基于差量特征与AdaBoost的家用负荷识别方法研究  

Research on household load identification method based on difference features and AdaBoost

作　　者：王岩俊 蔡高琰 骆德汉[1] 梁炳基 Wang Yanjun;Cai Gaoyan;Luo Dehan;Liang Bingji(School of Information Engineering,Guangdong University of Technology,Guangzhou 510006,China;Hodi Technology Co.,Ltd.,Foshan 528200,China)

机构地区：[1]广东工业大学信息工程学院,广东广州510006 [2]广东浩迪创新科技有限公司,广东佛山528200

出　　处：《信息技术与网络安全》2022年第3期78-82,共5页Information Technology and Network Security

摘　　要：针对家用负荷提出了一种使用智能电表进行数据采集的非侵入式负荷在线识别方法。该方法使用智能电表计算出负荷的差量特征向量预先建立特征库,训练以决策树作为弱分类器的AdaBoost分类器模型,利用负荷投切时电表的告警信息中包含的特征向量进行分类以实现负荷在线识别,实时性好且提高了单一决策树模型的识别效果。实验结果证明了该方法的可行性,实现了负荷使用信息的获取,具有较好的实际应用价值。Aiming at household load,a non-intrusive online load identification method using smart meters for data collection is proposed.This method uses the smart meter to calculate the difference feature vector of the load to build a feature library in advance,trains the AdaBoost classifier model which takes the decision tree as the weak classifier,and uses the feature vector contained in the alarm information of the smart meter when the load is switched to classify the load,and to achieve load online recognition.This method has good real-time performance and improves the recognition effect of a single decision tree model.The experimental results show that the proposed method is feasible,and realizes the acquisition of load usage information,has good practical application value.

关 键 词：非侵入式负荷识别 智能电表 差量特征 ADABOOST 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]