A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem

Faustine, Anthony; Mvungi, Nerey H.; Kaijage, Shubi; Kisangiri, Michael

doi:10.48550/arxiv.1703.00785

Cited by 40 publications

(34 citation statements)

References 59 publications

(110 reference statements)

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“…In the course of the years, a vast number of publicly-available data sets have been released. With NILMTK, an open-source toolkit was designed specifically to enable the comparison [11] an overview of the NILM system and its associated methods and techniques for energy disaggregation problem followed by the review of the state-of-the-art NILM algorithms.…”

Section: Scope and Contribution Of This Papermentioning

confidence: 99%

A Comprehensive Review on the NILM Algorithms for Energy Disaggregation

Verma,

Anwar,

Mahmud

et al. 2021

Preprint

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This paper presents an up to date outline of NILM framework and its related strategies and methods for the energy disaggregation problem.• The paper presents an experimental overview of the application of NILM-API, which was released with nilmtk-contrib, on three publicly available data sets, draws conclusions and highlights on future research directions.• A detailed overview on the energy disaggregation problem is presented. Here, we have shown the advantages of the NILM API through which algorithmic comparisons can be defined with relatively little model knowledge.

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Section: Scope and Contribution Of This Papermentioning

confidence: 99%

A Comprehensive Review on the NILM Algorithms for Energy Disaggregation

Verma,

Anwar,

Mahmud

et al. 2021

Preprint

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“…Shaloudegi et al [35] developed a scalable approximate inference FHMM algorithm based on a semi-definite relaxation combined with randomized rounding. Apart from FHMM, other techniques such as sparse coding [22] and support vector machines (SVMs) were also leveraged in the community [10]. Recently, deep neural networks have been introduced into energy disaggregation [5,18,36,40].…”

Section: Energy Disaggregationmentioning

confidence: 99%

Adversarial Energy Disaggregation for Non-intrusive Load Monitoring

Du,

Li,

Zhu

et al. 2021

Preprint

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Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis. NILM aims to help households understand how the energy is used and consequently tell them how to effectively manage the energy, thus allowing energy efficiency which is considered as one of the twin pillars of sustainable energy policy (i.e., energy efficiency and renewable energy). Although NILM is unidentifiable, it is widely believed that the NILM problem can be addressed by data science. Most of the existing approaches address the energy disaggregation problem by conventional techniques such as sparse coding, non-negative matrix factorization, and hidden Markov model. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM since DNNs can inherently learn the discriminative signatures of the different appliances. In this paper, we propose a novel method named adversarial energy disaggregation (AED) based on DNNs. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task. Our method trains a generator and multiple discriminators via an adversarial fashion. The proposed method not only learns shard representations for different appliances, but captures the specific multimode structures of each appliance. Extensive experiments on real-world datasets verify that our method can achieve new state-of-the-art performance.

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“…Furthermore, unusual power consumption patterns of appliances can be used to detect faulty appliances or malfunctions. A comprehensive review of NILM techniques can be obtained from [11].…”

Section: Related Workmentioning

confidence: 99%

Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring

Bousbiat

Klemenjak

Leitner

et al. 2020

2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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The need for reducing our energy consumption footprint and the increasing number of electric devices in today's homes is calling for new solutions that allow users to efficiently manage their energy consumption. Real-time feedback at device level would be of a significant benefit for this application. In addition, the aging population and their wish to be more autonomous have motivated the use of this same real-time data to indirectly monitor the household's occupants for their safety. By breaking down aggregate power consumption into its components, Non-Intrusive Load Monitoring provides information on individual appliances and their current state of operation. Since no additional metering equipment is required, residents are not confronted with intrusion into their familiar environment. Our work aims to depict an architecture supporting non-intrusive measurement with a smart electricity meter and the handling of these data using an open-source platform that allows to visualize and process real-time data about the total energy consumed. As a case study, we describe a series of measurements from common household devices and show how abnormal behavior can be detected.

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A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem

Cited by 40 publications

References 59 publications

A Comprehensive Review on the NILM Algorithms for Energy Disaggregation

A Comprehensive Review on the NILM Algorithms for Energy Disaggregation

Adversarial Energy Disaggregation for Non-intrusive Load Monitoring

Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring

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