A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM...
A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA
About this item
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Author / Creator
Lu, Peng , Ye, Lin , Sun, Bohao , Zhang, Cihang , Zhao, Yongning and Teng, Jingzhu
Publisher
Basel: MDPI AG
Journal title
Language
English
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Publication information
Publisher
Basel: MDPI AG
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Scope and Contents
Contents
Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitatio...
Alternative Titles
Full title
A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_fa6d08c2ec5a48f1ac0faddd732c14a3
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_fa6d08c2ec5a48f1ac0faddd732c14a3
Other Identifiers
ISSN
1996-1073
E-ISSN
1996-1073
DOI
10.3390/en11040697
