Búsqueda

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"?><collection xmlns="http://www.loc.gov/MARC21/slim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
  <record>
    <leader>00000cab a2200000   4500</leader>
    <controlfield tag="001">MAP20200037262</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20220911190415.0</controlfield>
    <controlfield tag="008">201123e20201201esp|||p      |0|||b|por d</controlfield>
    <datafield tag="040" ind1=" " ind2=" ">
      <subfield code="a">MAP</subfield>
      <subfield code="b">spa</subfield>
      <subfield code="d">MAP</subfield>
    </datafield>
    <datafield tag="084" ind1=" " ind2=" ">
      <subfield code="a">922.134</subfield>
    </datafield>
    <datafield tag="100" ind1=" " ind2=" ">
      <subfield code="0">MAPA20200022954</subfield>
      <subfield code="a">Oliveira Lima, Jean Phelipe de </subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="0">
      <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>
    </datafield>
    <datafield tag="500" ind1=" " ind2=" ">
      <subfield code="a">Artículo en portugués</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <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>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080611200</subfield>
      <subfield code="a">Inteligencia artificial</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20170005476</subfield>
      <subfield code="a">Machine learning</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20110029234</subfield>
      <subfield code="a">Eficiencia energética</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080584269</subfield>
      <subfield code="a">Consumo energético</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20200023012</subfield>
      <subfield code="a">Seródio Figueiredo, Carlos Maurício </subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="w">MAP20200034445</subfield>
      <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>
    </datafield>
    <datafield tag="856" ind1=" " ind2=" ">
      <subfield code="q">application/pdf</subfield>
      <subfield code="w">1108791</subfield>
      <subfield code="y">Recurso electrónico / Electronic resource</subfield>
    </datafield>
  </record>
</collection>