<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0012-7353</journal-id>
<journal-title><![CDATA[DYNA]]></journal-title>
<abbrev-journal-title><![CDATA[Dyna rev.fac.nac.minas]]></abbrev-journal-title>
<issn>0012-7353</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0012-73532022000200078</article-id>
<article-id pub-id-type="doi">10.15446/dyna.v89n221.97552</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Evaluation of losses in electrical subtransmission networks by neural network modeling]]></article-title>
<article-title xml:lang="es"><![CDATA[Modelación mediante red neuronal para la evaluación de pérdidas en redes eléctricas de subtransmisión]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Laurencio-Pérez]]></surname>
<given-names><![CDATA[Álvaro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez-Maliuk]]></surname>
<given-names><![CDATA[Igor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez-Maliuk]]></surname>
<given-names><![CDATA[Olga]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Holguín Departamento de Electrica Facultad de Ingeniería]]></institution>
<addr-line><![CDATA[Holguín ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Holguín Departamento de Mecánica Facultad de Ingeniería]]></institution>
<addr-line><![CDATA[Holguín ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Departamento de Redes y Sistemas  ]]></institution>
<addr-line><![CDATA[Holguín ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>89</volume>
<numero>221</numero>
<fpage>78</fpage>
<lpage>83</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0012-73532022000200078&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0012-73532022000200078&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0012-73532022000200078&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Determining technical losses in an electrical system is highly complex due to the large amount of information required for its evaluation. A solution to this problem is the evaluation of losses using an artificial neural network. In this work, a model for evaluating technical losses in subtransmission electrical networks was obtained by using artificial neural networks. This model considers the effective length of the circuit, the maximum apparent and active power, the resistance in the conductors and the number of clients connected to the circuit. The simulation results established a mean square error of 0.0028 and a correlation coefficient between the variables involved of 0.980. The proposed artificial neural network model resulted satisfactory for evaluating technical losses in electrical subtransmission networks.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La determinación de pérdidas técnicas en un sistema eléctrico es altamente compleja debido a la gran cantidad de información requerida para su evaluación. Una solución a este problema es la evaluación de pérdidas utilizando una red neuronal artificial. En este trabajo se obtuvo un modelo para evaluar pérdidas técnicas en redes eléctricas de subtransmisión mediante el uso de redes neuronales artificiales. Este modelo considera la longitud efectiva del circuito, la potencia máxima aparente y activa, la resistencia en los conductores y el número de clientes conectados al circuito. Los resultados de la simulación establecieron un error cuadrático medio de 0,0028 y un coeficiente de correlación entre las variables involucradas de 0,980. El modelo de red neuronal artificial propuesto es satisfactorio para evaluar pérdidas técnicas en redes de subtransmisión eléctrica.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[electric losses]]></kwd>
<kwd lng="en"><![CDATA[modeling]]></kwd>
<kwd lng="en"><![CDATA[network]]></kwd>
<kwd lng="es"><![CDATA[modelación]]></kwd>
<kwd lng="es"><![CDATA[red]]></kwd>
<kwd lng="es"><![CDATA[pérdidas eléctricas]]></kwd>
</kwd-group>
</article-meta>
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