Short-term load forecasting based on variational modal decomposition and optimization model

Accurate load forecasting is of great significance to ensure the safety and stable operation of the smart grid's power system. In this paper, we study this problem of making an accurate short-term load prediction for an electricity system. Due to its inherent nonlinear properties, our proposed solution is based on the nonlinear model, and the power load sequence is also random, non-stationary and periodic. The original historical load sequence is decomposed into a series of modal functions by using the variational mode decomposition (VMD) technique, and a load forecasting model is established for each modal function. A load forecasting model combining the beetle swarm optimisation(BSO) algorithm and extreme learning machine(ELM) is put forward. The BSO finds the optimal input weight and hidden layer threshold in the ELM network. In the end, the VMD-BSO-ELM prediction model is established. The experimental results demonstrate that the proposed method is better than other individual methods and their combinations.