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• W3113991957 abstract "Nonintrusive load monitoring (NILM) plays a key role in the real-time electricity consumption monitoring of household appliances. However, it is difficult to realize high precision load identification by using a single waveform feature. Therefore, this article proposes a two-stream convolutional neural network based on current time-frequency feature fusion for nonintrusive load identification. First, a time series image coding method for current time-frequency multi-feature fusion is proposed. The method can extract the time domain and frequency domain features of the current timing signal effectively. Then, we present a two-stream neural network combining the gated recurrent unit (GRU) and a two-dimensional convolutional neural network (2D-CNN) to improve the load identification performance. Finally, the experimental results on the PLAID and IDOUC datasets show that the proposed model outperforms the state-of-the-art methods." @default.
• W3113991957 created "2021-01-05" @default.
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• W3113991957 date "2021-01-01" @default.
• W3113991957 modified "2023-10-12" @default.
• W3113991957 title "A Nonintrusive Load Identification Model Based on Time-Frequency Features Fusion" @default.
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• W3113991957 doi "https://doi.org/10.1109/access.2020.3047147" @default.
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