The Research Of Short-Term Load Forecasting Based On RBF Neural Networks And Fuzzy Control Of Power System

Short-Term Load Forecasting (STLF) is one of the most important contents of running and dispatching of power system. It can be economic and reasonable to arrange start and stop of the Generator in wire net. It can be reasonable to arrange Generator the maintain plan, the national energy development strategy requests gradually to reduce energy consumption of the unit GDP, the production and consumption of electric power increasingly go to market, short-term load forecasting result become importance basis of drawing up the electric power market bargain plan. So these put short-term load forecasting forward a higher request.The normal calculate way can not reflect goodly weather condition and other outside factors to the influence for load forecasting, in recent years, the artificial neural network method etc have height nonlinear to reflect the ability of shoot,can reflect goodly the weather factor etc. So this text adopt radial basis function neural networks. But lack of the general rule that instruct model to chooses automatically in the speed, stability and the overall situation to order smallest aspects of studying to refraining from rash action, so this text author put fuzzy control to the RBF neural network, resolve the above-mentioned problem more goodly. This text analyze the present condition and various methods and mathematics model of the short- term load forecasting. 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. The implemented program based on the proposed method is used for the STLF in the actual network, the testing results illustrate that the forecasting accuracy is satisfactory, accordingly it shows the validity and practicability of the method, and have better applied foreground.