Study Of Short-term Power Load Forecasting Based On Modified Pso-bp Neural Network Model

Electric system load-forecasting is one of basis in the guide on electric system planning problem,economical running and dispatcher automation.It's accuracy directly influence power systems security's security,profit and quality.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 combination forecasting can make use of information provided by single forecasting models.We got combination forecasting model with an appropriate combination form to improve the forecasting accuracy.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 prevision are analyzed.And then it makes a summary of many traditional and modern load-forecasting technologies.To solves the problems of BP neural network convergence rate slow and easily falling into partial minimum.The modified particle swarm optimization (MPSO) and Back propagation (BP) neural network algorithm was presented.Considering weather,date and other factors,MPSO-BP algorithm can be used for training the neural network.Experimental results show that can quicken the learning speed of the network,and improve the predicting precision.Improves BP neural network generalization capacity.The model can be used to forecast the short-term load of the power system.