Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

被引:26
|
作者
Huang, Nantian [1 ]
Yuan, Chong [1 ]
Cai, Guowei [1 ]
Xing, Enkai [1 ]
机构
[1] Northeast Elect Power Univ, Sch Elect Engn, Jilin 132012, Jilin, Peoples R China
来源
ENERGIES | 2016年 / 9卷 / 12期
关键词
wind speed forecasting; variational mode decomposition; partial autocorrelation function; weighted regular extreme learning machine; ARTIFICIAL NEURAL-NETWORKS; SUPPORT VECTOR REGRESSION; AUTOREGRESSIVE MODELS; TIME-SERIES; PREDICTION; ALGORITHM; ENSEMBLE;
D O I
10.3390/en9120989
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD), the partial autocorrelation function (PACF), and weighted regularized extreme learning machine (WRELM), is proposed to improve the accuracy of wind speed forecasting. First, the historic wind speed time series is decomposed into several intrinsic mode functions (IMFs). Second, the partial correlation of each IMF sequence is analyzed using PACF to select the optimal subfeature set for particular predictors of each IMF. Then, the predictors of each IMF are constructed in order to enhance its strength using WRELM. Finally, wind speed is obtained by adding up all the predictors. The experiment, using real wind speed data, verified the effectiveness and advancement of the new approach.
引用
收藏
页数:19
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