基于改进Stacking集成分类算法的用户用电信息异常识别  

Abnormal identification of user electricity consumption information based on improved stacking integrated classification algorithm

作　　者：闫相伟 宋国壮 刘怡豪 Yan Xiangwei;Song Guozhuang;Liu Yihao(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《电子技术应用》2023年第8期13-18,共6页Application of Electronic Technique

摘　　要：随着电力用户信息采集系统的发展,更丰富的用户用电信息被用于用户用电信息异常的识别。基于FDI攻击进行虚假数据注入,构造用户用电信息异常数据集,并提出了一种基于召回率的改进Stacking集成分类算法。该算法采用K-近邻算法(k-Nearest Neighbors,KNN)、随机森林模型(Random Forests,RF)、支持向量机(Support Vector Machine,SVM)以及梯度决策树(Gradient Boosting Decision Tree,GBDT)作为Stacking结构的基分类模型;采用逻辑回归(Logistic Regression,LR)作为Stacking结构的元分类模型。并基于召回率为基分类模型的输出结果进行权值赋值,从而作为元分类模型的输入数据集。通过实验验证,所提的基于召回率的改进Stacking集成分类算法相比于传统Stacking集成分类算法拥有更高效的分类性能。With the development of power user information collection system,richer user electricity consumption information is used for the identification of user electricity consumption information anomalies.In this paper,a false data injection based on the FDI attack is performed to construct a dataset of user electricity consumption information anomalies,and an improved stacking integrated classification algorithm based on recall is proposed.K-nearest neighbors algorithm(KNN),random forest model(RF),support vector machine(SVM)and gradient decision tree(GBDT)are used as the scheme of base classification model of the stacking structure.Logistic regression(LR)is used as a meta-classification model of the stacking structure.The output of the basic classification model is weighted based on the recall rate,which is used as the input data set of the meta-classification model.The proposed improved stacking classification algorithm based on recall is shown to be more efficient than the traditional stacking classification algorithm.

关 键 词：用户用电信息 异常识别 改进Stacking集成分类算法 FDI 

分 类 号：TP3-0[自动化与计算机技术—计算机科学与技术]