Ensemble-Based Deep Learning Model for Non-Intrusive Load Monitoring

Wang, Junfei; Kababji, Samer El; Graham, Connor; Srikantha, Pirathayini

doi:10.1109/epec47565.2019.9074816

Cited by 16 publications

(8 citation statements)

References 12 publications

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“…Naturally, this dimension is mostly coupled with the third distinction, whether the learning problem is formulated as a classification or regression task. However, there are four works [33,61,68,118] where power values are clustered into groups, and the power regression problem is recast into a classification task. These references are marked with P class .…”

Section: Outputmentioning

confidence: 99%

Review on Deep Neural Networks Applied to Low-Frequency NILM

et al. 2021

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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. 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 F1-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.

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Section: Outputmentioning

confidence: 99%

Review on Deep Neural Networks Applied to Low-Frequency NILM

et al. 2021

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“…Although the proposed algorithm is applicable in the general smart grid context, this paper focuses on the NILM problem for demonstration purposes. In the literature, ML has been leveraged to perform NILM, and Wang et al [1], Lan et al [39], Mauch and Yang [40] are examples of some recent work in this area. This paper focuses on [1] for the Oracle.…”

Section: A the Threat Modelmentioning

confidence: 99%

“…In the literature, ML has been leveraged to perform NILM, and Wang et al [1], Lan et al [39], Mauch and Yang [40] are examples of some recent work in this area. This paper focuses on [1] for the Oracle. This NILM system is a deep learning based model which cannot be replicated easily when the parameters are unknown and thus allows us to showcase the efficacy of the proposed attack mechanism.…”

Section: A the Threat Modelmentioning

confidence: 99%

“…NILM disaggregates these readings into specific appliances that were active in the dwelling over the period under consideration without requiring the installation of sensors for individual appliances to detect the statuses of these devices. NILM offers tremendous insights into the power usage patterns of consumers and allows for the automation in energy applications that include home energy management systems and demand response programs that aim to increase economical and sustainable power usage [1]- [4]. NILM also allows for the detection of fraudulent activities such as illegal operations (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Stealthy Black-Box Attacks on Deep Learning Non-Intrusive Load Monitoring Models

Wang

Srikantha

2021

IEEE Trans. Smart Grid

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With the advent of the advanced metering infrastructure, electricity usage data is being continuously generated at large volumes by smart meters vastly deployed across the modern power grid. Electric power utility companies and third party entities such as smart home management solution providers gain significant insights into these datasets via machine learning (ML) models. These are then utilized to perform active/passive power demand management that fosters economical and sustainable electricity usage. Although ML models are powerful, these remain vulnerable to adversarial attacks. A novel stealthy black-box attack construction model is proposed that targets deep learning models utilized to perform non-intrusive load monitoring based on smart meter data. These attacks are practical as there is no assumption of the knowledge of training data, internal parameters, and architecture of the targeted ML model. The profound impact of the proposed stealthy attack constructions on energy analytics and decision-making processes is shown through comprehensive theoretical, practical, and comparative analysis. This work sheds light on vulnerabilities of ML models in the smart grid context and provides valuable insights for securely accommodating increasing prevalence of artificial intelligence in the modern power grid.

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“…Naturally, this dimension is mostly coupled with the third distinction, whether the learning problem is formulated as a classification or regression task. However, there are four works [121,[129][130][131] where power values are clustered into groups and the power regression problem is recast into a classification task. These references are marked with P class .…”

Section: Outputmentioning

confidence: 99%

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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Ensemble-Based Deep Learning Model for Non-Intrusive Load Monitoring

Cited by 16 publications

References 12 publications

Review on Deep Neural Networks Applied to Low-Frequency NILM

Review on Deep Neural Networks Applied to Low-Frequency NILM

Stealthy Black-Box Attacks on Deep Learning Non-Intrusive Load Monitoring Models

Review on Deep Neural Networks Applied to Low-Frequency NILM

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