Short Term Load Forecasting Based on the Particle Swarm Optimization with Simulated Annealing

摘要：

This paper presented an artificial neural network (ANN) method based on the particle swarm optimization (PSO) and simulated annealing (SA) for load forecasting. Using the modified PSO with SA train the ANN network and facilitate the tuning of the optimal network weight and threshold. The ANN network has a better ability to escape from the local optimum and is more effective than the conventional PSO-based ANN. Then use the network to forecast the daily load.Simulation example shows that the proposed approach has good accuracy.

关键词： short term load forecasting artificial neural network particle swarm optimization,simulated annealing.

作者: Mengliang Liu Jing Tang

作者单位: School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, P. R. The Middle School Attached to Shandong normal university, Jinan, 250014, P. R. China

会议类型: 国际会议

会议名称: The 1st International ELID-Grinding Conference(第一届镜面磨削技术国际会议)

会议地点: 长沙

会议语种:英文

页码: 397-400

在线出版日期: 2008-06-12（万方平台首次上网日期，不代表论文的发表时间）