SHORT TERM LOAD FORECASTING BASED ON BP NEURAL NETWORK TRAINED BY PSO

摘要：

A short-term load forecasting method based on BP neural network which is optimized by particle swarm optimization (PSO) algorithm is presented in this paper.PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization.Here, real load and weather data from the Xingtai power plant databases used as inputs to the neural network, which has been trained by PSO, are employed to illustrate the presented model.The experimental results prove that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional BP method and show that the method is not only simple to calculate, but also practical and effective.

关键词： Short term load forecasting BP neural network Particle swarm optimization

作者: WEI SUN YING ZOU

作者单位: School of Business Administration, North China Electric Power Univ., Baoding 071002, China

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2863-2868

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）