Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor

Ruzzelli, Antonio G.; Nicolas, Clément; Schoofs, Anthony; O’Hare, Gregory M. P.

doi:10.1109/secon.2010.5508244

Cited by 209 publications

(113 citation statements)

References 6 publications

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“…After edge detection, features (for example, active power) are extracted in the identified event windows, and then the events are classified into pre-defined categories, each corresponding to one known appliance. Different state-of-the-art classification tools have been used, including SVM (for example, in [22], [23], [24]), neural networks (for example, in [25], [26]), and decision trees [27], [4]. However, the performance of these event-based NALM approaches, is limited by the event detection tool employed.…”

Section: B Low-rate Nalmmentioning

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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Section: B Low-rate Nalmmentioning

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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“…The sampling rate influences the type of features that can be used. For example, low-rate NALM approaches can use only steady-state parameters, such as active or real power [9], reactive power [2], [4], power factor [16], voltage or current waveform [17], [18].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A low-complexity energy disaggregation method: Performance and robustness

Altrabalsi

Liao

Stanković

2014

2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG)

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This version is available at https://strathprints.strath.ac.uk/51404/ 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

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“…Conversely, refs. [6][7][8] address the problem of identifying user activities by analyzing the spectral fingerprint of the energy consumption due to the used appliances during their activities. Ref.…”

Section: Introductionmentioning

confidence: 99%

Computational Topology to Monitor Human Occupancy

Barsocchi

Cassarà

Giorgi

et al. 2018

International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

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Abstract:The recent advances in sensing technologies, embedded systems, and wireless communication technologies, make it possible to develop smart systems to monitor human activities continuously. The occupancy of specific areas or rooms in a smart building is an important piece of information, to infer the behavior of people, or to trigger an advanced surveillance module. We propose a method based on computational topology to infer the occupancy of a room monitored for a week by a system of low-cost sensors.

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Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor

Cited by 209 publications

References 6 publications

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

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

A low-complexity energy disaggregation method: Performance and robustness

Computational Topology to Monitor Human Occupancy

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