Review on Deep Neural Networks Applied to Low-Frequency NILM

Patrick Huber, Alberto Calatroni, Andreas Rumsch and Andrew Paice
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Patrick Huber: iHomeLab, Engineering and Architecture, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland
Alberto Calatroni: iHomeLab, Engineering and Architecture, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland
Andreas Rumsch: iHomeLab, Engineering and Architecture, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland
Andrew Paice: iHomeLab, Engineering and Architecture, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland

Energies, 2021, vol. 14, issue 9, 1-34

Abstract: 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.

Keywords: non-intrusive load monitoring; load disaggregation; NILM; review; deep learning; deep neural networks; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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