Journal
IEEE ACCESS
Volume 8, Issue -, Pages 55937-55952Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2981969
Keywords
NILM; high-frequency; steady-state; harmonics; frequency domain; FFT; bagging decision tree
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Funding
- Human Resources Development Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) through the Korea Government Ministry of Trade, Industry, and Energy [20184030202060]
- Korea Ministry of Land, Infrastructure and Transport (MOLIT) as Innovative Talent Education Program for Smart City
- National Research Foundation of Korea [21A20131612324] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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