A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings

Increasing the energy efficiency of the built environment has become a priority worldwide and especially in Europe. Because of the relatively low turnover rate of the existing built environment, energy efficiency retrofitting appears to be a fundamental step in reducing its energy consumption. Last...

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Autores: Grillone, Benedetto, Danov, Stoyan, Sumper, Andreas|||0000-0002-5628-1660, Cipriano Lindez, Jordi, Mor, Gerard
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/334747
Acceso en línea:https://hdl.handle.net/2117/334747
https://dx.doi.org/10.1016/j.rser.2020.110027
Access Level:acceso abierto
Palabra clave:Renewable energy sources
Buildings -- Energy conservation
Building energy retrofitting
Energy savings evaluation
Data-driven approach
Measurement and verification
Retrofitting decision support
Energy performance improvement
Energies renovables
Edificis -- Estalvi d'energia
Àrees temàtiques de la UPC::Energies
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spelling A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildingsGrillone, BenedettoDanov, StoyanSumper, Andreas|||0000-0002-5628-1660Cipriano Lindez, JordiMor, GerardRenewable energy sourcesBuildings -- Energy conservationBuilding energy retrofittingEnergy savings evaluationData-driven approachMeasurement and verificationRetrofitting decision supportEnergy performance improvementEnergies renovablesEdificis -- Estalvi d'energiaÀrees temàtiques de la UPC::EnergiesIncreasing the energy efficiency of the built environment has become a priority worldwide and especially in Europe. Because of the relatively low turnover rate of the existing built environment, energy efficiency retrofitting appears to be a fundamental step in reducing its energy consumption. Last experiences have shown that there is a vast energy efficiency potential lying in the building stock, and it is mainly untapped. One of the reasons is a lack of robust methodologies able to evaluate the effect of applied energy efficiency measures and inform about the expected impact of potential retrofitting strategies. Nowadays, dynamic measured data coming from automated metering infrastructure provides valuable information to evaluate the effect of energy conservation strategies. For this reason, energy performance modeling and assessment methods based on this data are starting to play a major role. In this paper, several methodologies for the measurement and verification of energy savings, and for the prediction and recommendation of energy retrofitting strategies, are analysed in detail. Practitioners looking at different options for these two processes, will find in this review a thorough and detailed overview of the different methods that can be used. Guidance is also provided to determine which method could work best depending on the specific case under analysis. The reviewed approaches include statistical learning models, machine learning models, Bayesian methods, deterministic approaches, and hybrid techniques that combine deterministic and data-driven modeling. Existing research gaps are identified and prospects for future investigation are presented within the main conclusions of this research work.Peer Reviewed20202020-10-0120202020-12-22journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/334747https://dx.doi.org/10.1016/j.rser.2020.110027reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3347472026-05-27T15:37:01Z
dc.title.none.fl_str_mv A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings
title A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings
spellingShingle A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings
Grillone, Benedetto
Renewable energy sources
Buildings -- Energy conservation
Building energy retrofitting
Energy savings evaluation
Data-driven approach
Measurement and verification
Retrofitting decision support
Energy performance improvement
Energies renovables
Edificis -- Estalvi d'energia
Àrees temàtiques de la UPC::Energies
title_short A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings
title_full A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings
title_fullStr A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings
title_full_unstemmed A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings
title_sort A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings
dc.creator.none.fl_str_mv Grillone, Benedetto
Danov, Stoyan
Sumper, Andreas|||0000-0002-5628-1660
Cipriano Lindez, Jordi
Mor, Gerard
author Grillone, Benedetto
author_facet Grillone, Benedetto
Danov, Stoyan
Sumper, Andreas|||0000-0002-5628-1660
Cipriano Lindez, Jordi
Mor, Gerard
author_role author
author2 Danov, Stoyan
Sumper, Andreas|||0000-0002-5628-1660
Cipriano Lindez, Jordi
Mor, Gerard
author2_role author
author
author
author
dc.subject.none.fl_str_mv Renewable energy sources
Buildings -- Energy conservation
Building energy retrofitting
Energy savings evaluation
Data-driven approach
Measurement and verification
Retrofitting decision support
Energy performance improvement
Energies renovables
Edificis -- Estalvi d'energia
Àrees temàtiques de la UPC::Energies
topic Renewable energy sources
Buildings -- Energy conservation
Building energy retrofitting
Energy savings evaluation
Data-driven approach
Measurement and verification
Retrofitting decision support
Energy performance improvement
Energies renovables
Edificis -- Estalvi d'energia
Àrees temàtiques de la UPC::Energies
description Increasing the energy efficiency of the built environment has become a priority worldwide and especially in Europe. Because of the relatively low turnover rate of the existing built environment, energy efficiency retrofitting appears to be a fundamental step in reducing its energy consumption. Last experiences have shown that there is a vast energy efficiency potential lying in the building stock, and it is mainly untapped. One of the reasons is a lack of robust methodologies able to evaluate the effect of applied energy efficiency measures and inform about the expected impact of potential retrofitting strategies. Nowadays, dynamic measured data coming from automated metering infrastructure provides valuable information to evaluate the effect of energy conservation strategies. For this reason, energy performance modeling and assessment methods based on this data are starting to play a major role. In this paper, several methodologies for the measurement and verification of energy savings, and for the prediction and recommendation of energy retrofitting strategies, are analysed in detail. Practitioners looking at different options for these two processes, will find in this review a thorough and detailed overview of the different methods that can be used. Guidance is also provided to determine which method could work best depending on the specific case under analysis. The reviewed approaches include statistical learning models, machine learning models, Bayesian methods, deterministic approaches, and hybrid techniques that combine deterministic and data-driven modeling. Existing research gaps are identified and prospects for future investigation are presented within the main conclusions of this research work.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-10-01
2020
2020-12-22
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/334747
https://dx.doi.org/10.1016/j.rser.2020.110027
url https://hdl.handle.net/2117/334747
https://dx.doi.org/10.1016/j.rser.2020.110027
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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