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A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition
Joint Authors
Wang, Zenghui
Sun, Yanxia
Matindife, Liston
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-18
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
In a smart home, the nonintrusive load monitoring recognition scheme normally achieves high appliance recognition performance in the case where the appliance signals have widely varying power levels and signature characteristics.
However, it becomes more difficult to recognize appliances with equal or very close power specifications, often with almost identical signature characteristics.
In literature, complex methods based on transient event detection and multiple classifiers that operate on different hand crafted features of the signal have been proposed to tackle this issue.
In this paper, we propose a deep learning approach that dispenses with the complex transient event detection and hand crafting of signal features to provide high performance recognition of close tolerance appliances.
The appliance classification is premised on the deep multilayer perceptron having three appliance signal parameters as input to increase the number of trainable samples and hence accuracy.
In the case where we have limited data, we implement a transfer learning-based appliance classification strategy.
With the view of obtaining an appropriate high performing disaggregation deep learning network for the said problem, we explore individually three deep learning disaggregation algorithms based on the multiple parallel structure convolutional neural networks, the recurrent neural network with parallel dense layers for a shared input, and the hybrid convolutional recurrent neural network.
We disaggregate a total of three signal parameters per appliance in each case.
To evaluate the performance of the proposed method, some simulations and comparisons have been carried out, and the results show that the proposed method can achieve promising performance.
American Psychological Association (APA)
Matindife, Liston& Sun, Yanxia& Wang, Zenghui. 2020. A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-21.
https://search.emarefa.net/detail/BIM-1202167
Modern Language Association (MLA)
Matindife, Liston…[et al.]. A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition. Mathematical Problems in Engineering No. 2020 (2020), pp.1-21.
https://search.emarefa.net/detail/BIM-1202167
American Medical Association (AMA)
Matindife, Liston& Sun, Yanxia& Wang, Zenghui. A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-21.
https://search.emarefa.net/detail/BIM-1202167
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202167