Short-term wind power prediction based on VMD-ISSA-GRU comprehensive model
In order to solve the problem of low accuracy of wind power prediction caused by wind speed uncertainty and volatility,this paper proposes a VMD-ISSA-GRU combination model based on variational mode decomposition(VMD),improved sparrow search algorithm(ISSA)and gated recurrent neural network(GRU).Firstly,the center frequency method is used to determine the number of modal components after VMD decomposition,which can effectively avoid over-decomposition or insufficient decomposition.Then,chaotic mapping,nonlinear decreasing weights and a mutation strategy are introduced to improve the sparrow search algorithm to optimize the gated recurrent neural network,and then an ISSA-GRU prediction model is established for each decomposed subsequence.Finally,the predicted value of each subseries is superimposed and the final predicted value is obtained.The experimental results show that,the mean absolute error,mean absolute percentage error and root mean square error of the VMD-ISSA-GRU model are 1.211 8,1.890 0 and 1.591 6 MW,respectively.Compared with the conventional GRU,long short-term memory(LSTM)neural network,Bi-directional LSTM(BiLSTM)neural network model and other combination models,the prediction accuracy has been significantly improved,which can solve the problem of low prediction accuracy of wind power.
万方数据
维普
