MID-TERM LOAD FORECASTING BASED ON DYNAMIC LEAST SQUARES SVMS

摘要：

In this study, a dynamic model based on least squares support vector machines is proposed to forecast the daily peak loads of a month. The model function is got from the training data set using least squares support vector machines. In the time series prediction process, new data points are included into training data set and some of the old ones are deleted, so as to track the dynamics of the nonlinear time-varying feature of load demand. The electricity load data from European Network on Intelligent Technologies (EUNITE) network competition are used to illustrate the performance of the proposed dynamic least squares support vector machines. The experimental results reveal that the proposed model outperforms the least squares support vector machines, which outperforms the support vector machine. Consequently, the dynamic least squares support vector machines provides a promising alternative for forecasting mid-term electricity load in power industry.

关键词： Least squares support vector machines Dynamic least square support vector machines Load forecasting

作者: DONG-XIAO NIU WEI LI LI-MIN CHENG XI-HUA GU

作者单位: Department of Economics and Management, North China Electric Power University, Beijing, 102206, Chin Department of Economics and Management, North China Electric Power University, Baoding, 071003, Chin

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 800-804

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）