Short-term Load Forecasting Based On Fuzzy Neural Network

Short-term load forecasting is the precondition of economic and secure operation of power system. 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 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 the short-term load forecasting field of electric power system. After analyzed the meaning and method of power system load forecasting, based on the analysis of fuzzy inference and neural network biology characteristic, the paper presents a new method of constructing Fuzzy Neural Network (FNN), which merges the fuzzy inference into BP network. In this method, GA is used to optimize connection weights of forward-back neural network until the learning error has tended to stability. Then we use BP algorithm with optimized weights to finish short-term load forecasting process. The results of the emulation experimental show that this method can quicken the learning speed of the network and improve the predicting precision.