Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision Tree

Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision Tree

Non-Intrusive Load Monitoring (NILM) systems have gained popularity in recent years for saving more energy. To reduce sensing infrastructure costs, NILM monitors the electrical loads based on a machine learning method. We propose a novel approach to improve the performance of classifying household a...

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Bibliographic Details
Main Authors: Thi-Thu-Huong Le, Hyoeun Kang, Howon Kim
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
NILM
high-frequency
steady-state
harmonics
frequency domain
FFT
Online Access:https://ieeexplore.ieee.org/document/9042316/

Internet

https://ieeexplore.ieee.org/document/9042316/

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