Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

# Review on Deep Neural Networks Applied to Low-Frequency NILM

Version 1 : Received: 14 April 2021 / Approved: 15 April 2021 / Online: 15 April 2021 (15:05:09 CEST)

A peer-reviewed article of this Preprint also exists.

Huber, P.; Calatroni, A.; Rumsch, A.; Paice, A. Review on Deep Neural Networks Applied to Low-Frequency NILM. Energies 2021, 14, 2390. Huber, P.; Calatroni, A.; Rumsch, A.; Paice, A. Review on Deep Neural Networks Applied to Low-Frequency NILM. Energies 2021, 14, 2390.

Journal reference: Energies 2021, 14, 2390
DOI: 10.3390/en14092390

## 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. We first review the many degrees of freedom of these approaches, what has already been done in 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 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 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.

## Subject Areas

non-intrusive load monitoring; load disaggregation; NILM; review; deep learning; deep neural networks; machine learning

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