APPLICATION OF ARTIFICIAL FISH-SWARM NEURAL NETWORK IN SHORT-TERM LOAD FORECASTING OF POWER SYSTEM
MA Jian-wei,ZHANG Guo-li (North China Electric Power University,Baoding 071003,Hebei Province,China)
As a non-linear optimal problem short-term load forecasting impacts on economic benefit of power system greatly. Based on the individual local searching the artificial fish-swarm algorithm (AFSA) is an up-to-date proposed optimal strategy, which possesses good capability to avoid the local extremum and obtain the global extremum. Here, a new artificial neural network (ANN) based forecasting model using AFSA is built in which the weights of ANN are trained by AFSA, then the neural network using AFSA is applied to short term load forecasting. Applying the presented forecasting method to a certain actual power network it is shown that comparing with traditional BP neural network forecasting method the presented forecasting method has better adaptive ability and can give better forecasting result.
【CateGory Index】： TM715