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Energies 2016, 9(12), 989; doi:10.3390/en9120989

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

School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
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Author to whom correspondence should be addressed.
Academic Editor: Ying-Yi Hong
Received: 3 August 2016 / Revised: 20 November 2016 / Accepted: 22 November 2016 / Published: 25 November 2016
(This article belongs to the Special Issue Electric Power Systems Research 2017)
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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. View Full-Text
Keywords: wind speed forecasting; variational mode decomposition; partial autocorrelation function; weighted regular extreme learning machine wind speed forecasting; variational mode decomposition; partial autocorrelation function; weighted regular extreme learning machine
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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. Energies 2016, 9, 989.

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