Abstract: The operation of power system is influenced by many factors and the power load data present strong volatility and instability affecting the accuracy of short-term load forecasting of the power grid. Traditional power load forecasting methods have increased prediction error when affected by nonlinear factors such as policy, weather and holidays, so a combined model strategy is proposed. Firstly, the original data is decomposed into several components by EEMD (Ensemble Empirical Mode Decomposition), and the components are divided into two groups according to the different information content of each component data, and the prediction is carried out by using back propagation neural network and short-and-long memory network respectively. A SSA ( Salp Swarm Algorithm) is used to optimize the number of neurons and the lag term of input variables in each component prediction network, and the final combined forecast model of EEMD-SSA is obtained. Finally, a real in-situ data is used with this combined model to forecast power load. The experimental results show that this combined model has better prediction effect than single network model and other models. 

Key words: load forecasting, combined model, ensemble empirical mode decomposition ( EEMD ) decomposition, salp swarm algorithm (SSA) optimization algorithm