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A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation

Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregatin...

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Detalles Bibliográficos
Autores principales: Massidda, Luca, Marrocu, Marino
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229269/
https://www.ncbi.nlm.nih.gov/pubmed/35746263
http://dx.doi.org/10.3390/s22124481
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author Massidda, Luca
Marrocu, Marino
author_facet Massidda, Luca
Marrocu, Marino
author_sort Massidda, Luca
collection PubMed
description Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmental conditions and occupants’ habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components.
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spelling pubmed-92292692022-06-25 A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation Massidda, Luca Marrocu, Marino Sensors (Basel) Article Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmental conditions and occupants’ habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components. MDPI 2022-06-14 /pmc/articles/PMC9229269/ /pubmed/35746263 http://dx.doi.org/10.3390/s22124481 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Massidda, Luca
Marrocu, Marino
A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation
title A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation
title_full A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation
title_fullStr A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation
title_full_unstemmed A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation
title_short A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation
title_sort bayesian approach to unsupervised, non-intrusive load disaggregation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9229269/
https://www.ncbi.nlm.nih.gov/pubmed/35746263
http://dx.doi.org/10.3390/s22124481
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