Traffic Prediction Based on Grey Model Optimized by Buffer Operator and PSO in Communication Network for Electric Power

To meet the requirements of planning and to improve accuracy and stability of traffic prediction model in the communication network for electric power, a traffic prediction method based on grey model optimized by buffer operator and particle swarm optimization (PSO) is proposed in this paper. Variable weights buffer operators are implemented for preprocessing traffic data to enhance the adaptability of gray prediction model. Taking the maximum grey correlation degree between prediction series and true series as objective function, based on the search ability of PSO, the fitness function is founded, which can determine the optimal parameters of gray model. Applying the improved model to traffic prediction in communication network for electric power, a new prediction result is drawn. The prediction result shows that the improved model has higher prediction accuracy compared with the traditional GM (1, N) model.