research-article

Bayesian model of electrical heating disaggregation

Authors:

François Culière,

Laetitia Leduc,

Alexander BelikovAuthors Info & Claims

NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring

Pages 25 - 29

https://doi.org/10.1145/3427771.3427848

Published: 18 November 2020 Publication History

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Abstract

Adoption of smart meters is a major milestone on the path of European transition to smart energy. The residential sector in France represents ≈35% of electricity consumption with ≈40% (INSEE) of households using electrical heating. The number of deployed smart meters Linky is expected to reach 35M in 2021. In this manuscript we present an analysis of 676 households with an observation period of at least 6 months, for which we have metadata, such as the year of construction and the type of heating and propose a Bayesian model of the electrical consumption conditioned on temperature that allows to disaggregate the heating component from the electrical load curve in an unsupervised manner. In essence the model is a mixture of piece-wise linear models, characterised by a temperature threshold, below which we allow a mixture of two modes to represent the latent state home/away.

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Cited By

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Kreft MBrudermueller TAnderson TStaake T(2024)Identifying Electric Water Heaters from Low-Resolution Smart Meter Data2024 IEEE Conference on Technologies for Sustainability (SusTech)10.1109/SusTech60925.2024.10553590(128-135)Online publication date: 14-Apr-2024
https://doi.org/10.1109/SusTech60925.2024.10553590
Zaeri Esfahani NAshouri AGunay HBahiraei F(2024)Energy consumption disaggregation in commercial buildings: a time series decomposition approachScience and Technology for the Built Environment10.1080/23744731.2024.230453930:6(660-674)Online publication date: 6-Feb-2024
https://doi.org/10.1080/23744731.2024.2304539
Hosseini SDelcroix BHenao NAgbossou KKelouwani S(2023)Towards Feasible Solutions for Load Monitoring in Quebec ResidencesSensors10.3390/s2316728823:16(7288)Online publication date: 21-Aug-2023
https://doi.org/10.3390/s23167288
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Index Terms

Bayesian model of electrical heating disaggregation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Mixture modeling

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NILM'20: Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring

November 2020

109 pages

ISBN:9781450381918

DOI:10.1145/3427771

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 18 November 2020

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BuildSys '20: The 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

November 18, 2020

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Kreft MBrudermueller TAnderson TStaake T(2024)Identifying Electric Water Heaters from Low-Resolution Smart Meter Data2024 IEEE Conference on Technologies for Sustainability (SusTech)10.1109/SusTech60925.2024.10553590(128-135)Online publication date: 14-Apr-2024
https://doi.org/10.1109/SusTech60925.2024.10553590
Zaeri Esfahani NAshouri AGunay HBahiraei F(2024)Energy consumption disaggregation in commercial buildings: a time series decomposition approachScience and Technology for the Built Environment10.1080/23744731.2024.230453930:6(660-674)Online publication date: 6-Feb-2024
https://doi.org/10.1080/23744731.2024.2304539
Hosseini SDelcroix BHenao NAgbossou KKelouwani S(2023)Towards Feasible Solutions for Load Monitoring in Quebec ResidencesSensors10.3390/s2316728823:16(7288)Online publication date: 21-Aug-2023
https://doi.org/10.3390/s23167288
Massidda LMarrocu M(2022)A Bayesian Approach to Unsupervised, Non-Intrusive Load DisaggregationSensors10.3390/s2212448122:12(4481)Online publication date: 14-Jun-2022
https://doi.org/10.3390/s22124481
Brudermueller TWirth FWeigert AStaake T(2022)Automatic Differentiation of Variable and Fixed Speed Heat Pumps With Smart Meter Data2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)10.1109/SmartGridComm52983.2022.9961055(412-418)Online publication date: 25-Oct-2022
https://doi.org/10.1109/SmartGridComm52983.2022.9961055
Ham SKarava PBilionis IBraun J(2022)A scalable and practical method for disaggregating heating and cooling electrical usage using smart thermostat and smart metre dataJournal of Building Performance Simulation10.1080/19401493.2022.203235215:2(251-267)Online publication date: 7-Feb-2022
https://doi.org/10.1080/19401493.2022.2032352
Belikov AMatheron GSassi J(2022)Domain knowledge aids in signal disaggregation; the example of the cumulative water heaterEnergy and Buildings10.1016/j.enbuild.2022.112200268(112200)Online publication date: Aug-2022
https://doi.org/10.1016/j.enbuild.2022.112200
Wamburu JGrazier EIrwin DCrago CShenoy PJiang XGnawali ONagy Z(2021)Towards equity in energy efficiency analysesProceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation10.1145/3486611.3492411(259-263)Online publication date: 17-Nov-2021
https://dl.acm.org/doi/10.1145/3486611.3492411

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Benefits of three-phase metering for load disaggregation

Energy disaggregation meets heating control

Disaggregation of Heat Pump Load Profiles From Low-Resolution Smart Meter Data

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