Non-Intrusive Load Monitoring Using Denoising Autoencoder Neural Networks-Reference-Cited by-同舟云学术

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Non-Intrusive Load Monitoring Using Denoising Autoencoder Neural Networks

Published:2022-09-16 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC)
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Author:

Sewwandi W. A. H. M.¹,Anthony C. N. D.¹,Disanayaka D. M. A. M.¹,Boralessa M. A. K. S.¹,Hemapala K. T. M. U.¹

Affiliation:

1. University of Moratuwa,Department of Electrical Engineering,Katubedda,Sri Lanka

Funder

University of Moratuwa

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9929431/9929334/09929710.pdf?arnumber=9929710

Reference8 articles.

1. A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

2. Application of load monitoring in appliances’ energy management – A review

3. REDD: A Public Data Set for Energy Disaggregation Research;kolter;Artif Intell,2011

4. Long Short-Term Memory (LSTM) Neural Networks Applied to Energy Disaggregation

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. A Combined Approach for Efficient Compression and Restoration of Multispectral Satellite Images;2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC);2023-06-16

2. Appliance Identification in NILM Applications by means of a Convolutional Auto-Encoder;2023 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv);2023-05-29

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