Enhancing neural non-intrusive load monitoring with generative adversarial networks

Enhancing neural non-intrusive load monitoring with generative adversarial networks

Abstract The application of Deep Learning methodologies to Non-Intrusive Load Monitoring (NILM) gave rise to a new family of Neural NILM approaches which increasingly outperform traditional NILM approaches. In this extended abstract describing our ongoing research, we analyze recent Neural NILM appr...

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Bibliographic Details
Main Authors: Kaibin Bao, Kanan Ibrahimov, Martin Wagner, Hartmut Schmeck
Format: Article
Language:English
Published: SpringerOpen 2018-10-01
Series:Energy Informatics
Subjects:
Non-intrusive load monitoring
Generative adversarial networks
Neural NILM
Generative modeling
Deep Learning
Online Access:http://link.springer.com/article/10.1186/s42162-018-0038-y

Internet

http://link.springer.com/article/10.1186/s42162-018-0038-y

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