SHORT TERM LOAD FORECASTING USING A MULTILAYER NEURAL NETWORK WITH BP-GA MIXED ALGORITHMS

In this paper, a modified method (BP-GA) for short-term load forecast is presented, which can quicken the learning speed of the network and improve the predicting precision compared with the traditional artificial neural network. We use GAs to train connection weights of multi-layer feed forward neural network (BP) until the learning error has tended to stability, here, the best initial weights have been found. Then we use BP method to finish short-term load forecast process. We also consider the influence of climate for the short-term load and make it as one of the input for the BP. Experimental results show that the short-term load forecast system based on BP-GA has high precision and high learning rate.