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Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks

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Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks. / Liu, Qi; Zhang, Jing; Liu, Xiaodong et al.
In: Physical Communication, Vol. 51, 101584, 30.04.2022.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Liu, Q, Zhang, J, Liu, X, Zhang, Y, Xu, X, Khosravi, M & Bilal, M 2022, 'Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks', Physical Communication, vol. 51, 101584. https://doi.org/10.1016/j.phycom.2021.101584

APA

Liu, Q., Zhang, J., Liu, X., Zhang, Y., Xu, X., Khosravi, M., & Bilal, M. (2022). Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks. Physical Communication, 51, Article 101584. https://doi.org/10.1016/j.phycom.2021.101584

Vancouver

Liu Q, Zhang J, Liu X, Zhang Y, Xu X, Khosravi M et al. Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks. Physical Communication. 2022 Apr 30;51:101584. Epub 2021 Dec 27. doi: 10.1016/j.phycom.2021.101584

Author

Liu, Qi ; Zhang, Jing ; Liu, Xiaodong et al. / Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks. In: Physical Communication. 2022 ; Vol. 51.

Bibtex

@article{d97710f13d9b451ea8b758d6468b5716,
title = "Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks",
abstract = "The intensification of the greenhouse effect is driving the implementation of energy saving and emission reduction policies, which lead to a wide variety of energy saving solutions benefiting from the advancement of emerging technologies such as Wireless Communication, the Internet of Things, etc. With the multi-convergence development of different domains in the power industry, demand-side refinement management solutions are constantly concerned. One of the key functions of demand-side refinement management solutions is non-intrusive load monitoring (NILM), which has benefited from the growing interest in emerging technologies such as wireless communications and the Internet of Things. Currently, deep learning methods such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are widely used for in-depth research on NILM. This paper investigates the role of attention mechanisms in the above two time-series deep learning models. Experiments show that the improved model is more than 10% more effective in indoor scenes, especially for typical household appliances such as refrigerators.",
keywords = "Attention, CNN, Load management, LSTM, NILM",
author = "Qi Liu and Jing Zhang and Xiaodong Liu and Yonghong Zhang and Xiaolong Xu and Mohammad Khosravi and Muhammad Bilal",
year = "2022",
month = apr,
day = "30",
doi = "10.1016/j.phycom.2021.101584",
language = "English",
volume = "51",
journal = "Physical Communication",
issn = "1874-4907",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks

AU - Liu, Qi

AU - Zhang, Jing

AU - Liu, Xiaodong

AU - Zhang, Yonghong

AU - Xu, Xiaolong

AU - Khosravi, Mohammad

AU - Bilal, Muhammad

PY - 2022/4/30

Y1 - 2022/4/30

N2 - The intensification of the greenhouse effect is driving the implementation of energy saving and emission reduction policies, which lead to a wide variety of energy saving solutions benefiting from the advancement of emerging technologies such as Wireless Communication, the Internet of Things, etc. With the multi-convergence development of different domains in the power industry, demand-side refinement management solutions are constantly concerned. One of the key functions of demand-side refinement management solutions is non-intrusive load monitoring (NILM), which has benefited from the growing interest in emerging technologies such as wireless communications and the Internet of Things. Currently, deep learning methods such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are widely used for in-depth research on NILM. This paper investigates the role of attention mechanisms in the above two time-series deep learning models. Experiments show that the improved model is more than 10% more effective in indoor scenes, especially for typical household appliances such as refrigerators.

AB - The intensification of the greenhouse effect is driving the implementation of energy saving and emission reduction policies, which lead to a wide variety of energy saving solutions benefiting from the advancement of emerging technologies such as Wireless Communication, the Internet of Things, etc. With the multi-convergence development of different domains in the power industry, demand-side refinement management solutions are constantly concerned. One of the key functions of demand-side refinement management solutions is non-intrusive load monitoring (NILM), which has benefited from the growing interest in emerging technologies such as wireless communications and the Internet of Things. Currently, deep learning methods such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are widely used for in-depth research on NILM. This paper investigates the role of attention mechanisms in the above two time-series deep learning models. Experiments show that the improved model is more than 10% more effective in indoor scenes, especially for typical household appliances such as refrigerators.

KW - Attention

KW - CNN

KW - Load management

KW - LSTM

KW - NILM

U2 - 10.1016/j.phycom.2021.101584

DO - 10.1016/j.phycom.2021.101584

M3 - Journal article

AN - SCOPUS:85122804545

VL - 51

JO - Physical Communication

JF - Physical Communication

SN - 1874-4907

M1 - 101584

ER -