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
收藏
页码：977 / 990
页数：14
相关论文
共 39 条
[1]   A novel application of an analog ensemble for short-term wind power forecasting [J].
Alessandrini, S. ;
Delle Monache, L. ;
Sperati, S. ;
Nissen, J. N. .
RENEWABLE ENERGY, 2015, 76 :768-781
[2]   Short-term prediction of wind power using EMD and chaotic theory [J].
An, Xueli ;
Jiang, Dongxiang ;
Zhao, Minghao ;
Liu, Chao .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2012, 17 (02) :1036-1042
[3]   Wind farm power prediction based on wavelet decomposition and chaotic time series [J].
An, Xueli ;
Jiang, Dongxiang ;
Liu, Chao ;
Zhao, Minghao .
EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (09) :11280-11285
[4]   Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model [J].
Cadenas, Erasmo ;
Rivera, Wilfrido .
RENEWABLE ENERGY, 2010, 35 (12) :2732-2738
[5]   Markov chain modeling for very-short-term wind power forecasting [J].
Carpinone, A. ;
Giorgio, M. ;
Langella, R. ;
Testa, A. .
ELECTRIC POWER SYSTEMS RESEARCH, 2015, 122 :152-158
[6]  
Chen Sheng, 2011, 2011 IEEE International Conference on Advanced Power System Automation and Protection (APAP), P1705, DOI 10.1109/APAP.2011.6180647
[7]   Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm [J].
Chitsaz, Hamed ;
Amjady, Nima ;
Zareipour, Hamidreza .
ENERGY CONVERSION AND MANAGEMENT, 2015, 89 :588-598
[8]   Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN) [J].
De Giorgi, Maria Grazia ;
Campilongo, Stefano ;
Ficarella, Antonio ;
Congedo, Paolo Maria .
ENERGIES, 2014, 7 (08) :5251-5272
[9]   Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods [J].
De Giorgi, Maria Grazia ;
Ficarella, Antonio ;
Tarantino, Marco .
ENERGY, 2011, 36 (07) :3968-3978
[10]   Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model [J].
Guo, Zhenhai ;
Zhao, Weigang ;
Lu, Haiyan ;
Wang, Jianzhou .
RENEWABLE ENERGY, 2012, 37 (01) :241-249
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