Autonomic management of a building's multi-HVAC system start-up

Most studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC sy...

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Autores: Aguilar Castro, José Lisandro|||0000-0003-4194-6882, Garcés Jiménez, Alberto|||0000-0002-1365-9280, Gómez Pulido, José Manuel|||0000-0002-6897-8262, Rodríguez Moreno, María Dolores|||0000-0002-7024-0427, Gutiérrez de Mesa, José Antonio|||0000-0002-3073-4369, Gallego Salvador, Nuria
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/48257
Acceso en línea:http://hdl.handle.net/10017/48257
https://dx.doi.org/10.1109/ACCESS.2021.3078550
Access Level:acceso abierto
Palabra clave:Energy management
Beating
Ventilation and air conditioning systems
Autonomic computing
Machine learning
Multi-objective optimization
Smart building
Informática
Computer science
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spelling Autonomic management of a building's multi-HVAC system start-upAguilar Castro, José Lisandro|||0000-0003-4194-6882Garcés Jiménez, Alberto|||0000-0002-1365-9280Gómez Pulido, José Manuel|||0000-0002-6897-8262Rodríguez Moreno, María Dolores|||0000-0002-7024-0427Gutiérrez de Mesa, José Antonio|||0000-0002-3073-4369Gallego Salvador, NuriaEnergy managementBeatingVentilation and air conditioning systemsAutonomic computingMachine learningMulti-objective optimizationSmart buildingInformáticaComputer scienceMost studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the "Autonomic Cycle of Data Analysis Tasks" concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects.European CommissionJunta de Comunidades de Castilla-La ManchaAgencia Estatal de InvestigaciónIEEE20212021-05-10journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/48257https://dx.doi.org/10.1109/ACCESS.2021.3078550reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)InglésengEuropean Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 754382 GOT Energy TalentJunta de Comunidades de Castilla-La Mancha http://dx.doi.org/10.13039/501100011698 Not available SBPLY%2F19%2F180501%2F000024 MEJORA DE LA GESTIÓN DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCIÓN DE LA DEMANDA CON APRENDIZAJE AUTOMÁTICO Y PLANIFICACIÓNAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-109891RB-I00 MEJORA DE LA GESTION DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCION DE LA DEMANDA CON APRENDIZAJE AUTOMATICO Y PLANIFICACIONopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/482572026-06-18T11:13:07Z
dc.title.none.fl_str_mv Autonomic management of a building's multi-HVAC system start-up
title Autonomic management of a building's multi-HVAC system start-up
spellingShingle Autonomic management of a building's multi-HVAC system start-up
Aguilar Castro, José Lisandro|||0000-0003-4194-6882
Energy management
Beating
Ventilation and air conditioning systems
Autonomic computing
Machine learning
Multi-objective optimization
Smart building
Informática
Computer science
title_short Autonomic management of a building's multi-HVAC system start-up
title_full Autonomic management of a building's multi-HVAC system start-up
title_fullStr Autonomic management of a building's multi-HVAC system start-up
title_full_unstemmed Autonomic management of a building's multi-HVAC system start-up
title_sort Autonomic management of a building's multi-HVAC system start-up
dc.creator.none.fl_str_mv Aguilar Castro, José Lisandro|||0000-0003-4194-6882
Garcés Jiménez, Alberto|||0000-0002-1365-9280
Gómez Pulido, José Manuel|||0000-0002-6897-8262
Rodríguez Moreno, María Dolores|||0000-0002-7024-0427
Gutiérrez de Mesa, José Antonio|||0000-0002-3073-4369
Gallego Salvador, Nuria
author Aguilar Castro, José Lisandro|||0000-0003-4194-6882
author_facet Aguilar Castro, José Lisandro|||0000-0003-4194-6882
Garcés Jiménez, Alberto|||0000-0002-1365-9280
Gómez Pulido, José Manuel|||0000-0002-6897-8262
Rodríguez Moreno, María Dolores|||0000-0002-7024-0427
Gutiérrez de Mesa, José Antonio|||0000-0002-3073-4369
Gallego Salvador, Nuria
author_role author
author2 Garcés Jiménez, Alberto|||0000-0002-1365-9280
Gómez Pulido, José Manuel|||0000-0002-6897-8262
Rodríguez Moreno, María Dolores|||0000-0002-7024-0427
Gutiérrez de Mesa, José Antonio|||0000-0002-3073-4369
Gallego Salvador, Nuria
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Energy management
Beating
Ventilation and air conditioning systems
Autonomic computing
Machine learning
Multi-objective optimization
Smart building
Informática
Computer science
topic Energy management
Beating
Ventilation and air conditioning systems
Autonomic computing
Machine learning
Multi-objective optimization
Smart building
Informática
Computer science
description Most studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the "Autonomic Cycle of Data Analysis Tasks" concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-05-10
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/48257
https://dx.doi.org/10.1109/ACCESS.2021.3078550
url http://hdl.handle.net/10017/48257
https://dx.doi.org/10.1109/ACCESS.2021.3078550
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 754382 GOT Energy Talent
Junta de Comunidades de Castilla-La Mancha http://dx.doi.org/10.13039/501100011698 Not available SBPLY%2F19%2F180501%2F000024 MEJORA DE LA GESTIÓN DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCIÓN DE LA DEMANDA CON APRENDIZAJE AUTOMÁTICO Y PLANIFICACIÓN
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-109891RB-I00 MEJORA DE LA GESTION DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCION DE LA DEMANDA CON APRENDIZAJE AUTOMATICO Y PLANIFICACION
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
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-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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