Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: a comparative study

Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavi...

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Autores: Garcés Jiménez, Alberto|||0000-0002-1365-9280, Gómez Pulido, José Manuel|||0000-0002-6897-8262, Gallego Salvador, Nuria, García Tejedor, Álvaro José
Formato: artículo
Fecha de publicación:2021
País:España
Recursos:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/68045
Acesso em linha:http://hdl.handle.net/10017/68045
https://dx.doi.org/10.3390/math9182181
Access Level:acceso abierto
Palavra-chave:Multi-objective optimization
Genetic algorithms
Evolutionary computation
Swarm intelligence
Heating, Ventilation and Air Conditioning (HVAC)
Metaheuristics search
Bio-inspired algorithms
Smart building
Soft computing
Informática
Computer science
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spelling Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: a comparative studyGarcés Jiménez, Alberto|||0000-0002-1365-9280Gómez Pulido, José Manuel|||0000-0002-6897-8262Gallego Salvador, NuriaGarcía Tejedor, Álvaro JoséMulti-objective optimizationGenetic algorithmsEvolutionary computationSwarm intelligenceHeating, Ventilation and Air Conditioning (HVAC)Metaheuristics searchBio-inspired algorithmsSmart buildingSoft computingInformáticaComputer scienceBuildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarm-intelligence-based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.MDPI20212021-09-07journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/68045https://dx.doi.org/10.3390/math9182181reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/680452026-06-18T11:13:07Z
dc.title.none.fl_str_mv Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: a comparative study
title Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: a comparative study
spellingShingle Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: a comparative study
Garcés Jiménez, Alberto|||0000-0002-1365-9280
Multi-objective optimization
Genetic algorithms
Evolutionary computation
Swarm intelligence
Heating, Ventilation and Air Conditioning (HVAC)
Metaheuristics search
Bio-inspired algorithms
Smart building
Soft computing
Informática
Computer science
title_short Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: a comparative study
title_full Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: a comparative study
title_fullStr Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: a comparative study
title_full_unstemmed Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: a comparative study
title_sort Genetic and swarm algorithms for optimizing the control of building HVAC systems using real data: a comparative study
dc.creator.none.fl_str_mv Garcés Jiménez, Alberto|||0000-0002-1365-9280
Gómez Pulido, José Manuel|||0000-0002-6897-8262
Gallego Salvador, Nuria
García Tejedor, Álvaro José
author Garcés Jiménez, Alberto|||0000-0002-1365-9280
author_facet Garcés Jiménez, Alberto|||0000-0002-1365-9280
Gómez Pulido, José Manuel|||0000-0002-6897-8262
Gallego Salvador, Nuria
García Tejedor, Álvaro José
author_role author
author2 Gómez Pulido, José Manuel|||0000-0002-6897-8262
Gallego Salvador, Nuria
García Tejedor, Álvaro José
author2_role author
author
author
dc.subject.none.fl_str_mv Multi-objective optimization
Genetic algorithms
Evolutionary computation
Swarm intelligence
Heating, Ventilation and Air Conditioning (HVAC)
Metaheuristics search
Bio-inspired algorithms
Smart building
Soft computing
Informática
Computer science
topic Multi-objective optimization
Genetic algorithms
Evolutionary computation
Swarm intelligence
Heating, Ventilation and Air Conditioning (HVAC)
Metaheuristics search
Bio-inspired algorithms
Smart building
Soft computing
Informática
Computer science
description Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarm-intelligence-based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-09-07
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/10017/68045
https://dx.doi.org/10.3390/math9182181
url http://hdl.handle.net/10017/68045
https://dx.doi.org/10.3390/math9182181
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 4.0 International
http://creativecommons.org/licenses/by/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 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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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