Recognition accuracy of each appliance(%).

<div><p>The loads that have several working states cannot be accurately distinguished by the conventional Non-Intrusive Load Monitoring (NILM) methods. This paper proposed an improved NILM method based on the Resnet18 Convolutional Neural Network (CNN) and Support Vector Machine (SVM) al...

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Bibliographic Details
Main Author:	Lei Wang (6656) (author)
Other Authors:	Xia Han (73012) (author), Yushu Cheng (5783054) (author), Jiaqi Ma (773451) (author), Xuerui Zhang (12478308) (author), Xiaoqing Han (2531497) (author)
Published:	2025
Subjects:	Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified various operating states support vector machine intrusive load monitoring feature selection algorithm achieve high accuracy state appliances quickly proposed nilm method state appliances paper proposed load features xlink "> successfully classified stage non significantly improve plaid dataset first classified conventional non accurately distinguished
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Recognition accuracy of each appliance(%).

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