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April 24, 2022 to April 28, 2022 - Barcelona, Spain

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InfoSys 2022 Congress
May 22, 2022 to May 26, 2022 - Venice, Italy

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May 22, 2022 to May 26, 2022 - Venice, Italy

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June 26, 2022 to June 30, 2022 - Porto, Portugal

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October 16, 2022 to October 20, 2022 - Lisbon, Portugal

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SoftNet 2022 Congress
October 16, 2022 to October 20, 2022 - Lisbon, Portugal

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NexTech 2022 Congress
November 13, 2022 to November 17, 2022 - Valencia, Spain

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TrendNews 2022 Congress
November 13, 2022 to November 17, 2022 - Valencia, Spain

  • CORETA 2022, Advances on Core Technologies and Applications
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ThinkMind // ENERGY 2021, The Eleventh International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies // View article energy_2021_3_30_30020


Non-Intrusive Load Monitoring of Single and Aggregated Profiles with a Hidden Markov Model

Authors:
Nadège Miquey
Etta Grover-Silva

Keywords: load monitoring, machine learning, disaggregation, energy efficiency.

Abstract:
Awareness raising programs to encourage energy efficient behaviour are important in the context of the current energy transition. Sensors and connected devices allowing for data collection are easily available providing an opportunity to collect electric consumption data from individual appliances. The effective use of these data sources is necessary to optimize an energy efficiency coaching program. This paper presents a methodology for non-intrusive load monitoring analysis on individual smart plugs to identify an unknown appliance and disaggregated an aggregated load profile, such as a power strip. The automatic detection of a connected appliance allows for appliance usage suggestions to be provided quickly without the need of an end-user input. The disaggregation of an aggregated curve such as a power strip allows for the optimization of the number of smart plugs required to represent a significant proportion of the total energy use of the household. The down sampling of high resolution data was also performed to observe the performance of the methodology on lower resolution data. The single appliance identification models all had very high accuracy (between 94 - 100 %). The disaggregation of an aggregated profile in the kitchen use case also had high accuracy for data with a resolution of less than one minute (95 - 99 %). The disaggregation of an aggregated profile for a multi-media use case had a lower performance when more than two appliances were considered (55 - 85 %).

Pages: 48 to 53

Copyright: Copyright (c) IARIA, 2021

Publication date: May 30, 2021

Published in: conference

ISSN: 2308-412X

ISBN: 978-1-61208-855-6

Location: Valencia, Spain

Dates: from May 30, 2021 to June 3, 2021

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