期刊
ENERGIES
卷 9, 期 12, 页码 -出版社
MDPI AG
DOI: 10.3390/en9120989
关键词
wind speed forecasting; variational mode decomposition; partial autocorrelation function; weighted regular extreme learning machine
资金
- National Nature Science Foundation of China [51307020]
- Science and Technology Development Project of Jilin Province [20160411003XH]
- Science and Technology Foundation of Department of Education of Jilin Province [90]
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.
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