THE NEURAL NETWORK MODEL BASED ON PSO FOR SHORT-TERM LOAD FORECASTING

摘要：

A new algorithm for load forecasting--the neural network model based on Particle Swarm Optimization (PSO-NN) for short-term load forecasting is proposed in this paper. The method is simple, easy to realize and its convergence rate is quick. The overall optimal solution of the problem can be found in great probability, and the intrinsic defects of artificial neural network, such as slow training speed and the existence of local minimum points, can be effectively overcome.Simulation results show that forecasting precision and speed can be improved by this method, and its forecasting capability is obviously better than the neural network model based on BP algorithm (BP-NN).

关键词： Load forecasting particle swarm optimization neural network training algorithm

作者: WEI SUN YING-XIA ZHANG FANG-TAO LI

作者单位: Department of Economy and Management, North China Electric Power University, Baoding 071003, China Department of Engineering, Jiangsu Huaneng Huaiyin Power Plant, Huaian 223001, China

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: 3069-3072

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）