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Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory

2018Journal article Open access
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dc.contributor.authorBaptista, Darío
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.contributor.authorPereira, Lucas
dc.contributor.authorSousa, Leonel
dc.contributor.authorDias, Fernando Morgado
dc.date.accessioned2024-04-23T15:05:58Z
dc.date.available2024-04-23T15:05:58Z
dc.date.issued2018
dc.description.abstractSpecific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/en11092460pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5644
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectNon-intrusive load monitoringpt_PT
dc.subjectConvolution neural networkpt_PT
dc.subjectV-I trajectorypt_PT
dc.subjectHardware classifierpt_PT
dc.subjectFPGApt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleImplementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectorypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FCEC%2F50021%2F2013/PT
oaire.citation.issue9pt_PT
oaire.citation.startPage2460pt_PT
oaire.citation.titleEnergiespt_PT
oaire.citation.volume11pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBaptista
person.familyNameMostafa
person.familyNamePereira
person.familyNameSousa
person.familyNameMorgado-Dias
person.givenNameDario
person.givenNameSheikh Shanawaz
person.givenNameLucas
person.givenNameLeonel
person.givenNameFernando
person.identifier34497
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.ciencia-id421F-7B0E-8647
person.identifier.ciencia-id7B14-DF07-AA6D
person.identifier.orcid0000-0001-9441-9221
person.identifier.orcid0000-0002-7677-0971
person.identifier.orcid0000-0002-9110-8775
person.identifier.orcid0000-0002-8066-221X
person.identifier.orcid0000-0001-7334-3993
person.identifier.ridF-5968-2015
person.identifier.ridN-9228-2015
person.identifier.ridH-4480-2014
person.identifier.ridB-2749-2009
person.identifier.scopus-author-id55489640900
person.identifier.scopus-author-id8909782300
person.identifier.scopus-author-id7004775548
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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