Unsupervised recognition and prediction of daily patterns in heating loads in buildings

This paper presents a multistep methodology combining unsupervised and supervised learning techniques for the identification of the daily heating energy consumption patterns in buildings. The relevant number of typical profiles is obtained through unsupervised clustering processes. Then Classificati...

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Detalles Bibliográficos
Autores: Lumbreras Mugaguren, Mikel, Diarce Belloso, Gonzalo, Martín Escudero, Koldobika, Garay Martínez, R., Arregui, Beñat
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/61847
Acceso en línea:http://hdl.handle.net/10810/61847
Access Level:acceso abierto
Palabra clave:pattern recognition
unsupervised clustering
heating loads
daily profiles
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spelling Unsupervised recognition and prediction of daily patterns in heating loads in buildingsLumbreras Mugaguren, MikelDiarce Belloso, GonzaloMartín Escudero, KoldobikaGaray Martínez, R.Arregui, Beñatpattern recognitionunsupervised clusteringheating loadsdaily profilesThis paper presents a multistep methodology combining unsupervised and supervised learning techniques for the identification of the daily heating energy consumption patterns in buildings. The relevant number of typical profiles is obtained through unsupervised clustering processes. Then Classification and Regression Trees are used to predict the profile type corresponding to external variables, including calendar and climatic variables, from any given day. The methodology is tested with a variety of datasets for three different buildings with different uses connected to the district heating network in Tartu (Estonia). The three buildings under analysis present different energy behaviors (residential, kindergarten and commercial buildings). The paper shows that unsupervised clustering is effective for pattern recognition since the results from the classification and regression trees match the results from the unsupervised clustering. Three main patterns have been identified in each building, seasonality and daily mean temperature being the variables that have the greatest effect. The results concluded that the best classification accuracy is obtained with a small number of clusters with a classification accuracy from 0.7 to 0.85, approximately.The authors would like to thank GREN Eesti [44] for providing data from the substations for academic purposes. The authors would like to acknowledge the Spanish Ministry of Science and Innovation (MICINN) for funding through the Sweet-TES research project (RTI2018-099557-B-C22). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768567.ElsevierEuropean Commission202320232023info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/61847reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/EC/H2020/768567info:eu-repo/grantAgreement/MICIU/RTI2018-099557-B-C22/https://www.sciencedirect.com/science/article/pii/S2352710222017387info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/3.0/es/© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Atribución-NoComercial-SinDerivadas 3.0 Españaoai:addi.ehu.eus:10810/618472026-06-18T09:23:17Z
dc.title.none.fl_str_mv Unsupervised recognition and prediction of daily patterns in heating loads in buildings
title Unsupervised recognition and prediction of daily patterns in heating loads in buildings
spellingShingle Unsupervised recognition and prediction of daily patterns in heating loads in buildings
Lumbreras Mugaguren, Mikel
pattern recognition
unsupervised clustering
heating loads
daily profiles
title_short Unsupervised recognition and prediction of daily patterns in heating loads in buildings
title_full Unsupervised recognition and prediction of daily patterns in heating loads in buildings
title_fullStr Unsupervised recognition and prediction of daily patterns in heating loads in buildings
title_full_unstemmed Unsupervised recognition and prediction of daily patterns in heating loads in buildings
title_sort Unsupervised recognition and prediction of daily patterns in heating loads in buildings
dc.creator.none.fl_str_mv Lumbreras Mugaguren, Mikel
Diarce Belloso, Gonzalo
Martín Escudero, Koldobika
Garay Martínez, R.
Arregui, Beñat
author Lumbreras Mugaguren, Mikel
author_facet Lumbreras Mugaguren, Mikel
Diarce Belloso, Gonzalo
Martín Escudero, Koldobika
Garay Martínez, R.
Arregui, Beñat
author_role author
author2 Diarce Belloso, Gonzalo
Martín Escudero, Koldobika
Garay Martínez, R.
Arregui, Beñat
author2_role author
author
author
author
dc.contributor.none.fl_str_mv European Commission
dc.subject.none.fl_str_mv pattern recognition
unsupervised clustering
heating loads
daily profiles
topic pattern recognition
unsupervised clustering
heating loads
daily profiles
description This paper presents a multistep methodology combining unsupervised and supervised learning techniques for the identification of the daily heating energy consumption patterns in buildings. The relevant number of typical profiles is obtained through unsupervised clustering processes. Then Classification and Regression Trees are used to predict the profile type corresponding to external variables, including calendar and climatic variables, from any given day. The methodology is tested with a variety of datasets for three different buildings with different uses connected to the district heating network in Tartu (Estonia). The three buildings under analysis present different energy behaviors (residential, kindergarten and commercial buildings). The paper shows that unsupervised clustering is effective for pattern recognition since the results from the classification and regression trees match the results from the unsupervised clustering. Three main patterns have been identified in each building, seasonality and daily mean temperature being the variables that have the greatest effect. The results concluded that the best classification accuracy is obtained with a small number of clusters with a classification accuracy from 0.7 to 0.85, approximately.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/61847
url http://hdl.handle.net/10810/61847
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/EC/H2020/768567
info:eu-repo/grantAgreement/MICIU/RTI2018-099557-B-C22/
https://www.sciencedirect.com/science/article/pii/S2352710222017387
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Atribución-NoComercial-SinDerivadas 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Atribución-NoComercial-SinDerivadas 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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