An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes

Autores
Nesmachnow, Sergio; Rossit, Diego Gabriel; Toutouh, Jamal; Luna, Francisco
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.
Fil: Nesmachnow, Sergio. Facultad de Ingeniería; Uruguay
Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
Fil: Toutouh, Jamal. Massachusetts Institute of Technology. Science And Artificial Intelligence Laboratory; Estados Unidos
Fil: Luna, Francisco. Universidad de Málaga. Instituto de Tecnologías e Ingeniería del Software; España
Materia
SMART CITIES
ENERGY CONSUMPTION PLANNING PROBLEM
USER PREFERENCES
MULTIOBJECTIVE OPTIMIZATION
EVOLUTIONARY ALGORITHM
GREEDY ALGORITHMS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/138290
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/138290
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homesNesmachnow, SergioRossit, Diego GabrielToutouh, JamalLuna, FranciscoSMART CITIESENERGY CONSUMPTION PLANNING PROBLEMUSER PREFERENCESMULTIOBJECTIVE OPTIMIZATIONEVOLUTIONARY ALGORITHMGREEDY ALGORITHMShttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.Fil: Nesmachnow, Sergio. Facultad de Ingeniería; UruguayFil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toutouh, Jamal. Massachusetts Institute of Technology. Science And Artificial Intelligence Laboratory; Estados UnidosFil: Luna, Francisco. Universidad de Málaga. Instituto de Tecnologías e Ingeniería del Software; EspañaGrowing Science2021-05-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/zipapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/138290Nesmachnow, Sergio; Rossit, Diego Gabriel; Toutouh, Jamal; Luna, Francisco; An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes; Growing Science; International Journal of Industrial Engineering Computations; 12; 4; 12-5-2021; 365-3801923-29261923-2934CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://growingscience.com/beta/ijiec/4893-an-explicit-evolutionary-approach-for-multiobjective-energy-consumption-planning-considering-user-preferences-in-smart-homes.htmlinfo:eu-repo/semantics/altIdentifier/doi/10.5267/j.ijiec.2021.5.005info:eu-repo/semantics/altIdentifier/url/http://www.growingscience.com/ijiec/Vol12/IJIEC_2021_15.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:04:45Zoai:ri.conicet.gov.ar:11336/138290instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:04:45.913CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes
title An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes
spellingShingle An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes
Nesmachnow, Sergio
SMART CITIES
ENERGY CONSUMPTION PLANNING PROBLEM
USER PREFERENCES
MULTIOBJECTIVE OPTIMIZATION
EVOLUTIONARY ALGORITHM
GREEDY ALGORITHMS
title_short An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes
title_full An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes
title_fullStr An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes
title_full_unstemmed An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes
title_sort An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes
dc.creator.none.fl_str_mv Nesmachnow, Sergio
Rossit, Diego Gabriel
Toutouh, Jamal
Luna, Francisco
author Nesmachnow, Sergio
author_facet Nesmachnow, Sergio
Rossit, Diego Gabriel
Toutouh, Jamal
Luna, Francisco
author_role author
author2 Rossit, Diego Gabriel
Toutouh, Jamal
Luna, Francisco
author2_role author
author
author
dc.subject.none.fl_str_mv SMART CITIES
ENERGY CONSUMPTION PLANNING PROBLEM
USER PREFERENCES
MULTIOBJECTIVE OPTIMIZATION
EVOLUTIONARY ALGORITHM
GREEDY ALGORITHMS
topic SMART CITIES
ENERGY CONSUMPTION PLANNING PROBLEM
USER PREFERENCES
MULTIOBJECTIVE OPTIMIZATION
EVOLUTIONARY ALGORITHM
GREEDY ALGORITHMS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.
Fil: Nesmachnow, Sergio. Facultad de Ingeniería; Uruguay
Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
Fil: Toutouh, Jamal. Massachusetts Institute of Technology. Science And Artificial Intelligence Laboratory; Estados Unidos
Fil: Luna, Francisco. Universidad de Málaga. Instituto de Tecnologías e Ingeniería del Software; España
description Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.
publishDate 2021
dc.date.none.fl_str_mv 2021-05-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/138290
Nesmachnow, Sergio; Rossit, Diego Gabriel; Toutouh, Jamal; Luna, Francisco; An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes; Growing Science; International Journal of Industrial Engineering Computations; 12; 4; 12-5-2021; 365-380
1923-2926
1923-2934
CONICET Digital
CONICET
url http://hdl.handle.net/11336/138290
identifier_str_mv Nesmachnow, Sergio; Rossit, Diego Gabriel; Toutouh, Jamal; Luna, Francisco; An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes; Growing Science; International Journal of Industrial Engineering Computations; 12; 4; 12-5-2021; 365-380
1923-2926
1923-2934
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://growingscience.com/beta/ijiec/4893-an-explicit-evolutionary-approach-for-multiobjective-energy-consumption-planning-considering-user-preferences-in-smart-homes.html
info:eu-repo/semantics/altIdentifier/doi/10.5267/j.ijiec.2021.5.005
info:eu-repo/semantics/altIdentifier/url/http://www.growingscience.com/ijiec/Vol12/IJIEC_2021_15.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/zip
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Growing Science
publisher.none.fl_str_mv Growing Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269874571706368
score 13.13397

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