Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature
Joint Authors
Kim, Jihyun
Le, Thi-Thu-Huong
Kim, Howon
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-22, 22 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-02
Country of Publication
Egypt
No. of Pages
22
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Kim, Jihyun& Le, Thi-Thu-Huong& Kim, Howon. 2017. Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-22.
https://search.emarefa.net/detail/BIM-1140945
Modern Language Association (MLA)
Kim, Jihyun…[et al.]. Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-22.
https://search.emarefa.net/detail/BIM-1140945
American Medical Association (AMA)
Kim, Jihyun& Le, Thi-Thu-Huong& Kim, Howon. Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-22.
https://search.emarefa.net/detail/BIM-1140945
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140945