilling G, Flandrin P．On the influence of sampling on the empirical mode decomposition[C] /Proc 2006 IEEE International Conference on Acoustics, Speech and Signal proccssha8(ICASSP 2006), Toulouse, France, Vol. 3 (2006), p.444.

DOI: 10.1109/icassp.2006.1660686

Google Scholar

[9] Vapnik V. Statistical learning theory [M]. New York: John Wiley. (1998).

Google Scholar

[10] Peiyi Zhou, Xinyan Zhang. Shanxi electric power (in Chinese), vol. 12 (2009), p.1.

Google Scholar