基于小波变换的风电场短期风速组合预测

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Abstract In order to improve short-term wind speed prediction accuracy of wind farms,a combined prediction method based on the wavelet transform is proposed.Firstly,the db3 wavelet is used for three-layer decomposition and reconstruction for short-term wind speed time series through Mallat algorithm.The approximation components and the detail components of the short-term wind speed are then obtained.Next,according to the different characteristics of these components,the least square support vector machine optimized by particle swarm algorithm and the autoregressive integrated moving average model are adopted as the predictive models for the approximate components and the detail components respectively.Then,the final predictive value of the short-term wind speed is obtained by the combination of the two components.The simulation results indicate that higher accuracy can be obtained in this prediction method.

Key words： Short-term wind speed wavelet transform auto regressive integrated moving average least square support vector machine combined prediction

Received: 04 December 2014 Published: 22 May 2015

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TM614

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	Tian Zhongda
	Li Shujiang
	Wang Yanhong
	Gao Xianwen

Cite this article:

Tian Zhongda,Li Shujiang,Wang Yanhong等. Short-Term Wind Speed Combined Prediction for Wind Farms Based on Wavelet Transform[J]. Transactions of China Electrotechnical Society, 2015, 30(9): 112-120.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2015/V30/I9/112

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