Short Term Load Forecasting Based on Optimized VMD-mRMR
WANG Shudong
CHEN Yong
TANG Weiqiang
CHEN Wangsheng
Abstract：To address the issue of low prediction accuracy caused by the lack of consideration for the correlation and eigenval-ues of time-series data in traditional load forecasting,this paper proposes a combination model based on optimized variational mode decomposition,maximum correlation minimum redundancy,and gate recurrent unit.Firstly,genetic algorithm is used to optimize the key parameters of variational modal decomposition,decomposing the original load sequence into components of different frequen-cies.Secondly,the optimal feature set for each component is selected using the maximum correlation minimum redundancy method.Finally,the key parameters of the gate recurrent unit are optimized using the monkey algorithm,and each component is predicted separately.The predicted values of each component are superimposed to obtain the final predicted value.By using the data from Aus-tralia for prediction and comparing it with other methods,the results show that this method has higher prediction accuracy.
Keywords：variational modal decompositionmaximum correlation and minimum redundancymonkey algorithmgate re-current unitload forecasting
Publication Date：2025-04-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1020-1024,1043 )