Short-term Load Forecasting Considering VMD Residuals and Optimizing BiLSTM
This study proposes a new method to improve short-term load forecasting accuracy.The method is based on Variational Modal Decomposition(VMD)with consideration of VMD re-siduals and an Improved Northern Eagle Algorithm(INGO)optimized Bi-directional Long Short Term Memory(BiLSTM)network.The VMD is used to decompose historical load data into multiple eigenmode components(IMFs)and a residual quantity.The BiLSTM model is then con-structed separately for each IMF and residual,as well as the associated meteorological parame-ters.To avoid the impact of poorly selected hyperparameters on prediction accuracy,the INGO algorithm optimizes the implied layer nodes,training times,and learning rates of the BiLSTM.Last but not least,the prediction results are superimposed to obtain the final results.By analy-zing specific cases,this paper's method has demonstrated a higher prediction precision when com-pared to alternative methods.This validation confirms the effectiveness of the method presented in this article.
short-term load forecastingvariational mode decompositionnorthern goshawk opti-mizationbi-directional long short-trem memory
万方数据
维普
