<?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>0120-6230</journal-id>
<journal-title><![CDATA[Revista Facultad de Ingeniería Universidad de Antioquia]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.fac.ing.univ. Antioquia]]></abbrev-journal-title>
<issn>0120-6230</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ingeniería, Universidad de Antioquia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-62302022000100026</article-id>
<article-id pub-id-type="doi">10.17533/udea.redin.20200694</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Method of monitoring and detection of failures in PV system based on machine learning]]></article-title>
<article-title xml:lang="es"><![CDATA[Método de monitoreo y detección de fallos en el sistema fotovoltaico basado en aprendizaje automático]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Benavides]]></surname>
<given-names><![CDATA[Darío Javier]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arévalo-Cordero]]></surname>
<given-names><![CDATA[Páúl]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gonzalez]]></surname>
<given-names><![CDATA[Luis G.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández-Callejo]]></surname>
<given-names><![CDATA[Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Jurado]]></surname>
<given-names><![CDATA[Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aguado]]></surname>
<given-names><![CDATA[José A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Málaga  ]]></institution>
<addr-line><![CDATA[Málaga ]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Cuenca  ]]></institution>
<addr-line><![CDATA[Cuenca ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Jaén  ]]></institution>
<addr-line><![CDATA[Linares ]]></addr-line>
<country>Spain</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de Valladolid  ]]></institution>
<addr-line><![CDATA[Soria ]]></addr-line>
<country>Spain</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2022</year>
</pub-date>
<numero>102</numero>
<fpage>26</fpage>
<lpage>43</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-62302022000100026&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-62302022000100026&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-62302022000100026&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Machine learning methods have been used to solve complicated practical problems in different areas and are becoming increasingly popular today. The purpose of this article is to evaluate the prediction of the energy production of three different photovoltaic systems and the supervision of measurement sensors, through Machine learning and data mining in response to the behavior of the climatic variables of the place under study. On the other hand, it also includes the implementation of the resulting models in the SCADA system through indicators, which will allow the operator to actively manage the electricity grid. It also offers a strategy in simulation and prediction in real-time of photovoltaic systems and measurement sensors in the concept of smart grids.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Los métodos de aprendizaje automático se han utilizado para resolver problemas prácticos complicados en diferentes áreas y se están volviendo cada vez más populares hoy en día. El propósito de este artículo es evaluar la predecición de la producción de energía de tres sistemas fotovoltaicos diferentes y la supervision de sensores de medición, por medio un aprendizaje automático y minería de datos en respuesta al comportamiento de las variables climáticas del lugar en estudio. Por otro lado, también incluye la implementación de los modelos resultantes en el sistema SCADA por medio de indicadores, que permitirá al operador gestionar activamente la red eléctrica. Ademas ofrece una estrategia en la simulación y predicción en tiempo real de sistemas fotovoltaicos y sensores de medición en el concepto de redes inteligentes.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[renewable energy sources]]></kwd>
<kwd lng="en"><![CDATA[monitoring]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[fuentes de energía renovable]]></kwd>
<kwd lng="es"><![CDATA[supervisión]]></kwd>
</kwd-group>
</article-meta>
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