A low-complexity energy disaggregation method: Performance and robustness

Altrabalsi, Hana; Liao, Jing; Stanković, Lina

doi:10.1109/ciasg.2014.7011569

Cited by 27 publications

(20 citation statements)

References 18 publications

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“…In [22], for example, k-means and SVM are combined to disaggregate washing machine in an unknown house using models generated in two 'known' houses. Deep learning is used in [34], where three neural network architectures are adapted to NALM with supervised training in known houses.…”

Section: Low-rate Non-intrusive Appliance Load Monitoring (Nalm):mentioning

confidence: 99%

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

2016

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With ongoing large-scale smart energy metering deployments worldwide, disaggregation of a household's total energy consumption down to individual appliances using analytical tools, also known as non-intrusive appliance load monitoring (NALM), has generated increased research interest lately. NALM can deepen energy feedback, support appliance retrofit advice, and support home automation. However, despite the fact that NALM was proposed over 30 years ago, there are still many open challenges with respect to its practicality and effectiveness at low sampling rates. Indeed, the majority of NALM approaches, supervised or unsupervised, require training to build appliance models, and are sensitive to appliance changes in the house, thus requiring regular re-training. In this paper, we tackle this challenge by proposing an NALM approach that does not require any training. The main idea is to build upon the emerging field of graph signal processing to perform adaptive thresholding, signal clustering, and pattern matching. We determine the performance limits of our approach and demonstrate its usefulness in practice. Using two open access datasets-the US REDD data set with active power measurements downsampled to 1 min resolution and the UK REFIT data set with 8-s resolution, we demonstrate the effectiveness of the proposed method for typical smart meter sampling rate, with the state-of-the-art supervised and unsupervised NALM approaches as benchmarks. 1 INDEX TERMS Non-intrusive appliance load monitoring, load disaggregation, graph signal processing.

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Section: Low-rate Non-intrusive Appliance Load Monitoring (Nalm):mentioning

confidence: 99%

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

2016

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“…Results of event-based approaches developed by authors are discussed in [4], [14], [32], where comparison with [11] is always provided. We use a dataset from the publicly available REDD database [18] downsampled to 1min resolution.…”

Section: Resultsmentioning

confidence: 99%

“…The key issue of these approaches is that event detection, usually performed via fixed or adaptive thresholding, limits the performance of the algorithm regardless of the classification method employed. Further details of the practical implementation and complexity of some of the approaches are discussed in [4], [14].…”

Section: Introductionmentioning

confidence: 99%

A graph-based signal processing approach for low-rate energy disaggregation

Stanković

Liao

2014

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)

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This version is available at https://strathprints.strath.ac.uk/51401/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output.A graph-based signal processing approach for low-rate energy disaggregation Abstract-Graph-based signal processing (GSP) is an emerging field that is based on representing a dataset using a discrete signal indexed by a graph. Inspired by the recent success of GSP in image processing and signal filtering, in this paper, we demonstrate how GSP can be applied to non-intrusive appliance load monitoring (NALM) due to smoothness of appliance load signatures. NALM refers to disaggregating total energy consumption in the house down to individual appliances used. At low sampling rates, in the order of minutes, NALM is a difficult problem, due to significant random noise, unknown base load, many household appliances that have similar power signatures, and the fact that most domestic appliances (for example, microwave, toaster), have usual operation of just over a minute. In this paper, we proposed a different NALM approach to more traditional approaches, by representing the dataset of active power signatures using a graph signal. We develop a regularization on graph approach where by maximizing smoothness of the underlying graph signal, we are able to perform disaggregation. Simulation results using publicly available REDD dataset demonstrate potential of the GSP for energy disaggregation and competitive performance with respect to more complex Hidden Markov Model-based approaches.

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“…Qi adopts graph shift quadratic form constraint to complete low-rate load disaggregation [32]. A novel combined k-means-SVM-based NILM method is developed [33]. However, event-based methods face a common challenge, that is, most of the existing algorithms only rely on a two-dimensional feature space of active and reactive power for load identification without considering other additional features, such as time and sequence signatures.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

Gao

et al. 2019

Applied Sciences

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Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.

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A low-complexity energy disaggregation method: Performance and robustness

Cited by 27 publications

References 18 publications

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

A graph-based signal processing approach for low-rate energy disaggregation

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

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