NDLI: Multi-step wind power forecast based on VMD-LSTM

Content Provider	IET Digital Library
Author	Han, Li Zhang, Rongchang Wang, Xuesong Bao, Achun Jing, Huitian
Abstract	To improve the accuracy of multi-step wind power forecast, a variational mode decomposition-long short-term memory (VMD-LSTM) forecast method is proposed. Firstly, the variational mode decomposition method is adopted to decompose the wind power data into three constituent modes, named as the long-term component, the fluctuation component and the random component. Secondly, long short-term memory network is utilised to deeply learn the characteristics of the three constituent modes. Profit from its unique forget gate and memory gate structure, the association with long-term time series is learned to build a multi-step forecast model. Finally, the wind power data from ELIA and NERL are used to test. The error analysis shows that the proposed method has superior performance in the multi-step forecast and real-time forecast.
Starting Page	1690
Ending Page	1700
Page Count	11
ISSN	17521416
Volume Number	13
e-ISSN	17521424
Issue Number	Issue 10, Jul (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2018.5781
Journal	IET Renewable Power Generation
Publisher Date	2019-04-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Constituent Modes Error Analysis Forecasting Theory Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Least Squares Approximation Load Forecasting Long-term Component Long-term Time Series Mathematical Analysis Multistep Forecast Model Multistep Wind Power Forecast Neural Computing Technique Neural Nets Numerical Analysis Power Engineering Computing Power System Planning And Layout Real-time Forecast Recurrent Neural Nets Short-term Memory Network Statistics Time Series Variational Mode Decomposition Method Variational Mode Decomposition-long Short-term Memory VMD-LSTM Wind Power Wind Power Data Wind Power Plant
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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