关键词: 短期负荷预测, 多重核学习, 支持向量机, 核函数

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.

Key words: short-term load forecasting, multi-kernel learning, Support Vector Machines（SVM）, kernel function