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Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation

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Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation. / Schirmer, Pascal; Mporas, Iosif; Sheikh-Akbari, Akbar .

In: Energies, Vol. 14, No. 9, 2485, 27.04.2021.

Research output: Contribution to journalArticlepeer-review

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Author

Schirmer, Pascal ; Mporas, Iosif ; Sheikh-Akbari, Akbar . / Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation. In: Energies. 2021 ; Vol. 14, No. 9.

Bibtex

@article{b7d9ebf2f3a54ab9a54d00b51fa1477b,
title = "Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation",
abstract = "Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house{\textquoteright}s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm.",
author = "Pascal Schirmer and Iosif Mporas and Akbar Sheikh-Akbari",
note = "{\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)",
year = "2021",
month = apr,
day = "27",
doi = "10.3390/en14092485",
language = "English",
volume = "14",
journal = "Energies",
issn = "1996-1073",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "9",

}

RIS

TY - JOUR

T1 - Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation

AU - Schirmer, Pascal

AU - Mporas, Iosif

AU - Sheikh-Akbari, Akbar

N1 - © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)

PY - 2021/4/27

Y1 - 2021/4/27

N2 - Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house’s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm.

AB - Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house’s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm.

U2 - 10.3390/en14092485

DO - 10.3390/en14092485

M3 - Article

VL - 14

JO - Energies

JF - Energies

SN - 1996-1073

IS - 9

M1 - 2485

ER -