Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach
- Autores
- Rossit, Diego Gabriel; Nesmachnow, Sergio; Toutouh, Jamal; Luna, Francisco
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources. In this line of work, this article presents an optimization model to schedule deferrable appliances in households, which simultaneously optimize two conflicting objectives: the minimization of the cost of electricity bill and the maximization of users satisfaction with the consumed energy. Since users satisfaction is based on human preferences, it is subjected to a great variability and, thus, stochastic resolution methods have to be applied to solve the proposed model. In turn, a maximum allowable power consumption value is included as constraint, to account for the maximum power contracted for each household or building. Two different algorithms are proposed: a simulation-optimization approach and a greedy heuristic. Both methods are evaluated over problem instances based on real-world data, accounting for different household types. The obtained results show the competitiveness of the proposed approach, which are able to compute different compromising solutions accounting for the trade-off between these two conflicting optimization criteria in reasonable computing times. The simulation-optimization obtains better solutions, outperforming and dominating the greedy heuristic in all considered scenarios.
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: Nesmachnow, Sergio. Universidad de la República; Uruguay
Fil: Toutouh, Jamal. Universidad de Málaga. Departamento de Lenguajes y Ciencias de la Computación; España
Fil: Luna, Francisco. Universidad de Málaga. Departamento de Lenguajes y Ciencias de la Computación; España - Materia
-
SMART CITIES
SMART HOMES
URBAN DATA ANALYSIS
HOUSEHOLD ENERGY PLANNING
MIXED-INTEGER PROGRAMMING
MONTE CARLO SIMULATION
BI-OBJECTIVE OPTIMIZATION
GREEDY HEURISTIC
STOCHASTIC OPTIMIZATION - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/152119
Ver los metadatos del registro completo
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Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approachRossit, Diego GabrielNesmachnow, SergioToutouh, JamalLuna, FranciscoSMART CITIESSMART HOMESURBAN DATA ANALYSISHOUSEHOLD ENERGY PLANNINGMIXED-INTEGER PROGRAMMINGMONTE CARLO SIMULATIONBI-OBJECTIVE OPTIMIZATIONGREEDY HEURISTICSTOCHASTIC OPTIMIZATIONhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources. In this line of work, this article presents an optimization model to schedule deferrable appliances in households, which simultaneously optimize two conflicting objectives: the minimization of the cost of electricity bill and the maximization of users satisfaction with the consumed energy. Since users satisfaction is based on human preferences, it is subjected to a great variability and, thus, stochastic resolution methods have to be applied to solve the proposed model. In turn, a maximum allowable power consumption value is included as constraint, to account for the maximum power contracted for each household or building. Two different algorithms are proposed: a simulation-optimization approach and a greedy heuristic. Both methods are evaluated over problem instances based on real-world data, accounting for different household types. The obtained results show the competitiveness of the proposed approach, which are able to compute different compromising solutions accounting for the trade-off between these two conflicting optimization criteria in reasonable computing times. The simulation-optimization obtains better solutions, outperforming and dominating the greedy heuristic in all considered scenarios.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; ArgentinaFil: Nesmachnow, Sergio. Universidad de la República; UruguayFil: Toutouh, Jamal. Universidad de Málaga. Departamento de Lenguajes y Ciencias de la Computación; EspañaFil: Luna, Francisco. Universidad de Málaga. Departamento de Lenguajes y Ciencias de la Computación; EspañaAmerican Institute of Mathematical Sciences2021-11-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/152119Rossit, Diego Gabriel; Nesmachnow, Sergio; Toutouh, Jamal; Luna, Francisco; Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach; American Institute of Mathematical Sciences; Mathematical Biosciences And Engineering; 19; 1; 8-11-2021; 34-651547-1063CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.aimspress.com/article/doi/10.3934/mbe.2022002info:eu-repo/semantics/altIdentifier/doi/10.3934/mbe.2022002info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:03:37Zoai:ri.conicet.gov.ar:11336/152119instacron: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 10:03:37.648CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach |
title |
Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach |
spellingShingle |
Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach Rossit, Diego Gabriel SMART CITIES SMART HOMES URBAN DATA ANALYSIS HOUSEHOLD ENERGY PLANNING MIXED-INTEGER PROGRAMMING MONTE CARLO SIMULATION BI-OBJECTIVE OPTIMIZATION GREEDY HEURISTIC STOCHASTIC OPTIMIZATION |
title_short |
Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach |
title_full |
Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach |
title_fullStr |
Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach |
title_full_unstemmed |
Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach |
