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dc.contributor.authorMellit, A.-
dc.contributor.authorBenghanem, Mohamed S.-
dc.contributor.authorKalogirou, Soteris A.-
dc.contributor.authorMassi Pavan, Alessandro-
dc.date.accessioned2023-06-28T07:04:00Z-
dc.date.available2023-06-28T07:04:00Z-
dc.date.issued2023-05-01-
dc.identifier.citationRenewable Energy, 2023, vol. 208, pp. 399-408en_US
dc.identifier.issn09601481-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/29518-
dc.description.abstractIn this paper a novel embedded system for remote monitoring and fault diagnosis of photovoltaic systems is introduced. The idea is to embed machine leaning algorithms into a low-cost edge device for real-time deployment. First, an artificial neural network is developed to detect faults. Then an effective stacking ensemble learning algorithm is developed to classify the nature of the fault. The method performance is evaluated through common error metrics such as RMSE, MAE, MAPE, r and confusion matrix. Additional algorithms are also embedded into the edge device in order to remotely control the photovoltaic array parameters. Users can be notified by email and SMS about the state of their photovoltaic array. The Blynk IoT platform is used to monitor remotely the photovoltaic array parameters. The experimental results demonstrate the ability of the proposed embedded system to diagnose and monitor the photovoltaic array with a good accuracy.en_US
dc.language.isoenen_US
dc.relation.ispartofRenewable Energyen_US
dc.rightsCopyright © Elsevier B.V.en_US
dc.subjectPhotovoltaic arrayen_US
dc.subjectFault diagnosisen_US
dc.subjectMonitoring systemen_US
dc.subjectMachine learningen_US
dc.subjectEmbedded systemen_US
dc.titleAn embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of thingsen_US
dc.typeArticleen_US
dc.collaborationUniversity of Jijelen_US
dc.collaborationIslamic University of Madinahen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationUniversity of Triesteen_US
dc.subject.categoryMechanical Engineeringen_US
dc.journalsHybrid Open Accessen_US
dc.countryCyprusen_US
dc.countryAlgeriaen_US
dc.countrySaudi Arabiaen_US
dc.countryItalyen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.renene.2023.03.096en_US
dc.identifier.scopus2-s2.0-85151264437-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85151264437-
dc.relation.volume208en_US
cut.common.academicyear2022-2023en_US
dc.identifier.spage399en_US
dc.identifier.epage408en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4497-0602-
crisitem.author.parentorgFaculty of Engineering and Technology-
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