<?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>2602-8492</journal-id>
<journal-title><![CDATA[Revista Técnica energía]]></journal-title>
<abbrev-journal-title><![CDATA[Revista Técnica energía]]></abbrev-journal-title>
<issn>2602-8492</issn>
<publisher>
<publisher-name><![CDATA[Operador Nacional de Electricidad CENACE]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2602-84922019000200070</article-id>
<article-id pub-id-type="doi">10.37116/revistaenergia.v16.n1.2019.337</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Predicción de la Generación para un Sistema Fotovoltaico mediante la aplicación de técnicas de Minería de Datos]]></article-title>
<article-title xml:lang="en"><![CDATA[Prediction of Generation in a Photovoltaic System through the application of Data Mining techniques]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fabara]]></surname>
<given-names><![CDATA[Cristian]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Maldonado]]></surname>
<given-names><![CDATA[Diego]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Soria]]></surname>
<given-names><![CDATA[Mauricio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Tovar]]></surname>
<given-names><![CDATA[Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Escuela Politécnica Nacional  Facultad de Ingeniería Eléctrica y Electrónica]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Escuela Politécnica Nacional Facultad de Ingeniería Eléctrica y Electrónica ]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Escuela Politécnica Nacional Facultad de Ingeniería Eléctrica y Electrónica ]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Escuela Politécnica Nacional Facultad de Ingeniería Eléctrica y Electrónica ]]></institution>
<addr-line><![CDATA[Quito ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>16</volume>
<numero>1</numero>
<fpage>70</fpage>
<lpage>78</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S2602-84922019000200070&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S2602-84922019000200070&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S2602-84922019000200070&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen:  Este documento presenta un modelo de predicción de generación de energía mediante técnicas de minería de datos para la central fotovoltaica ubicada en la Comunidad Paragachi, perteneciente al cantón Pimampiro (Imbabura), con un total de 14400 paneles solares y potencia nominal de 3.6 MW. Este sistema no cuenta con banco de baterías para almacenamiento, debido a esto no aporta durante las noches, pero en el día abastece a más de 2000 familias, que representa toda la población urbana de Pimampiro. Se inicia con un análisis univariante y multivariante de las variables de medición, cuyo objetivo es determinar el comportamiento, incidencia y la relación de cada variable en la generación de energía de la central. Con las variables de mayor incidencia como entrada, se entrena una máquina de aprendizaje que usa la técnica de árboles de decisión mediante bosques aleatorios (Random Forest) para predecir la generación de energía. En energías renovables, el sistema fotovoltaico es uno de los más implementados y desarrollados en la actualidad. Sin embargo, predecir la cantidad de generación que puede proveer es complicado por el comportamiento estocástico de las variables, limitando el ingreso de esta tecnología a un mercado competitivo que se integre al Sistema Nacional Interconectado de forma óptima y eficiente.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: This document presents a generation prediction model through data mining techniques for a photovoltaic plant located at Paragachi Community, belonging to Pimampiro (Imbabura), with a total of 14400 solar panels and 3.6 MW nominal power. This system does not have a battery bank for storage, for this reason, it does not provide energy at night, but during the day, it supplies the energy to 2000 households that represent Pimampiro&#8217;s urban population. It begins with a univariate and multivariate analysis of the measurement variables, whose objective is to determine the behavior, incidence and the relationship of each variable in the generation of the photovoltaic system. With the variables of higher incidence as input, a learning machine is trained; it uses the technique of decision trees through random forest to predict the generation. In renewable energies, the photovoltaic system is one of the most implemented and developed nowadays. However, predicting the amount of power it can generate is complicated by the stochastic behavior of the variables, limiting the entry of this technology into a competitive market, which can integrate into the National Interconnected System in an optimal and efficient way.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Centrales fotovoltaicas]]></kwd>
<kwd lng="es"><![CDATA[predicción de generación]]></kwd>
<kwd lng="es"><![CDATA[minería de datos]]></kwd>
<kwd lng="es"><![CDATA[máquina de aprendizaje]]></kwd>
<kwd lng="es"><![CDATA[árboles de decisión.]]></kwd>
<kwd lng="en"><![CDATA[Phot ovoltaic s ystems]]></kwd>
<kwd lng="en"><![CDATA[generation predictio n]]></kwd>
<kwd lng="en"><![CDATA[data mining]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[d ecision trees.]]></kwd>
</kwd-group>
</article-meta>
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