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Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

2019Journal article Open access
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dc.contributor.authorPinto, Tiago
dc.contributor.authorFaia, Ricardo
dc.contributor.authorNavarro-Caceres, Maria
dc.contributor.authorSantos, Gabriel
dc.contributor.authorCorchado, Juan Manuel
dc.contributor.authorVale, Zita
dc.date.accessioned2021-02-24T11:51:53Z
dc.date.available2021-02-24T11:51:53Z
dc.date.issued2019
dc.description.abstractThis paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system.pt_PT
dc.description.sponsorshipThis work was supported in part by the EU's H 2020 research and innovation programme under the Marie SklodowskaCurie Grant Agreement 641794 (project DREAM-GO) and Grant Agreement 703689 (project ADAPT), in part by the FEDER Funds through COMPETE program, and in part by the National Funds through FCT under the Project UID/EEA/00760/2013. (Corresponding author: Tiago Pinto.)pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/JSYST.2018.2876933pt_PT
dc.identifier.issn1932-8184
dc.identifier.urihttp://hdl.handle.net/10400.22/17110
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationAdaptive Decision support for Agents negotiation in electricity market and smart grid Power Transactions
dc.relationEnabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8533391pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/pt_PT
dc.subjectBuilding energy managementpt_PT
dc.subjectCase-based reasoning (CBR)pt_PT
dc.subjectEnergy efficiencypt_PT
dc.subjectMulti-agent systems (MAS)pt_PT
dc.titleMulti-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildingspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleAdaptive Decision support for Agents negotiation in electricity market and smart grid Power Transactions
oaire.awardTitleEnabling Demand Response for short and real-time Efficient And Market Based smart Grid Operation - An intelligent and real-time simulation approach
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/703689/EU
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/641794/EU
oaire.citation.endPage1095pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage1084pt_PT
oaire.citation.titleIEEE Systems Journalpt_PT
oaire.citation.volume13pt_PT
oaire.fundingStream5876
oaire.fundingStreamH2020
oaire.fundingStreamH2020
person.familyNamePinto
person.familyNameFaia
person.familyNameSantos
person.familyNameVale
person.givenNameTiago
person.givenNameRicardo Francisco Marcos
person.givenNameGabriel
person.givenNameZita
person.identifierR-000-T7J
person.identifier78FtZwIAAAAJ
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
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person.identifier.ciencia-id1413-B9E5-12BE
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-1053-7720
person.identifier.orcid0000-0001-8839-8807
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridH-7012-2018
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id48761868500
person.identifier.scopus-author-id7004115775
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameEuropean Commission
project.funder.nameEuropean Commission
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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