Review on Deep Neural Networks Applied to Low-Frequency NILM

Review on Deep Neural Networks Applied to Low-Frequency NILM

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview on the state of...

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Bibliographic Details
Main Authors: Patrick Huber, Alberto Calatroni, Andreas Rumsch, Andrew Paice
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Energies
Subjects:
non-intrusive load monitoring
load disaggregation
NILM
review
deep learning
deep neural networks
Online Access:https://www.mdpi.com/1996-1073/14/9/2390

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https://www.mdpi.com/1996-1073/14/9/2390

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