The Study On Short-term Electric Load Forecasting Based On PSO-BP

The short-term load forecasting of electric power system, Predicting electric load for a period of hours, days, or weeks, is a important research area of electric power system' s operation. It is an important foundation of the study on electric system planning problem, economical running and dispatcher automation. Furthermore, with the establishment of power market, short-term load forecasting will play a more important role in the future. With the power system becoming more and more complex, it's demonstrated that those traditional load-forecasting technologies can't satisfy the requirement of load forecasting accuracy, which becomes more and more strict. So using intelligent technologies to improve the forecasting accuracy and stability of the load forecasting of electric power system is a new character of short-term load forecasting field of electric power system.Firstly, the principle, features, current status and development of the electric power system short-term load forecasting are generalized in the thesis, the impact factors of forecasting-preoision are analyzed. And then it makes a summary of many traditional and modern load-forecasting technologies, especially, introduces the application of ANN in electric power system short-term load forecasting. In order to solve the problems of the slow rate of convergence and falling easily into local minimum in the BP algorithm, an improved PSO-BP algorithm was used in the paper to train the weights of ANN.Simulation results show that, the neural network forecasting model based on PSO (PSO-BP) created in this thesis can greatly improve forecasting precision and speed, and its forecasting capability is obviously better than the neural network model based on BP algorithm.