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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9042316/ |