TransNILM: A Transformer-based Deep Learning Model for Non-intrusive Load Monitoring-Reference-Cited by-同舟云学术

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TransNILM: A Transformer-based Deep Learning Model for Non-intrusive Load Monitoring

Published:2022-12-10 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on High Performance Big Data and Intelligent Systems (HDIS)
language:
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Author:

Cheng Xu¹,Zhao Meng²,Zhang Jianhua²,Wang Jinghao³,Pan Xueping⁴,Liu Xiufeng⁵

Affiliation:

1. Section of Energy Markets Smart Innovation Norway,Halden,Norway

2. Tianjin University of Technology,School of Computer Science and Engineering,Tianjin,China

3. Norwegian University of Science and Technology,Department of Electric Power Engineering,Trondheim,Norway

4. College of Energy and Electrical Engineering HoHai University,Nanjing,China

5. Technical University of Denmark Lyngby,Department of Technology Management and Economics,Denmark

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9991245/9991269/09991439.pdf?arnumber=9991439

Reference41 articles.

1. Development of a Non-Intrusive Load Monitoring (NILM) with Unknown Loads using Support Vector Machine

2. Sequence-to-Point Learning Based on Temporal Convolutional Networks for Nonintrusive Load Monitoring

3. Dilated residual attention network for load disaggregation

4. A Time Series Transformer based method for the rotating machinery fault diagnosis

5. Nonintrusive Load Monitoring Using Wavelet Design and Machine Learning

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Equipment- and Time-Constrained Data Acquisition Protocol for Non-Intrusive Appliance Load Monitoring;Energies;2023-10-28

2. An Operation Scheme Generation Method for Nuclear Power Plant Operation under the Condition of No Operating Procedures Guided;Electronics;2023-04-12

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