A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring

Guolong Liu; Junhua Zhao; Gaoqi Liang; Jinjie Liu; Huan Zhao; Zhanxin Wu

doi:10.1109/EI252483.2021.9713379

A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring

Guolong Liu
, Junhua Zhao^*
, Gaoqi Liang
, Jinjie Liu
, Huan Zhao
, Zhanxin Wu

^*Corresponding author for this work

The Chinese University of Hong Kong, Shenzhen
Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Identifying industrial appliances can assist in demand-side management in smart grids. In this paper, a temporal convolutional neural network with attention mechanism based method is proposed for industrial non-intrusive load monitoring (NILM). First, the industrial load sequence is segmented into fixed length subsequences, and the ON-OFF states of appliances are also recorded simultaneously. Then, some segmented load subsequences are used as the input of the proposed method, and the corresponding classifiers of different appliances are trained by using the input together with the corresponding appliance states. Finally, the trained classifiers are used to classify the subsequences to be identified. This paper releases a dataset named Textile Mill Load Dataset (TMLD) that contains 30 days of load data from a textile mill. Experiments on this dataset show that the overall accuracy of the proposed method is over 88% when the load data is sampled once every one second and longer.

Original language	English
Title of host publication	5th IEEE Conference on Energy Internet and Energy System Integration
Subtitle of host publication	Energy Internet for Carbon Neutrality, EI2 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3279-3284
Number of pages	6
ISBN (Electronic)	9781665434256
DOIs	https://doi.org/10.1109/EI252483.2021.9713379
State	Published - 2021
Externally published	Yes
Event	5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021 - Taiyuan, China Duration: 22 Oct 2021 → 25 Oct 2021

Publication series

Name	5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021

Conference

Conference	5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021
Country/Territory	China
City	Taiyuan
Period	22/10/21 → 25/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

TMLD
artificial intelligence
deep learning
industrial appliance identification
non-intrusive load monitoring

Access to Document

10.1109/EI252483.2021.9713379

Cite this

Liu, G., Zhao, J., Liang, G., Liu, J., Zhao, H., & Wu, Z. (2021). A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring. In 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021 (pp. 3279-3284). (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EI252483.2021.9713379

Liu, Guolong ; Zhao, Junhua ; Liang, Gaoqi et al. / A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring. 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 3279-3284 (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021).

@inproceedings{00cd8a30fd9b46d7b437a9528413fd42,

title = "A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring",

abstract = "Identifying industrial appliances can assist in demand-side management in smart grids. In this paper, a temporal convolutional neural network with attention mechanism based method is proposed for industrial non-intrusive load monitoring (NILM). First, the industrial load sequence is segmented into fixed length subsequences, and the ON-OFF states of appliances are also recorded simultaneously. Then, some segmented load subsequences are used as the input of the proposed method, and the corresponding classifiers of different appliances are trained by using the input together with the corresponding appliance states. Finally, the trained classifiers are used to classify the subsequences to be identified. This paper releases a dataset named Textile Mill Load Dataset (TMLD) that contains 30 days of load data from a textile mill. Experiments on this dataset show that the overall accuracy of the proposed method is over 88\% when the load data is sampled once every one second and longer.",

keywords = "TMLD, artificial intelligence, deep learning, industrial appliance identification, non-intrusive load monitoring",

author = "Guolong Liu and Junhua Zhao and Gaoqi Liang and Jinjie Liu and Huan Zhao and Zhanxin Wu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021 ; Conference date: 22-10-2021 Through 25-10-2021",

year = "2021",

doi = "10.1109/EI252483.2021.9713379",

language = "英语",

series = "5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3279--3284",

booktitle = "5th IEEE Conference on Energy Internet and Energy System Integration",

address = "美国",

}

Liu, G, Zhao, J, Liang, G, Liu, J, Zhao, H & Wu, Z 2021, A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring. in 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021, Institute of Electrical and Electronics Engineers Inc., pp. 3279-3284, 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021, Taiyuan, China, 22/10/21. https://doi.org/10.1109/EI252483.2021.9713379

A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring. / Liu, Guolong; Zhao, Junhua; Liang, Gaoqi et al.
5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 3279-3284 (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring

AU - Liu, Guolong

AU - Zhao, Junhua

AU - Liang, Gaoqi

AU - Liu, Jinjie

AU - Zhao, Huan

AU - Wu, Zhanxin

PY - 2021

Y1 - 2021

N2 - Identifying industrial appliances can assist in demand-side management in smart grids. In this paper, a temporal convolutional neural network with attention mechanism based method is proposed for industrial non-intrusive load monitoring (NILM). First, the industrial load sequence is segmented into fixed length subsequences, and the ON-OFF states of appliances are also recorded simultaneously. Then, some segmented load subsequences are used as the input of the proposed method, and the corresponding classifiers of different appliances are trained by using the input together with the corresponding appliance states. Finally, the trained classifiers are used to classify the subsequences to be identified. This paper releases a dataset named Textile Mill Load Dataset (TMLD) that contains 30 days of load data from a textile mill. Experiments on this dataset show that the overall accuracy of the proposed method is over 88% when the load data is sampled once every one second and longer.

AB - Identifying industrial appliances can assist in demand-side management in smart grids. In this paper, a temporal convolutional neural network with attention mechanism based method is proposed for industrial non-intrusive load monitoring (NILM). First, the industrial load sequence is segmented into fixed length subsequences, and the ON-OFF states of appliances are also recorded simultaneously. Then, some segmented load subsequences are used as the input of the proposed method, and the corresponding classifiers of different appliances are trained by using the input together with the corresponding appliance states. Finally, the trained classifiers are used to classify the subsequences to be identified. This paper releases a dataset named Textile Mill Load Dataset (TMLD) that contains 30 days of load data from a textile mill. Experiments on this dataset show that the overall accuracy of the proposed method is over 88% when the load data is sampled once every one second and longer.

KW - TMLD

KW - artificial intelligence

KW - deep learning

KW - industrial appliance identification

KW - non-intrusive load monitoring

UR - https://www.scopus.com/pages/publications/85128188257

U2 - 10.1109/EI252483.2021.9713379

DO - 10.1109/EI252483.2021.9713379

M3 - 会议稿件

AN - SCOPUS:85128188257

T3 - 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021

SP - 3279

EP - 3284

BT - 5th IEEE Conference on Energy Internet and Energy System Integration

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021

Y2 - 22 October 2021 through 25 October 2021

ER -

Liu G, Zhao J, Liang G, Liu J, Zhao H, Wu Z. A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring. In 5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 3279-3284. (5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021). doi: 10.1109/EI252483.2021.9713379

A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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