EdgeNILM

Kukunuri, Rithwik; Aglawe, Anup; Chauhan, Jainish; Bhagtani, Kratika; Patil, Rohan; Walia, Sumit; Batra, Nipun

doi:10.1145/3408308.3427977

Cited by 37 publications

(39 citation statements)

References 16 publications

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“…Due to the presented observations and the general benefits of multi-task learning presented in [174], we conclude that multi-task learning is beneficial for DNN-NILM approaches. As has also been noted by [139], we see the additional benefit of multi-task learning in a reduced computational burden for edge devices because a major amount of computations for disaggregation can be shared between several applications.…”

Section: Multi-task Learningsupporting

confidence: 55%

“…A large improvement from joint learning on multiple appliances is also reported by [61] and, as was already mentioned in section 4.1, four of the best approaches [56,76,91,134] use multi-task learning for network training. Only the authors of [139] report a general decrease in performance of multi-task learning models with respect to their single-task counterparts. They propose to employ a different architecture or share less layers between appliances as a remedy.…”

Section: Multi-task Learningmentioning

confidence: 99%

“…That means that the DNN-NILM inference has to work on an embedded system even though that can be quite challenging in terms of computational, storage, and energy resources. This direction has been investigated by [132,[137][138][139]145]: [132] is to our knowledge the first to publish the implementation of DNN-NILM inference on an embedded device. Both [132] and later [145] used for that purpose a Raspberry Pi computer.…”

Section: Data Scarcitymentioning

confidence: 99%

“…The authors report that "disaggregation accuracy deviates up to ≈9.4 % from original disaggregation model, but, on average, remains satisfactory". Both [138] and [139] investigate different pruning methods based on the network from [80]. Pruning methods aim at reducing neurons in the network that contribute little to the final output.…”

Section: Data Scarcitymentioning

confidence: 99%

“…The final goal is to result with sparse networks that have lower storage and computational requirements but similar performance compared to the original networks. Both publications find that networks can be heavily pruned with only slight decrease in performance: [138] reports a reduction of the number of network weights by 87 % and [139] reports a 100-fold reduction in model size and a 25-fold reduction in inference times. [139] additionally investigates multi-task learning and vector decomposition as further paths towards efficient computations in embedded systems.…”

Section: Data Scarcitymentioning

confidence: 99%

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Review on Deep Neural Networks Applied to Low-Frequency NILM

Huber¹,

Calatroni²,

Rumsch³

et al. 2021

Preprint

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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.

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Section: Multi-task Learningsupporting

confidence: 55%

Section: Multi-task Learningmentioning

confidence: 99%

Section: Data Scarcitymentioning

confidence: 99%

Section: Data Scarcitymentioning

confidence: 99%

Section: Data Scarcitymentioning

confidence: 99%

See 3 more Smart Citations

Review on Deep Neural Networks Applied to Low-Frequency NILM

Huber¹,

Calatroni²,

Rumsch³

et al. 2021

Preprint

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show abstract

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

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Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first Int J Intell Syst. 2022;37:7124-7179. wileyonlinelibrary.com/journal/int investigated, including data collection devices and public data sets. Next, event-based and non-eventbased NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

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Pre-trained non-intrusive load monitoring model for recognizing activity of daily living

Kim

Park

2022

Appl Intell

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EdgeNILM

Cited by 37 publications

References 16 publications

Review on Deep Neural Networks Applied to Low-Frequency NILM

Review on Deep Neural Networks Applied to Low-Frequency NILM

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Pre-trained non-intrusive load monitoring model for recognizing activity of daily living

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