基于支持向量机的电力系统短期负荷预测

POWER SYSTEM SHORT-TERM LOAD FORECASTING BASED ON SUPPORT VECTOR MACHINES

对将径向基函数(Radial Base Function,RBF)作为核函数的支持向量机(Support Vector Machine,SVM)方法应用于短期负荷预测进行了研究。作者使用基于 SVM 的回归估计算法建立了回归估计函数表达式,给出了SVM 网络结构;采用江苏省某市的实际负荷数据,按照不同的负荷日属性和历史负荷数据进行样本选择,使用 LIBSVM 算法和适当的核函数进行了负荷预测,并将该预测结果同由时间序列及 BP 神经网络方法得到的预测结果进行了比较,结果表明,所提出的预测方法有较高的精度。

Using the radial base function (RBF) as kernel function, the research of applying the Support Vector Machines (SVM) method to power system short-term load forecasting is presented. At first, the expression of regression estimation function is established by SVM based regression estimation algorithm and the structure of SVM network is given. Adopting the actual data from the distribution network of a certain domestic city, the samples are chosen according to different attributes of daily power loads and historical load data, and then the load is forecasted by use of LIBSVM algorithm and proper kernel function. The forecasted results are compared with those from time series method and BP artificial neural network (ANN) method, and it is shown that the presented forecasting method is more accurate.