计算机工程与应用2012，Vol.48Issue(33)：207-211,5.DOI:10.3778/j.issn.1002-8331.1207-0260
基于多重核学习支持向量机短期负荷预测研究
Study of short-term load forecasting based on multi-kernel Support Vector Machine learning
孔强 1姚建刚 1汪梦健 2孙谦 3毛田 3康童3
作者信息
- 1. 湖南大学电气与信息工程学院,长沙410082
- 2. 常德石门电力局,湖南常德415300
- 3. 湖南湖大华龙电气与信息技术有限公司,长沙410082
- 折叠
摘要
Abstract
In recent years, the SVM method in load forecasting application research has become the hot spot. This paper in view of the traditional standard support vector machine method in the prediction of time and prediction accuracy of deficiencies, firstly applies the MKL in power system short-term load forecasting field. The algorithm is realized through solving quadratic constrained programmer in the hybrid kernel space. Compared with the standard support vector regression algorithm, this method not only can improve the prediction performance, but also can reduce the number of support vectors. The practical example shows that, the method can effectively improve the prediction accuracy, shorten the prediction time, and with good generalization performance.关键词
短期负荷预测/多重核学习/支持向量机/核函数Key words
short-term load forecasting/ multi-kernel learning/ Support Vector Machines (SVM)/ kernel function分类
信息技术与安全科学引用本文复制引用
孔强,姚建刚,汪梦健,孙谦,毛田,康童..基于多重核学习支持向量机短期负荷预测研究[J].计算机工程与应用,2012,48(33):207-211,5.
