A SVM and Variable Structure Neural Network Method for Short-term Load Forecasting

摘要：

This paper put forward a new method of the SVM and variable structure artificial neural network model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of SVM. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective way to forecast short-term electric load.

关键词： SVM variable structure neural network electric load forecasting

作者: Qian Zhang Tongna Liu

作者单位: Department of Economic Management North China Electric Power University Baoding,Hebei,China 071000 Department of Electronic and Communication Engineering North China Electric Power University Baoding

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 392-396

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）