Abstract:Due to the electric power load is the basis of development of electric power system, our work intents to improve predicting accuracy of electric power load is beneficial to the development of electric power system. This paper by using the good performance of particle swarm algorithm to optimize the parameters and the advantage of grey prediction method for forecasting uncertainty factors affecting the system puts forward the grey mutation particle swarm combination forecasting model to predict urban public transit volume, improved the electric power load accuracy. Also through the examples analyzed prediction accuracy and effectiveness of the combination forecast model. The results show that the accuracy of the combination forecast model is better than that a single gray of forecasting model and other prediction algorithms, this model can well predict urban public transit volume which provides a reliable scientific data for the decision and development of electric power system.