短期负荷预测的支持向量机方法研究

STUDY OF SUPPORT VECTOR MACHINES FOR SHORT-TERM LOAD FORECASTING

提出了一种基于支持向量机(SVM)理论的电力系统短期负荷预测方法。该方法采用结构风险最小化原则(SRM),与采用经验风险最小化原则(ERM)的传统神经网络方法相比,具有更好的泛化性能和精度,减少了对经验的依赖。SVM算法以统计学习理论作为其理论基础,它的训练等价于解决一个二次规划问题。为了提高负荷预测精度,文中在训练数据集中采用了负荷数据和温度数据。通过和多层BP神经网络进行比较的试验,结果证明了其在短期负荷预测中的有效性。

A new methodology based on SVM for the electric power system load forecasting was presented. The proposed algorithm embodies the Structural Risk Minimization (SRM) principle is more generalized performance and accurate as compared to artificial neural network which embodies the Embodies Risk Minimization (ERM) principle. The theory of the SVM algorithm is based on statistical learning theory. Training of SVM leads to a quadratic programming problem. In order to improve forecast accuracy, the SVM interpolates among the load and temperature data in a training data set. Analysis of the experimental results proved that SVM could achieve greater accuracy than the BP neural network.