Study of Support Vector Machines Based on immunogenetic particle swarm algorithm in Short-Term Power Load Forecasting Model

摘要：

Accurate power load forecasting is important for electric power system, it must guarantee its economical and safe operation. In this article, an improved support vector machine mode was applied in predicting the load forecasting and calculating the optimum solution of the SVM model by new immunogenetic particle swarm algorithm. Applying the presented forecasted method to actual load forecasting and the comparing among the forecasted results single SVM and BP method, it is shown that the presented forecasting method is more accurate and efficient.

作者: Dongxiao Niu Yongli Wang

作者单位: Department of Economy and Management North China Electric Power University Beijing 102206,China

会议类型: 国际会议

会议名称: The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

会议地点: 上海

会议语种:英文

页码: 4680-4683

在线出版日期: 2008-05-16（万方平台首次上网日期，不代表论文的发表时间）