NDLI: Review on Deep Neural Networks Applied to Low-Frequency NILM

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Review on Deep Neural Networks Applied to Low-Frequency NILM

Content Provider	MDPI
Author	Huber, Patrick Calatroni, Alberto Rumsch, Andreas Paice, Andrew
Copyright Year	2021
Description	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 the research up to November 2020, and secondly, to identify worthwhile open research topics. Accordingly, we first review the many degrees of freedom of these approaches, what has already been done in the literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported mean absolute error (MAE) and F $1$ -scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10 s, a large field of view, the usage of generative adversarial network (GAN) losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning, and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.
Starting Page	2390
e-ISSN	19961073
DOI	10.3390/en14092390
Journal	Energies
Issue Number	9
Volume Number	14
Language	English
Publisher	MDPI
Publisher Date	2021-04-23
Access Restriction	Open
Subject Keyword	Energies Industrial Engineering Non-intrusive Load Monitoring Load Disaggregation Nilm Review Deep Learning Deep Neural Networks Machine Learning
Content Type	Text
Resource Type	Article

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