Publication
Non-intrusive load monitoring of household devices using a hybrid deep learning model through convex hull-based data selection
| dc.contributor.author | Habou Laouali, Inoussa | |
| dc.contributor.author | Ruano, Antonio | |
| dc.contributor.author | Ruano, Maria da Graça | |
| dc.contributor.author | Bennani, Saad Dosse | |
| dc.contributor.author | Fadili, Hakim El | |
| dc.date.accessioned | 2022-02-14T11:16:27Z | |
| dc.date.available | 2022-02-14T11:16:27Z | |
| dc.date.issued | 2022-02-07 | |
| dc.date.updated | 2022-02-11T14:46:16Z | |
| dc.description.abstract | The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches. | pt_PT |
| dc.description.sponsorship | This research was funded by Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020, grant numbers 39578/2018 and 72581/2020. Antonio Ruano also acknowledges the support of Fundação para a Ciência e Tecnologia, grant UID/EMS/50022/2020, through IDMEC under LAETA | pt_PT |
| dc.description.sponsorship | Grant numbers 72581/2020 | |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Energies 15 (3): 1215 (2022) | pt_PT |
| dc.identifier.doi | 10.3390/en15031215 | pt_PT |
| dc.identifier.issn | doi: 10.3390/en15031215 | |
| dc.identifier.issn | 1996-1073 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/17541 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation | Associate Laboratory of Energy, Transports and Aeronautics | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Non-intrusive load monitoring | pt_PT |
| dc.subject | Energy disaggregation | pt_PT |
| dc.subject | Low frequency power data | pt_PT |
| dc.subject | Convex hull | pt_PT |
| dc.subject | Bidirectional long short time memory | pt_PT |
| dc.subject | Convolutional neural networks | pt_PT |
| dc.title | Non-intrusive load monitoring of household devices using a hybrid deep learning model through convex hull-based data selection | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Associate Laboratory of Energy, Transports and Aeronautics | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/Concurso de Projetos de Investigação Científica e Desenvolvimento Tecnológico nos domínios Prioritários do Turismo, das Energias Renováveis e TIC - Programa Operacional do Algarve - 2018/SAICT-ALG%2F39578%2F2018/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT | |
| oaire.citation.issue | 3 | pt_PT |
| oaire.citation.startPage | 1215 | pt_PT |
| oaire.citation.title | Energies | pt_PT |
| oaire.citation.volume | 15 | pt_PT |
| oaire.fundingStream | Concurso de Projetos de Investigação Científica e Desenvolvimento Tecnológico nos domínios Prioritários do Turismo, das Energias Renováveis e TIC - Programa Operacional do Algarve - 2018 | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | HABOU LAOUALI | |
| person.familyName | Ruano | |
| person.familyName | Ruano | |
| person.givenName | Inoussa | |
| person.givenName | Antonio | |
| person.givenName | Maria | |
| person.identifier.ciencia-id | 9811-A0DD-D5A5 | |
| person.identifier.orcid | 0000-0002-6078-6813 | |
| person.identifier.orcid | 0000-0002-6308-8666 | |
| person.identifier.orcid | 0000-0002-0014-9257 | |
| person.identifier.rid | B-4135-2008 | |
| person.identifier.rid | A-8321-2011 | |
| person.identifier.scopus-author-id | 7004284159 | |
| person.identifier.scopus-author-id | 7004483805 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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