Support Vector Machines Based on Data Mining Technology in Power Load Forecasting

摘要：

This system mines the historical daily loading which has the same meteorological category as the forecasting day in order to compose data sequence with highly similar meteorological features, with this method it can decrease SVM training data and overcome the disadvantage of very large data and slow processing speed when constructing SVM model. With the advantage of data mining technology in processing, it can reduce the large data and eliminate redundant information. Comparing with single SVM and BP neural network in short-term load forecasting, this new method can achieve greater forecasting accuracy. It is denoted that the SVM learning system has advantage when the information preprocessing based on data mining technology.

关键词： data mining meteorological factor support vector machines Short-term load forecasting

作者: Dong-xiao Niu Yong-li Wang

作者单位: Institute of Business Management North China Electric Power University, 102206 Beijing, China

会议类型: 国际会议

会议名称: 第三届IEEE无线通讯、网络技术暨移动计算国际会议

会议地点: 上海

会议语种:英文

在线出版日期: 2007-09-21（万方平台首次上网日期，不代表论文的发表时间）