VMD-KELM Short-term Load Forecasting Based on Improved Hunter-prey Optimizer

In order to further improve the accuracy and reliability of load forecasting, a forecasting model based on variational mode decomposition(VMD) and Levy-hunter-prey optimizer(LHPO) improved by population chaos strategy and Levy flight strategy is proposed to optimize kernel extreme learning machine(KELM), in view of the shortcomings of the KELM parameter selection that affects the forecasting ability and the characteristics of the volatility and non-stationary of load data. Firstly, the environmental factors and load data of the original data are analyzed by using the grey relational analysis. Then, VMD is used to decompose the load data, and each decomposition subsequence is input into the KELM model optimized by LHPO for short-term load forecasting. Finally, each prediction result is stacked. Simulation experiment results show that the prediction model has high adaptability to short-term load forecasting.