Short-Term Load Forecasting Of Power System Based On RBF Neural Network And Fuzzy Theory

Short-term load forecasting is important basis of safely assigning and economically running. The forecasting precision will directly affect the reliability, economy running and supplying power quality of power system. So finding an appropriate load forecasting method to improve the accuracy of precision has important application value.According to the rule of change of load characteristic, after calculating the factors such as date type, temperature, weather status etc which influencing the load forecasting a forecasting method based on radial basis function neural networks and fuzzy theory. At first, considering load seasonal variation, forecasting models are established to forecast each season including spring, summer, autumn and winter respectively, using fuzzy clustering analysis method, the clustering analysis of the related data of load forecasting are carried out, the data of the same property are used as the input of neural network, train RBF neural network to predict short-term load. Secondly, in order to eliminate forecast error, on-line self-tuning fuzzy control is used, make prediction model adapt to real-time change. Finally, Electricity price is also factor which must be considered in load forecasting model in future power market environment. in order to overcome the defect of the RBF network in power market environment, the article first draws on the nonlinear approaching capacity of the RBF network to forecast the load on the prediction day which takes no account of the factor of electric price, and then, based on the recent changes of real-time price, uses the fuzzy control system to modify the results of load forecasting obtained by using the RBF network.Practical examples indicate that the forecasting method is convenient and practical, possesses a good convergence, greater forecasting accuracy and faster training speed.