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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9229269 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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