RBF Neural Network Wind Power Prediction Based on Chaos Theory

Li Dong Zhang; Shan Shan Li; Xu Dong He

doi:10.4028/www.scientific.net/AMR.1070-1072.315

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1070-1072RBF Neural Network Wind Power Prediction Based on...

RBF Neural Network Wind Power Prediction Based on Chaos Theory

Abstract:

Using the C - C method to reconstruct the phase space of wind power time series, get the maximum wind power time series Lyapunov exponent, confirmed that the wind power time series have chaotic characteristics. Followed by the radial basis function (RBF) neural network model for wind power chaotic local multi-step prediction, results show that the prediction effect is better than that of the predicted effect of 48 hours for 24 hours.

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Periodical:

Advanced Materials Research (Volumes 1070-1072)

Pages:

315-318

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1070-1072.315

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Online since:

December 2014

Authors:

Li Dong Zhang*, Shan Shan Li, Xu Dong He

Keywords:

Chaos Theory, Phase Space Construction, RBF Neural Network, Wind Power Prediction

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* - Corresponding Author

References

[1] L.Y. Zhang, T.L. Ye, Y. ZH. Xin, et al. The related problems of large scale wind power integration and measures[J]. China Journal of Electrical Engineering，2010，30(25): 1-9(In Chinese).

Google Scholar

[2] SH. Han. Studies on the methods of short-term wind power prediction[D]. Beijing：North China Electric Power University, 2008(In Chinese).

Google Scholar

[3] Y.M. Zhang S.R. Qu, K.G. Wen. Short term traffic flow chaotic and prediction based on RBF neural network[J]. Journal of Northwestern Polytechnical University, 2010，25(11): 4-8(In Chinese).

Google Scholar

[4] M. Han. The theory and method of predicting chaotic time series[M]. Beijing：Chinese water conservancy and Hydropower Press, 2007: 24-155(In Chinese).

Google Scholar

[5] J.H. Lu, J.A. Lu, S.H. Chen. The analysis of chaotic time series and its application[M]. Wuhan: Wuhan University press, 2002(In Chinese).

Google Scholar

[6] A. Wolf, J.B. Swift, H.L. Swinney, et al. Determining Lyapunov exponents from a time series[J]. Physica D：Nonlinear Phenomena，1985，16(3): 285-317.

DOI: 10.1016/0167-2789(85)90011-9

Google Scholar