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

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<title>Smart monitoring of electrical circuits for distinction of connected devices through current pattern analysis using machine learning algorithms</title>
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<name type="personal" usage="primary">
<namePart>Oliveira Lima, Jean Phelipe de</namePart>
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<name type="personal">
<namePart>Seródio Figueiredo, Carlos Maurício</namePart>
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<abstract>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>
<topic>Inteligencia artificial</topic>
</subject>
<subject>
<topic>Machine learning</topic>
</subject>
<subject>
<topic>Eficiencia energética</topic>
</subject>
<subject>
<topic>Consumo energético</topic>
</subject>
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<title>Revista Iberoamericana de Inteligencia Artificial</title>
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<publisher>IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</publisher>
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<identifier type="issn">1988-3064</identifier>
<identifier type="local">MAP20200034445</identifier>
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<text>31/12/2020 Volumen 23 Número 66 - diciembre 2020 , p. 36-50</text>
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