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Energies 2018, 11(12), 3409; https://doi.org/10.3390/en11123409

Non-Intrusive Load Monitoring Based on Novel Transient Signal in Household Appliances with Low Sampling Rate

and
†,*
School of Computer Science and Engineering, Pusan National University, Busan 609-735, Korea
Current address: Information Security & IoT Lab, Building A06, School of Computer Science & Engineering, Pusan National University, San-30, JangJeon–dong, Geumjeong–gu, Busan 609-735, Korea.
*
Author to whom correspondence should be addressed.
Received: 31 October 2018 / Revised: 1 December 2018 / Accepted: 3 December 2018 / Published: 5 December 2018
PDF [5214 KB, uploaded 5 December 2018]

Abstract

Nowadays climate change problems have been more and more concerns and urgent in the real world. Especially, the energy power consumption monitoring is a considerate trend having positive effects in decreasing affecting climate change. Non-Intrusive Load Monitoring (NILM) is the best economic solution to solve the electrical consumption monitoring issue. NILM captures the electrical signals from the aggregate energy consumption, feature extraction from these signals and then learning and predicting the switch ON/OFF of appliances used these feature extracted. This paper proposed a NILM framework including data acquisition, data feature extraction, and classification model. The main contribution is to develop a new transient signal in a different aspect. The proposed transient signal is extracted from the active power signal in the low-frequency sampling rate. This transient signal is used to detect the event of household appliances. In household appliances event detection, we applied to Decision Tree and Long Short-Time Memory (LSTM) models. The average accuracies of these models achieved 92.64% and 96.85%, respectively. The computational and result experiments present the solution effectiveness for the accurate transient signal extraction in the electrical input signals.
Keywords: NILM; energy disaggregation; MCP39F511; Jetson TX2; transient signature; decision tree; LSTM NILM; energy disaggregation; MCP39F511; Jetson TX2; transient signature; decision tree; LSTM
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Le, T.-T.-H.; Kim, H. Non-Intrusive Load Monitoring Based on Novel Transient Signal in Household Appliances with Low Sampling Rate. Energies 2018, 11, 3409.

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