Application And Research Of Short-term Load Forecasting Method

The normal operation of the power system is related to the national economy, the main topic of how to ensure the normal operation of the power system current and future research work.And power system load forecasting in order to ensure the normal operation of the power system, provides a reliable basis, especially short-term power load forecasting.The prediction accuracy of electric power system of good or bad influences electric power system and its economic value and operation safety of power supply quality. At present, there are many load forecasting model, both single model and combination prediction model.This paper describes the research meaning and background of short-term power system load forecasting and the concepts and characteristics of the load forecasting, analyzing the composition, characteristics and regularity of the power load, and citing several forecast evaluation index. On this basis, the method of time series and artificial neural network model of short-term load forecasting method is analyzed. Time series is divided into stationary time series and non-stationary time series, accordingly, the different specific time series forecasting method are proposed, and the steps of time series models is pointed out. The paper discusses the concept, features and mathematical model of the combination forecasting method. Several common fixed combination of prediction methods are pointed out, such as equally weighted average combination of forecasts, determining the weight coefficients based on the error level and the formula and steps are described. The combined model of fuzzy variable weight coefficient method and neural network combination forecasting method of variable weight coefficient prediction model are analyzed. In order to overcome the shortcomings of the BP neural network, two prediction methods are proposed on the base of and PSO(particle swarm optimization)-WNN(wavelet neural network).And fuzzy clustering analysis method is introduced to filer the training samples of wavelet neural network. The prediction accuracy of those two methods has been improved, and particle swarm optimization is very important. The structural characteristics network learning process, and prediction methods of the echo state networks in a new recurrent neural are described. In the light of chaos analysis of the power load,and on the basis of the power load phase space reconstruction, a short-term load forecasting method based on echo state networks is proposed. The effectiveness of this method in power load forecasting is showed through experimental simulation. And a short-term load combinations prediction model based on echo state network is proposed. Simulation results shows that this model is very good.