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 |
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| Online Access: | https://ieeexplore.ieee.org/document/9042316/ |
| _version_ | 1818430570384850944 |
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| author | Thi-Thu-Huong Le Hyoeun Kang Howon Kim |
| author_facet | Thi-Thu-Huong Le Hyoeun Kang Howon Kim |
| author_sort | Thi-Thu-Huong Le |
| collection | DOAJ |
| description | 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 appliances at a high sampling rate called FFT-BDT. The proposed method includes two main processes. The first process is generating novel features in the feature extraction stage. These features are the magnitude and phase (MP) at lower odd-numbered harmonics based on the Fast Fourier Transform (FFT). MP features are steady-state features at high frequency and used as input for a learning model. The second process is where a machine learning model, a bagging decision tree (BDT), learns the novel MP features. The proposed method enhances the accuracy of recognizing different appliances that have similar power consumption. To evaluate the FFT-BDT, we experimented on two NILM datasets, including the public PLAID dataset and our own private dataset. The method outperformed prior methods and could significantly contribute to load identification in NILM. |
| first_indexed | 2024-12-14T15:35:31Z |
| format | Article |
| id | doaj.art-91f61cf8ee5c4b5eabfd8c5fedc190df |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T15:35:31Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-91f61cf8ee5c4b5eabfd8c5fedc190df2022-12-21T22:55:43ZengIEEEIEEE Access2169-35362020-01-018559375595210.1109/ACCESS.2020.29819699042316Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision TreeThi-Thu-Huong Le0https://orcid.org/0000-0002-8366-9396Hyoeun Kang1https://orcid.org/0000-0002-9651-7439Howon Kim2https://orcid.org/0000-0001-8475-7294IoT Research Center, Pusan National University, Busan, South KoreaSchool of Computer Science and Engineering, Pusan National University, Busan, South KoreaSchool of Computer Science and Engineering, Pusan National University, Busan, South KoreaNon-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 appliances at a high sampling rate called FFT-BDT. The proposed method includes two main processes. The first process is generating novel features in the feature extraction stage. These features are the magnitude and phase (MP) at lower odd-numbered harmonics based on the Fast Fourier Transform (FFT). MP features are steady-state features at high frequency and used as input for a learning model. The second process is where a machine learning model, a bagging decision tree (BDT), learns the novel MP features. The proposed method enhances the accuracy of recognizing different appliances that have similar power consumption. To evaluate the FFT-BDT, we experimented on two NILM datasets, including the public PLAID dataset and our own private dataset. The method outperformed prior methods and could significantly contribute to load identification in NILM.https://ieeexplore.ieee.org/document/9042316/NILMhigh-frequencysteady-stateharmonicsfrequency domainFFT |
| spellingShingle | Thi-Thu-Huong Le Hyoeun Kang Howon Kim Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision Tree IEEE Access NILM high-frequency steady-state harmonics frequency domain FFT |
| title | Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision Tree |
| title_full | Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision Tree |
| title_fullStr | Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision Tree |
| title_full_unstemmed | Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision Tree |
| title_short | Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision Tree |
| title_sort | household appliance classification using lower odd numbered harmonics and the bagging decision tree |
| topic | NILM high-frequency steady-state harmonics frequency domain FFT |
| url | https://ieeexplore.ieee.org/document/9042316/ |
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