Short-term Load Forecasting Based on Empirical Modal Decomposition and Optimized BiLSTM
Aiming at the problem of nonlinearity and instability of load data,an EMD-ISSA-BiLSTM prediction model based on the combination of empirical modal decomposition-improved sparrow search al-gorithm-bidirectional long and short-term memory neural network is proposed.Firstly,EMD is used to process the nonlinear load data,and the original load data are decomposed into several different scales of in-trinsic modal functions(IMFs)and residuals(Res),and the inverse learning strategy and the Levy flight strategy are introduced to improve the convergence speed and local optimization problem of the Sparrow Search Algorithm(SSA),respectively,and the Improved Sparrow Search Algorithm(ISSA)is utilized to perform BiLSTM neural network parameter search optimization.Then the optimized BiLSTM model is used to predict each component,and the prediction results of each prediction are superimposed and com-bined to obtain the prediction results of the whole load sequence.Finally,through the analysis of actual ca-ses,it is proved that this method has better prediction accuracy and stability than the traditional prediction methods,and can be used as an effective short-term load prediction method.
万方数据
维普
