Power Load Forecasting Based on the Locally Weighted Support Vector Machines

摘要：

Accurate forecasting of electricity load has been one of the most important issues in the electricity industry. Modern data mining methods have played a crucial role in forecasting electricity load. Support Vector Machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. In view of the recent load have greater impact on the predicted results, the paper improves traditional support vector machine, and proposes a Locally Weighted Support Vector Machines (LW-S VMs). The methodology is applied to the case of load forecasting in Inner Mongolia of China. The results shows that power load forecasting using Locally Weighted Support Vector Machine have much more accurate result than traditional Support Vector Machine.

关键词： power load forecasting data mining support vector machines locally weighted regression

作者: CAI Yongming ZHAO Shuhai

作者单位: School of Management, University of Jinan, Jinan, P.R.China, 250022

会议类型: 国际会议

会议名称: The 3rd International Institute of Statistics & Management Engineering Symposium(2010 国际统计与管理工程研讨会 IISMES)

会议地点: 威海

会议语种:英文

页码: 383-387

在线出版日期: 2010-07-24（万方平台首次上网日期，不代表论文的发表时间）