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Computational Intelligence and Neuroscience
Volume 2017, Article ID 4216281, 22 pages
Research Article

Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

1IoT Research Center, PNU, Busan, Republic of Korea
2Pusan National University, Busan, Republic of Korea

Correspondence should be addressed to Jihyun Kim; moc.liamg@000sphjk

Received 5 May 2017; Revised 2 August 2017; Accepted 21 August 2017; Published 2 October 2017

Academic Editor: Nikolaos Doulamis

Copyright © 2017 Jihyun Kim et al. 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.


Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.