中图分类号：TP3-0 文献标志码：A DOI: 10.16157/j.issn.0258-7998.223699 中文引用格式： 闫相伟，宋国壮，刘怡豪. 基于改进Stacking集成分类算法的用户用电信息异常识别[J]. 电子技术应用，2023，49(8)：13-18. 英文引用格式： Yan Xiangwei，Song Guozhuang，Liu Yihao. Abnormal identification of user electricity consumption information based on improved stacking integrated classification algorithm[J]. Application of Electronic Technique，2023，49(8)：13-18.
Abnormal identification of user electricity consumption information based on improved stacking integrated classification algorithm
Yan Xiangwei，Song Guozhuang，Liu Yihao
(School of Communication and Information Engineering， Chongqing University of Posts and Telecommunications， Chongqing 400065， China)
Abstract： 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.
Key words : user electricity consumption information；anomaly identification；improved stacking integrated classification algorithm；FDI