An Improved Sequence-to-point Learning for Non-intrusive Load Monitoring Based on Discrete Wavelet Transform

Nonintrusive load monitoring (NILM) is a process that monitors the aggregated power consumption data of customers measured by a single sensor and decomposes the real-time power consumption of each dedicated device. Recent research has been focused on in-depth learning. However, the expensive cost of...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement 2023-01, Vol.72, p.1-1
Hauptverfasser:	Xiong, Chang, Cai, Zhaohui, Liu, Shubo, Luo, Jie, Tu, Guoqing
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms autoencoder bidirectional long short-term memory (Bi-LSTM) combined pruning algorithm convolutional network Decomposition Discrete Wavelet Transform discrete wavelet transform (DWT) Discrete wavelet transforms Feature extraction Fluctuations Home appliances Learning Load modeling Monitoring Nonintrusive load monitoring (NILM) Power consumption Power demand Power management Real time sequence-to-point learning (S2P) Training Wavelet transforms
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