A Hybrid Short-Term Wind Speed Forecasting Model Based on Wavelet Decomposition and Extreme Learning Machine

Yi Hui Zhang; He Wang; Zhi Jian Hu; Kai Wang; Yan Li; Dong Shan Huang; Wen Hui Ning; Cheng Xue Zhang

doi:10.4028/www.scientific.net/AMR.860-863.361

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 860-863A Hybrid Short-Term Wind Speed Forecasting Model...

A Hybrid Short-Term Wind Speed Forecasting Model Based on Wavelet Decomposition and Extreme Learning Machine

Abstract:

This paper studied the short-term prediction of wind speed by means of wavelet decomposition and Extreme Learning Machine. Wind speed signal was decomposed into several sequences by wavelet decomposition to reduce the non-stationary. Secondly, the phase space reconstructed was used to mine sequences characteristics, and then an improved extreme learning machine model of each component was established. Finally, the results of each component forecast superimposed to get the final result. The simulation result verified that the hybrid model effectively improved the wind speed prediction accuracy.

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Periodical:

Advanced Materials Research (Volumes 860-863)

Pages:

361-367

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.860-863.361

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Online since:

December 2013

Authors:

Yi Hui Zhang, He Wang, Zhi Jian Hu*, Kai Wang, Yan Li, Dong Shan Huang, Wen Hui Ning, Cheng Xue Zhang

Keywords:

Extreme Learning Machine, Forecasting, Phase Space Reconstruction, Wavelet Decomposition, Wind Speed

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* - Corresponding Author

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