Forecasting increasing rate of power consumption based on immune genetic algorithm combined with neural network

来源期刊：中南大学学报(英文版)2008年第z2期

论文作者：杨淑霞

文章页码：327 - 330

Key words：immune genetic algorithm； neural network； power consumption； increasing rate； forecast

Abstract: Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.