Multi-Objective Gray Wolf Cooperative Optimization of VMD-LSTM Parameters for Load Forecasting and Its Application

To address the issue of inaccurate load forecasting affecting the effectiveness of minimum demand scheduling in railway traction stations, this study introduces a multi-objective grey wolf optimizer (MOGWO) to jointly optimize the parameters of variational mode decomposition (VMD) and long short-term memory network (LSTM) within the forecasting framework. The proposed MOGWO-VMD-LSTM model enhances the data decomposition capability of VMD and improves LSTM training, prediction accuracy, and inverse normalization reconstruction. Using a 10-day load dataset from a traction station, the model’s performance is compared against LSTM and VMD-LSTM baselines. Simulation results demonstrate superior performance in terms of mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) metrics. Application of the forecasting results to traction station scheduling reduces the single-peak power purchase from 22.279 MW to 20.052 MW, achieving a 9.995% reduction, indicating strong practical potential.