LAPSE:2019.0291
Published Article
LAPSE:2019.0291
Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine
Nantian Huang, Chong Yuan, Guowei Cai, Enkai Xing
February 27, 2019
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.
Keywords
partial autocorrelation function, variational mode decomposition, weighted regular extreme learning machine, wind speed forecasting
Suggested Citation
Huang N, Yuan C, Cai G, Xing E. Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine. (2019). LAPSE:2019.0291
Author Affiliations
Huang N: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Yuan C: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Cai G: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Xing E: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
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Journal Name
Energies
Volume
9
Issue
12
Article Number
E989
Year
2016
Publication Date
2016-11-25
Published Version
ISSN
1996-1073
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PII: en9120989, Publication Type: Journal Article
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LAPSE:2019.0291
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doi:10.3390/en9120989
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Feb 27, 2019
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Feb 27, 2019
 
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Feb 27, 2019
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Calvin Tsay
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