Generative Adversarial Networks and Transfer Learning for Non-Intrusive Load Monitoring in Smart Grids-Reference-Cited by-同舟云学术

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Generative Adversarial Networks and Transfer Learning for Non-Intrusive Load Monitoring in Smart Grids

Published:2020-11-11 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
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Author:

Ahmed Awadelrahman M. A.,Zhang Yan,Eliassen Frank

Funder

The Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9302911/9302928/09302933.pdf?arnumber=9302933

Cited by 17 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 review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context;Energy and Buildings;2024-02

2. A Generalizable Method for Practical Non-Intrusive Load Monitoring via Metric-Based Meta-Learning;IEEE Transactions on Smart Grid;2024-01

3. Conditional-TimeGAN for Realistic and High-Quality Appliance Trajectories Generation and Data Augmentation in Nonintrusive Load Monitoring;IEEE Transactions on Instrumentation and Measurement;2024

4. SGAN: Appliance Signatures Data Generation for NILM Applications Using GANs;Lecture Notes in Networks and Systems;2024

5. Evaluation of Diffusion Models on Non-Intrusive Load Monitoring;2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2);2023-12-15

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