Recognition accuracy of each appliance(%).

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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items