Smart metering by using &amp;#x201C;Matrix Pencil&amp;#x201D;

Najmeddine, Hala; Drissi, Khalil El Khamlichi; Pasquier, Christophe; Faure, Claire; Kerroum, Kamal; Jouannet, Thierry; Michou, Michel; Diop, Alioune

doi:10.1109/eeeic.2010.5489981

Cited by 10 publications

(9 citation statements)

References 4 publications

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“…It is an important topic to address a larger monitoring system called smart metering. The MP method in the time domain to treat the problem of non intrusive appliance load monitoring (NIALM) [4] is introduced, the main goal is to monitor load appliances in residential home and without intrusion. This technique takes the advantage from the transient and steady state analysis to improve monitoring efficiency.…”

Section: Application In the Time Domain: Electrical Load Monitoringmentioning

confidence: 99%

The Matrix Pencil method applied to smart monitoring and radar

Drissi¹,

Poljak²

2015

WIT Transactions on Modelling and Simulation

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The paper deals with the Matrix Pencil method, an efficient numerical method to identify deterministic signals based on either simulation or measurement. The method is usually referred to as a high-resolution technique as it is capable of identifying non-stationary signals, even in the presence of numerical or measurement noise, respectively. The basis functions are complex exponentials with a damping factor. Those functions are rather appropriate to identify electromagnetic waves or distributed currents with an optimal number of elements. The method is applied either in time domain, in frequency domain or in space domain allowing one to identify the original signal by means of a limited number of singular values, poles and residues.

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Section: Application In the Time Domain: Electrical Load Monitoringmentioning

confidence: 99%

The Matrix Pencil method applied to smart monitoring and radar

Drissi¹,

Poljak²

2015

WIT Transactions on Modelling and Simulation

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“…The concept of NILM was first introduced by George W. Hart [2]. NILM methods for analyzing the energy signatures are based on three primary approaches-steady-state analysis, transient-state analysis and non-traditional appliance features [1][2][3][4]. The steady-state analysis detects the changes in load identification considering stable states of devices, while the transient-state analysis focuses on the transitional states in energy consumption profile.…”

Section: Introductionmentioning

confidence: 99%

“…The last approach concentrates on determining non-typical features of the electrical devices to disaggregate them. The detailed comparative study of these three NILM approaches is reported in [1,3,4].…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Monitoring Using Current Shapelets

Hasan¹,

Chowdhury

Khan

2019

Applied Sciences

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Using a single-point sensor, non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components. To classify devices, potential features need to be extracted from the electrical signatures. In this article, a novel features extraction method based on current shapelets is proposed. Time-series current shapelets are determined from the normalized current data recorded from different devices. In general, shapelets can be defined as the subsequences constituting the most distinguished shapes of a time-series sequence from a particular class and can be used to discern the class among many subsequences from different classes. In this work, current envelopes are determined from the original current data by locating and connecting the peak points for each sample. Then, a unique approach is proposed to extract shapelets from the starting phase (device is turned on) of the time-series current envelopes. Subsequences windowed from the starting moment to a few seconds of stable device operation are taken into account. Based on these shapelets, a multi-class classification model consisting of five different supervised algorithms is developed. The performance evaluations corroborate the efficacy of the proposed framework.

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“…The feature extraction module applies the ESPRIT method, a well-known subspace-based estimation technique, to the drawn electric current. The result is a compact representation of the current in terms of complex numbers referred to as poles and residues [15,16]. These complex numbers are shown to be characteristic of the considered load and thus can serve as features for the subsequent classification layer.…”

Section: Introductionmentioning

confidence: 99%

Towards automatic setup of non intrusive appliance load monitoring – feature extraction and clustering

Chahine¹

2019

IJECE

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<p style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">Given climate change concerns and incessantly increasing energy demands of the present time, improving energy efficiency becomes of significant environmental and economic impact. Monitoring household electrical consumption through a non-intrusive appliance load monitoring (NIALM) system achieves significant efficiency improvement by providing appliance-level energy consumption and relaying this information back to the user. This paper focuses on feature extraction and clustering, which constitute two of the four modules of the proposed automatic-setup NIALM system, the other two being labeling and classification. The feature extraction module applies the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the signal in terms of complex numbers referred to as poles and residues. These complex numbers are then used to determine a feature vector consisting of the contribution of the fundamental, the third and the fifth harmonic currents to the maximum of the total load current. Once a signature is extracted, the clustering module applies distance-based rules inferred off-line from various databases and decides either to create a new class out of the new signature or to discard it and increase the count of an existing signature. As a result, the feature space is clustered without the a priori knowledge of the number of appliances into singleton clusters. Results obtained from a set of appliances indicate that these two modules succeed in creating an unlabeled database of signatures.</p>

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Smart metering by using “Matrix Pencil”

Cited by 10 publications

References 4 publications

The Matrix Pencil method applied to smart monitoring and radar

The Matrix Pencil method applied to smart monitoring and radar

Non-Intrusive Load Monitoring Using Current Shapelets

Towards automatic setup of non intrusive appliance load monitoring – feature extraction and clustering

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