LAUSR

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.