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

Nantian Huang, Chong Yuan, Guowei Cai and Enkai Xing
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Nantian Huang: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Chong Yuan: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Guowei Cai: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
Enkai Xing: School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China

Energies, 2016, vol. 9, issue 12, 1-19

Abstract: 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: wind speed forecasting; variational mode decomposition; partial autocorrelation function; weighted regular extreme learning machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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