POWER DEMAND FORECAST BASED ON OPTIMIZED NEURAL NETWORKS BY IMPROVED GENETIC ALGORITHM

摘要：

Power demand forecast is the basis for making power development plan. Through analyzing the factors, which affect power demand, one model for forecasting power demand has been established, and its data are standardized firstly. Then by designing the structure of BP neural networks and applying the improved genetic algorithm, the network structure and weights of neural networks for power demand are optimized.Finally through training the data from 1980 to 2004 in China,a non-linear relation model between power demand and its influential factors is obtained. The method avoids the shortcomings such as the slow speed of obtaining the optimal solution by genetic algorithm and easily trapping into local optimal solution by the neural networks. The result shows that the method is accurate and feasible.

关键词： Power Demand Forecast Neural Networks Genetic Algorithm

作者: SHU-XIA YANG NING LI

作者单位: School of business administration, North China Electric Power University 102206, Beijing

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2877-2881

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）