Ultra-Short-Term Prediction of Wind Power Based on VMD-PE-MulitiBiLSTM
In order to reduce the error of ultra-short-term wind power prediction, an ultra-short-term prediction model of wind power based on variational mode decomposition (VMD), permutation entropy (PE) and multilayer bidirectional long short-term memory (MultiBiLSTM) is proposed. Firstly, the historical wind power sequence is decomposed into several sub-modal components using VMD decomposition algorithm, and the sub-modal wind power components are reconstructed according to the calculated PE value. Then, the feature attention (FA) mechanism and deep residual cascade network (DRCnet) are used to construct a MulitiBiLSTM prediction model to predict the reconstructed subsequences. Finally, the predicted value of the sub-sequence is reconstructed to obtain the final prediction result of wind power. The datum set of a wind field in Guizhou province is used to verify the proposed method and compare it with other prediction models. The results show that using VMD-PE-MultiBiLSTM model can significantly reduce the prediction error of wind power.
ultra-short-term prediction of wind powervariational mode decomposition (VMD)permutation entropy (PE)multilayer bidirectional long short-term memory (MultiBiLSTM)
万方数据
维普
