Multi‐step wind power forecast based on VMD‐LSTM

• DOI: 10.1049/iet-rpg.2018.5781
• Publication Date: 2019-04-13T02:22:58Z
• Authors: Li Han; Rongchang Zhang; Xuesong Wang; Achun Bao; Huitian Jing
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 7. Clean energy

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.