Optimizing Energy Consumption of Household Appliances Using PSO and GWO

Detalhes bibliográficos
Autor(a) principal: Tavares, Inês
Data de Publicação: 2021
Outros Autores: Almeida, José, Soares, João, Ramos, Sérgio, Vale, Zita, Foroozandeh, Zahra
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/20668
Resumo: Due to the increasing electricity consumption in the residential sector, new control systems emerged to control the demand side. Some techniques have been developed, such as shaping the curve’s load peaks by planning and shifting the electricity demand for household appliances. This paper presents a comparative analysis for the energy consumption optimization of two household appliances using two Swarm Intelligence (SI) algorithms: Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). This problem’s main objective is to minimize the energy cost according to both machines’ energy consumption, respecting the restrictions applied. Three scenarios are presented: changing the energy market price during the day according to three types of energy tariffs. The results show that the user in the cheapest periods could switch on both machines because both techniques presented the highest energy consumption values. Regarding the objective function analysis, PSO and GWO obtained the best (more economical) values for the simple tariff due to its lower energy consumption. The GWO technique also presented more diverging values from the average objective function value than the PSO algorithm.
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spelling Optimizing Energy Consumption of Household Appliances Using PSO and GWOEnergy consumptionGrey Wolf OptimizerOptimizationParticle Swarm OptimizationSwarm IntelligenceDue to the increasing electricity consumption in the residential sector, new control systems emerged to control the demand side. Some techniques have been developed, such as shaping the curve’s load peaks by planning and shifting the electricity demand for household appliances. This paper presents a comparative analysis for the energy consumption optimization of two household appliances using two Swarm Intelligence (SI) algorithms: Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). This problem’s main objective is to minimize the energy cost according to both machines’ energy consumption, respecting the restrictions applied. Three scenarios are presented: changing the energy market price during the day according to three types of energy tariffs. The results show that the user in the cheapest periods could switch on both machines because both techniques presented the highest energy consumption values. Regarding the objective function analysis, PSO and GWO obtained the best (more economical) values for the simple tariff due to its lower energy consumption. The GWO technique also presented more diverging values from the average objective function value than the PSO algorithm.This work has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project BENEFICE–PTDC/EEI-EEE/29070/2017 and UIDB/00760/2020 un- der CEECIND/02814/2017 grant.SpringerRepositório Científico do Instituto Politécnico do PortoTavares, InêsAlmeida, JoséSoares, JoãoRamos, SérgioVale, ZitaForoozandeh, Zahra2022-07-07T08:50:54Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdftext/plain; charset=utf-8http://hdl.handle.net/10400.22/20668porTavares, I., Almeida, J., Soares, J., Ramos, S., Vale, Z., Foroozandeh, Z. (2021). Optimizing Energy Consumption of Household Appliances Using PSO and GWO. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_11978-3-030-86230-510.1007/978-3-030-86230-5_11info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T13:16:12Zoai:recipp.ipp.pt:10400.22/20668Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:40:43.347497Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Optimizing Energy Consumption of Household Appliances Using PSO and GWO
title Optimizing Energy Consumption of Household Appliances Using PSO and GWO
spellingShingle Optimizing Energy Consumption of Household Appliances Using PSO and GWO
Tavares, Inês
Energy consumption
Grey Wolf Optimizer
Optimization
Particle Swarm Optimization
Swarm Intelligence
title_short Optimizing Energy Consumption of Household Appliances Using PSO and GWO
title_full Optimizing Energy Consumption of Household Appliances Using PSO and GWO
title_fullStr Optimizing Energy Consumption of Household Appliances Using PSO and GWO
title_full_unstemmed Optimizing Energy Consumption of Household Appliances Using PSO and GWO
title_sort Optimizing Energy Consumption of Household Appliances Using PSO and GWO
author Tavares, Inês
author_facet Tavares, Inês
Almeida, José
Soares, João
Ramos, Sérgio
Vale, Zita
Foroozandeh, Zahra
author_role author
author2 Almeida, José
Soares, João
Ramos, Sérgio
Vale, Zita
Foroozandeh, Zahra
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Tavares, Inês
Almeida, José
Soares, João
Ramos, Sérgio
Vale, Zita
Foroozandeh, Zahra
dc.subject.por.fl_str_mv Energy consumption
Grey Wolf Optimizer
Optimization
Particle Swarm Optimization
Swarm Intelligence
topic Energy consumption
Grey Wolf Optimizer
Optimization
Particle Swarm Optimization
Swarm Intelligence
description Due to the increasing electricity consumption in the residential sector, new control systems emerged to control the demand side. Some techniques have been developed, such as shaping the curve’s load peaks by planning and shifting the electricity demand for household appliances. This paper presents a comparative analysis for the energy consumption optimization of two household appliances using two Swarm Intelligence (SI) algorithms: Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). This problem’s main objective is to minimize the energy cost according to both machines’ energy consumption, respecting the restrictions applied. Three scenarios are presented: changing the energy market price during the day according to three types of energy tariffs. The results show that the user in the cheapest periods could switch on both machines because both techniques presented the highest energy consumption values. Regarding the objective function analysis, PSO and GWO obtained the best (more economical) values for the simple tariff due to its lower energy consumption. The GWO technique also presented more diverging values from the average objective function value than the PSO algorithm.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01T00:00:00Z
2022-07-07T08:50:54Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/20668
url http://hdl.handle.net/10400.22/20668
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv Tavares, I., Almeida, J., Soares, J., Ramos, S., Vale, Z., Foroozandeh, Z. (2021). Optimizing Energy Consumption of Household Appliances Using PSO and GWO. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_11
978-3-030-86230-5
10.1007/978-3-030-86230-5_11
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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