Multi-step wind power forecast based on VMD-LSTM (Englisch)
Freier Zugriff
- Neue Suche nach: Han, Li
- Neue Suche nach: Zhang, Rongchang
- Neue Suche nach: Wang, Xuesong
- Neue Suche nach: Bao, Achun
- Neue Suche nach: Jing, Huitian
- Neue Suche nach: Han, Li
- Neue Suche nach: Zhang, Rongchang
- Neue Suche nach: Wang, Xuesong
- Neue Suche nach: Bao, Achun
- Neue Suche nach: Jing, Huitian
In:
IET Renewable Power Generation
;
13
, 10
;
1690-1700
;
2019
- Aufsatz (Zeitschrift) / Elektronische Ressource
-
Titel:Multi-step wind power forecast based on VMD-LSTM
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Beteiligte:Han, Li ( Autor:in ) / Zhang, Rongchang ( Autor:in ) / Wang, Xuesong ( Autor:in ) / Bao, Achun ( Autor:in ) / Jing, Huitian ( Autor:in )
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Erschienen in:IET Renewable Power Generation ; 13, 10 ; 1690-1700
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Verlag:
- Neue Suche nach: The Institution of Engineering and Technology
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Erscheinungsdatum:07.05.2019
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Format / Umfang:11 pages
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ISSN:
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DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Englisch
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Schlagwörter:multistep forecast model , wind power , short-term memory network , wind power data , learning (artificial intelligence) , mathematical analysis , VMD-LSTM , long-term time series , long-term component , neural nets , error analysis , variational mode decomposition method , load forecasting , time series , least squares approximations , constituent modes , real-time forecast , wind power plants , forecasting theory , power engineering computing , recurrent neural nets , multistep wind power forecast , variational mode decomposition-long short-term memory
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Datenquelle:
Metadata by IET is licensed under CC BY 3.0
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