基于深度迁移学习的家用电器识别研究  

Study on household appliances recognition based on deep transfer learning

作　　者：陈红[1] 余志斌[1] CHEN Hong;YU Zhibin(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学电气工程学院,四川成都611756

出　　处：《传感器与微系统》2020年第6期48-50,54,共4页Transducer and Microsystem Technologies

基　　金：四川省科技厅重大人工智能专项项目(2018GZDZX0043)。

摘　　要：非侵入式家用负荷识别可实现电能管理、能源监测等电能质量分析,具有低成本、易实施诸多优点。针对实测家用电器稳定运行数据存在噪声干扰,导致大量V-I图数据不可用问题,提出一种V-I图数据筛选算法,筛选表征家用电器的二维V-I特征图数据,并改进深度学习网络,利用迁移学习,实现更好的家用电器识别效果。实验结果表明:数据筛选后不仅提高了电器识别的准确度,又加快了算法的收敛速度。Non-intrusive household load indentification can realize a series of power quality analysis such as power management,energy monitoring. It has the advantages of low cost,easy implementation. Aiming at the problem that a large number of V-I figure data is unavailable due to noise interference of stable operation data of household appliances of the actual measurement,a V-I map data screening algorithm is proposed,which screens the two-dimensional V-I feature map data of characterizing household appliances,improves the deep learning network,and achieves better recognition effect of household appliances by using transfer learning. The experimental result shows that not only the accuracy of the appliances identification is improved,but also the convergence speed of the algorithm is accelerated.

关 键 词：非侵入式负荷识别 V-I特征图 数据筛选 深度学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]