title_sort |
Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach |
dc.creator.none.fl_str_mv |
Rossit, Diego Gabriel Nesmachnow, Sergio Toutouh, Jamal Luna, Francisco |
author |
Rossit, Diego Gabriel |
author_facet |
Rossit, Diego Gabriel Nesmachnow, Sergio Toutouh, Jamal Luna, Francisco |
author_role |
author |
author2 |
Nesmachnow, Sergio Toutouh, Jamal Luna, Francisco |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
SMART CITIES SMART HOMES URBAN DATA ANALYSIS HOUSEHOLD ENERGY PLANNING MIXED-INTEGER PROGRAMMING MONTE CARLO SIMULATION BI-OBJECTIVE OPTIMIZATION GREEDY HEURISTIC STOCHASTIC OPTIMIZATION |
topic |
SMART CITIES SMART HOMES URBAN DATA ANALYSIS HOUSEHOLD ENERGY PLANNING MIXED-INTEGER PROGRAMMING MONTE CARLO SIMULATION BI-OBJECTIVE OPTIMIZATION GREEDY HEURISTIC STOCHASTIC OPTIMIZATION |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/2.11 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources. In this line of work, this article presents an optimization model to schedule deferrable appliances in households, which simultaneously optimize two conflicting objectives: the minimization of the cost of electricity bill and the maximization of users satisfaction with the consumed energy. Since users satisfaction is based on human preferences, it is subjected to a great variability and, thus, stochastic resolution methods have to be applied to solve the proposed model. In turn, a maximum allowable power consumption value is included as constraint, to account for the maximum power contracted for each household or building. Two different algorithms are proposed: a simulation-optimization approach and a greedy heuristic. Both methods are evaluated over problem instances based on real-world data, accounting for different household types. The obtained results show the competitiveness of the proposed approach, which are able to compute different compromising solutions accounting for the trade-off between these two conflicting optimization criteria in reasonable computing times. The simulation-optimization obtains better solutions, outperforming and dominating the greedy heuristic in all considered scenarios. 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: Nesmachnow, Sergio. Universidad de la República; Uruguay Fil: Toutouh, Jamal. Universidad de Málaga. Departamento de Lenguajes y Ciencias de la Computación; España Fil: Luna, Francisco. Universidad de Málaga. Departamento de Lenguajes y Ciencias de la Computación; España |
description |
In the last decades, cities have increased the number of activities and services that depends on an efficient and reliable electricity service. In particular, households have had a sustained increase of electricity consumption to perform many residential activities. Thus, providing efficient methods to enhance the decision making processes in demand-side management is crucial for achieving a more sustainable usage of the available resources. In this line of work, this article presents an optimization model to schedule deferrable appliances in households, which simultaneously optimize two conflicting objectives: the minimization of the cost of electricity bill and the maximization of users satisfaction with the consumed energy. Since users satisfaction is based on human preferences, it is subjected to a great variability and, thus, stochastic resolution methods have to be applied to solve the proposed model. In turn, a maximum allowable power consumption value is included as constraint, to account for the maximum power contracted for each household or building. Two different algorithms are proposed: a simulation-optimization approach and a greedy heuristic. Both methods are evaluated over problem instances based on real-world data, accounting for different household types. The obtained results show the competitiveness of the proposed approach, which are able to compute different compromising solutions accounting for the trade-off between these two conflicting optimization criteria in reasonable computing times. The simulation-optimization obtains better solutions, outperforming and dominating the greedy heuristic in all considered scenarios. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-08 |
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/11336/152119 Rossit, Diego Gabriel; Nesmachnow, Sergio; Toutouh, Jamal; Luna, Francisco; Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach; American Institute of Mathematical Sciences; Mathematical Biosciences And Engineering; 19; 1; 8-11-2021; 34-65 1547-1063 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/152119 |
identifier_str_mv |
Rossit, Diego Gabriel; Nesmachnow, Sergio; Toutouh, Jamal; Luna, Francisco; Scheduling deferrable electric appliances in Smart Homes: a bi-objective stochastic optimization approach; American Institute of Mathematical Sciences; Mathematical Biosciences And Engineering; 19; 1; 8-11-2021; 34-65 1547-1063 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.aimspress.com/article/doi/10.3934/mbe.2022002 info:eu-repo/semantics/altIdentifier/doi/10.3934/mbe.2022002 |
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/pdf |
dc.publisher.none.fl_str_mv |
American Institute of Mathematical Sciences |
publisher.none.fl_str_mv |
American Institute of Mathematical Sciences |
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 |
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1842269810902171648 |
score |
13.13397 |