<?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>S2602-84922021000200072</article-id>
<article-id pub-id-type="doi">10.37116/revistaenergia.v18.n1.2021.461</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Minería de Datos y Proyección a Corto Plazo de la Demanda de Potencia en el Sistema Eléctrico Ecuatoriano]]></article-title>
<article-title xml:lang="en"><![CDATA[Data Mining and Short-Term Projection of Power Demand in the Ecuadorian Electric System]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gallo]]></surname>
<given-names><![CDATA[Angel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez]]></surname>
<given-names><![CDATA[Fabián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Salinas]]></surname>
<given-names><![CDATA[Diego]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Escuela Politécnica Nacional  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Escuela Politécnica Nacional  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Agencia de Regulación y Control de Recursos Naturales No Renovables  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>18</volume>
<numero>1</numero>
<fpage>72</fpage>
<lpage>85</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_arttext&amp;pid=S2602-84922021000200072&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_abstract&amp;pid=S2602-84922021000200072&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.senescyt.gob.ec/scielo.php?script=sci_pdf&amp;pid=S2602-84922021000200072&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[  Resumen:  En este artículo se presenta una herramienta computacional elaborada en lenguaje de programación Python para minería de datos y proyección a corto plazo de la demanda de potencia eléctrica del Sistema Nacional Interconectado (SNI), utilizando el enfoque predictivo del algoritmo de aprendizaje automático Random Forest. La implementación de la función Hyperopt para definir los principales hiperparámetros del algoritmo Random Forest junto con la aplicación de la ingeniería de características permiten ajustar un modelo adecuado de aprendizaje automático para las series de datos. Dicho algoritmo es implementado en tareas de mitigación de valores faltantes y valores atípicos para estructurar bases de datos completos y libres de desviaciones. El procedimiento para minería de datos y proyección de demanda muestra la confiabilidad y versatilidad de utilizar la herramienta computacional obteniendo resultados relevantes, como la disminución de anomalías en las series de datos para mejorar la precisión en las curvas de demanda eléctrica proyectadas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: This article presents a computational tool developed in the Python programming language for data mining and short-term projection of the electrical power demand of the National Interconnected System (SNI), using the predictive approach of the Random Forest machine learning algorithm. The implementation of the Hyperopt function to define the main hyperparameters of the Random Forest algorithm together with the application of feature engineering allows to fit a suitable machine learning model for the data series. This algorithm is implemented in tasks to mitigate missing values and outliers to structure complete databases free of deviations. The procedure for data mining and demand projection shows the reliability and versatility of using the computational tool, obtaining relevant results, such as the reduction of anomalies in the data series to improve the precision in the projected electrical demand curves.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Aprendizaje Automático]]></kwd>
<kwd lng="es"><![CDATA[Minería de datos]]></kwd>
<kwd lng="es"><![CDATA[Potencia Eléctrica]]></kwd>
<kwd lng="es"><![CDATA[Proyección de demanda a corto plazo]]></kwd>
<kwd lng="es"><![CDATA[Sistema Nacional Interconectado]]></kwd>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
<kwd lng="en"><![CDATA[Data mining]]></kwd>
<kwd lng="en"><![CDATA[Electrical Power]]></kwd>
<kwd lng="en"><![CDATA[Short-term load forecasting]]></kwd>
<kwd lng="en"><![CDATA[National Interconnected System.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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