A Systematic Review on Low-Resolution NILM: Datasets, Algorithms, and Challenges

Chavan, Deepika R.; More, Dagadu S.

doi:10.1007/978-981-16-9488-2_9

Cited by 4 publications

(3 citation statements)

References 58 publications

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“…Following the recent NILM review papers [1,4,8,16,19], we adapt the DL seq2subseq NILM approach of [15], shortlisted in [8] as one of the best performing on standard household appliances and demonstrated on the PECAN [9] dataset in [21].…”

Section: Methodsmentioning

confidence: 99%

Benefits of three-phase metering for load disaggregation

Vavouris

Stanković

et al. 2022

Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

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With the ever increasing pace of introduction of energy intensive devices and services, such as electric vehicle (EV) charging and heat pumps, the transition to smart metering for three-phase electric installations for nationwide smart meter roll-outs is underway. In this paper, we explore how three-phase metering can benefit nonintrusive load monitoring (NILM), especially for those appliances that are difficult to disaggregate and not widely reported in the literature. Traditionally, the NILM literature tends to tackle threephase metering by summing the three phases, without exploiting the potential benefits of load disaggregation per phase. Emphasis is placed on the disaggregation performance and loss that is introduced when using different levels of granularity of low-frequency data. Finally, we augment a public dataset with which phase the appliance is connected to, and release a three-phase electric vehicle dataset from three-phase aggregate measurements.

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Section: Methodsmentioning

confidence: 99%

Benefits of three-phase metering for load disaggregation

Vavouris

Stanković

et al. 2022

Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

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“…Open Data: Nowadays, there is a great number of publicly available datasets for NILM [1,56]. In the current research, UK-DALE [57] was used, which contains ground truth and total consumption measurements for five households in the UK for more than four years.…”

Section: Data Sourcesmentioning

confidence: 99%

HeartDIS: A Generalizable End-to-End Energy Disaggregation Pipeline

Dimitriadis,

Virtsionis Gkalinikis,

Gkiouzelis

et al. 2023

Energies

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The need for a more energy-efficient future is now more evident than ever. Energy disagreggation (NILM) methodologies have been proposed as an effective solution for the reduction in energy consumption. However, there is a wide range of challenges that NILM faces that still have not been addressed. Herein, we propose HeartDIS, a generalizable energy disaggregation pipeline backed by an extensive set of experiments, whose aim is to tackle the performance and efficiency of NILM models with respect to the available data. Our research (i) shows that personalized machine learning models can outperform more generic models; (ii) evaluates the generalization capabilities of these models through a wide range of experiments, highlighting the fact that the combination of synthetic data, the decreased volume of real data, and fine-tuning can provide comparable results; (iii) introduces a more realistic synthetic data generation pipeline based on other state-of-the-art methods; and, finally, (iv) facilitates further research in the field by publicly sharing synthetic and real data for the energy consumption of two households and their appliances.

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“…Therefore, with the help of effective energy management solutions and through fine-grained energy monitoring and analysis, improving energy structure and optimizing electricity consumption habits are effective means to improve energy efficiency. The non-intrusive load monitoring method was proposed by Professor Hart of the Massachusetts Institute of Technology 2 , and has received a lot of attention from researchers [3][4][5][6][7][8] . The NILM method collects power data by installing an intelligent acquisition module at the household end, and analyzes the collected data with the help of statistical learning methods or artificial intelligence technology, so as to realize real-time monitoring of all load usage conditions in the household electricity environment 9 .…”

mentioning

confidence: 99%

A NILM load identification method based on structured V-I mapping

Du,

Yin,

Zhu

et al. 2023

Sci Rep

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With the increasing number and types of global power loads and the development and popularization of smart grid technology, a large number of researches on load-level non-intrusive load monitoring technology have emerged. However, the unique power characteristics of the load make NILM face the difficult problem of low robustness of feature extraction and low accuracy of classification and identification in the recognition stage. This paper proposes a structured V-I mapping method to address the inherent limitations of traditional V-I trajectory mapping methods from a new perspective. In addition, for the verification of the V-I trajectory mapping method proposed in this paper, the complexity of load characteristics is comprehensively considered, and a lightweight convolutional neural network is designed based on AlexNet. The experimental results on the NILM dataset show that the proposed method significantly improves recognition accuracy compared to existing VI trajectory mapping methods.

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A Systematic Review on Low-Resolution NILM: Datasets, Algorithms, and Challenges

Cited by 4 publications

References 58 publications

Benefits of three-phase metering for load disaggregation

Benefits of three-phase metering for load disaggregation

HeartDIS: A Generalizable End-to-End Energy Disaggregation Pipeline

A NILM load identification method based on structured V-I mapping

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