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

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<subfield code="a">Oliveira Lima, Jean Phelipe de </subfield>
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<subfield code="a">Smart monitoring of electrical circuits for distinction of connected devices through current pattern analysis using machine learning algorithms</subfield>
<subfield code="c">Jean Phelipe de Oliveira Lima, Carlos Maurício Seródio Figueiredo</subfield>
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<subfield code="a">Artículo en portugués</subfield>
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<subfield code="a">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.</subfield>
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<subfield code="a">Inteligencia artificial</subfield>
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<subfield code="a">Machine learning</subfield>
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<subfield code="a">Eficiencia energética</subfield>
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<subfield code="a">Consumo energético</subfield>
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<subfield code="a">Seródio Figueiredo, Carlos Maurício </subfield>
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<subfield code="t">Revista Iberoamericana de Inteligencia Artificial</subfield>
<subfield code="d">IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</subfield>
<subfield code="x">1988-3064</subfield>
<subfield code="g">31/12/2020 Volumen 23 Número 66 - diciembre 2020 , p. 36-50</subfield>
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