Repository logo
 
Publication

Energy disaggregation using multi-objective genetic algorithm designed neural networks

2022-11-30Journal article Open access
Simple item page
dc.contributor.authorHabou Laouali, Inoussa
dc.contributor.authorGomes, Isaías
dc.contributor.authorRuano, Maria
dc.contributor.authorBennani, Saad Dosse
dc.contributor.authorFadili, Hakim El
dc.contributor.authorRuano, Antonio
dc.date.accessioned2022-12-20T09:57:33Z
dc.date.available2022-12-20T09:57:33Z
dc.date.issued2022-11-30
dc.date.updated2022-12-09T20:23:07Z
dc.description.abstractEnergy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.pt_PT
dc.description.sponsorshipGrant number 72581/2020
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationEnergies 15 (23): 9073 (2022)pt_PT
dc.identifier.doi10.3390/en15239073pt_PT
dc.identifier.eissn1996-1073
dc.identifier.urihttp://hdl.handle.net/10400.1/18672
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationNon-Invasive Load Monitoring for Intelligent Home Energy Management
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectNon-intrusive load monitoring (NILM)pt_PT
dc.subjectEnergy disaggregationpt_PT
dc.subjectNeural networkspt_PT
dc.subjectMulti-objective genetic algorithmpt_PT
dc.subjectLow frequency power datapt_PT
dc.subjectConvex hull algorithmspt_PT
dc.titleEnergy disaggregation using multi-objective genetic algorithm designed neural networkspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleNon-Invasive Load Monitoring for Intelligent Home Energy Management
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/SAICT-ALG%2F39578%2F2018/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT
oaire.citation.issue23pt_PT
oaire.citation.startPage9073pt_PT
oaire.citation.titleEnergiespt_PT
oaire.citation.volume15pt_PT
oaire.fundingStream9471 - RIDTI
oaire.fundingStream6817 - DCRRNI ID
person.familyNameHABOU LAOUALI
person.familyNameRuano
person.familyNameRuano
person.givenNameInoussa
person.givenNameMaria
person.givenNameAntonio
person.identifier.ciencia-id9811-A0DD-D5A5
person.identifier.orcid0000-0002-6078-6813
person.identifier.orcid0000-0002-0014-9257
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridA-8321-2011
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004483805
person.identifier.scopus-author-id7004284159
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationba2eedb0-4eca-4346-a332-969d82e740a4
relation.isAuthorOfPublication61fc8492-d73f-46ca-a3a3-4cd762a784e6
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscoveryba2eedb0-4eca-4346-a332-969d82e740a4
relation.isProjectOfPublicationd463053e-7303-47e8-bf7d-67667cb18694
relation.isProjectOfPublication9df77b70-8231-47e7-9b34-c702e9c6021c
relation.isProjectOfPublication.latestForDiscovery9df77b70-8231-47e7-9b34-c702e9c6021c

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
energies-15-09073.pdf
Size:
3.49 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.46 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

FCT2-Artigos (em revistas ou actas indexadas)