Smart monitoring of electrical circuits for distinction of connected devices through current pattern analysis using machine learning algorithms

<?xml version="1.0" encoding="UTF-8"?><modsCollection xmlns="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-8.xsd">
<mods version="3.8">
<titleInfo>
<title>Smart monitoring of electrical circuits for distinction of connected devices through current pattern analysis using machine learning algorithms</title>
</titleInfo>
<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20200023012">
<namePart>Seródio Figueiredo, Carlos Maurício </namePart>
<nameIdentifier>MAPA20200023012</nameIdentifier>
</name>
<typeOfResource>text</typeOfResource>
<genre authority="marcgt">periodical</genre>
<originInfo>
<place>
<placeTerm type="code" authority="marccountry">esp</placeTerm>
</place>
<dateIssued encoding="marc">2020</dateIssued>
<issuance>serial</issuance>
</originInfo>
<language>
<languageTerm type="code" authority="iso639-2b">por</languageTerm>
</language>
<physicalDescription>
<form authority="marcform">print</form>
<internetMediaType>application/pdf</internetMediaType>
</physicalDescription>
<abstract displayLabel="Summary">Energy Monitoring is a crucial activity in Energy Eciency, which involves the study of techniques to supervise the energy consumption in a power grid, regarding the main purpose is to assure a good level of detail, to achieve consumption quotas for each connected device, for a low infrastructure cost. This paper presents the evaluation of dierent Machine Learning models to classify electric current patterns to identify and monitor electric charges present in circuits with a single sensing device. The models were trained and validated by a database created from signal samples of 4 electrical devices: Notebook Charger, Refrigerator, Blender and Fan. The models that presented the best metrics achieved, respectively, 97% and 100% Accuracy and 98% and 100% F1-Score, surpassing results obtained in related researches.</abstract>
<note type="statement of responsibility">Jean Phelipe de Oliveira Lima, Carlos Maurício Seródio Figueiredo</note>
<note>Artículo en portugués</note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080611200">
<topic>Inteligencia artificial</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20170005476">
<topic>Machine learning</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20110029234">
<topic>Eficiencia energética</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080584269">
<topic>Consumo energético</topic>
</subject>
<classification authority="">922.134</classification>
<relatedItem type="host">
<titleInfo>
<title>Revista Iberoamericana de Inteligencia Artificial</title>
</titleInfo>
<originInfo>
<publisher>IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</publisher>
</originInfo>
<identifier type="issn">1988-3064</identifier>
<identifier type="local">MAP20200034445</identifier>
<part>
<text>31/12/2020 Volumen 23 Número 66 - diciembre 2020 , p. 36-50</text>
</part>
</relatedItem>
<recordInfo>
<recordContentSource authority="marcorg">MAP</recordContentSource>
<recordCreationDate encoding="marc">201123</recordCreationDate>
<recordChangeDate encoding="iso8601">20220911190415.0</recordChangeDate>
<recordIdentifier source="MAP">MAP20200037262</recordIdentifier>
<languageOfCataloging>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</languageOfCataloging>
</recordInfo>
</mods>
</modsCollection>