A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction

被引：87

作者：

Wang, Cong ^{[1
]}

Zhang, Hongli ^{[1
]}

Fan, Wenhui ^{[2
]}

Ma, Ping ^{[1
]}

机构：

[1] Xinjiang Univ, Sch Elect Engn, Urumqi 830047, Xinjiang, Peoples R China

[2] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

来源：

ENERGY | 2017年 / 138卷

基金：

中国国家自然科学基金;

关键词：

Wind power prediction; EEMD-SE; Full-parameters continued fraction; Primal dual state transition algorithm; EMPIRICAL MODE DECOMPOSITION; SUPPORT VECTOR MACHINE; NEURAL-NETWORK; SWARM OPTIMIZATION; WAVELET TRANSFORM; ENSEMBLE; ALGORITHM; SPEED; SELECTION; SVM;

D O I：

10.1016/j.energy.2017.07.112

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

The wind power time series always exhibits nonlinear and non-stationary features, which make it very difficult to predict accurately. In this paper, a new chaotic time series prediction model of wind power based on ensemble empirical mode decomposition-sample entropy (EEMD-SE) and full-parameters continued fraction is proposed. In this proposed method, EEMD-SE technique is used to decompose original wind power series into a number of subsequences with obvious complexity differences. The forecasting model of each subsequence is created by full-parameters continued fraction. On the basis of the inverse difference quotient continued fraction, the full-parameters continued fraction model is proposed. The parameters of model are optimized by the primal dual state transition algorithm (PDSTA). The effectiveness of the proposed approach is demonstrated with practical hourly data of wind power generation in Xinjiang. A comprehensive error analysis is carried out to compare the performance with other approaches. The forecasting results show that forecast improvement is observed based on EEMD-SE and full-parameters continued fraction model. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：977 / 990

页数：14

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