A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply-demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on variational mode decomposition and long short-term memory with seasonal factors elimination and error correction is proposed in this article. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models.

Automated summary

This article proposes a hybrid model based on variational mode decomposition and long short-term memory, which improves the accuracy of load forecasting by eliminating seasonal factors and correcting errors. Experimental results demonstrate the significant performance of the proposed model.