<?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>1659-4142</journal-id>
<journal-title><![CDATA[E-Ciencias de la Información]]></journal-title>
<abbrev-journal-title><![CDATA[E-Ciencias de la Información]]></abbrev-journal-title>
<issn>1659-4142</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Costa Rica, Escuela de Bibliotecología y Ciencias de la Información]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1659-41422022000100059</article-id>
<article-id pub-id-type="doi">10.15517/eci.v12i1.47628</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estado del Arte de la Predicción de Variables en Sistemas de Ingeniería Eléctrica Basada en Inteligencia Artificial]]></article-title>
<article-title xml:lang="en"><![CDATA[State of the art of Predicting Electrical Engineering Variables Based on Artificial Intelligence]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez Solís]]></surname>
<given-names><![CDATA[Joseline]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Coto Jiménez]]></surname>
<given-names><![CDATA[Marvin]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Costa Rica Escuela de Ingeniería Eléctrica ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Costa Rica Escuela de Ingeniería Eléctrica ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</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>12</volume>
<numero>1</numero>
<fpage>59</fpage>
<lpage>78</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S1659-41422022000100059&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S1659-41422022000100059&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S1659-41422022000100059&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Existe una gran cantidad de sistemas que se estudian y desarrollan en el campo de la Ingeniería Eléctrica en los que se realizan análisis que tienen como uno de sus fines principales la predicción de sus variables, tanto para procesos de planificación como de toma de decisiones. Con el advenimiento de la Inteligencia Artificial, se ha observado cómo distintas técnicas relacionadas con el aprendizaje automático y la optimización se han incorporado a estas tareas de predicción, con las cuales se obtienen generalmente mejores resultados en los valores estimados que aquellos generados a partir de técnicas más tradicionales. La presente investigación tiene como objetivo realizar una revisión de lo publicado sobre predicciones de variables en sistemas de Ingeniería Eléctrica en las bases de datos EBSCO, SciELO, RedAlyc, Springer Link, IEEE Xplorer, y Google Académico, a partir de una delimitación temporal y de palabras clave del área. A partir del análisis de la literatura se obtuvo la tendencia sobre el tema a partir de los años más productivos, áreas de impacto e idiomas más frecuentes. Se observó que los estudios desarrollados han crecido en años recientes, y que las áreas de mayor impacto, de acuerdo con el número de publicaciones y de citas son la predicción del consumo y producción de energía eléctrica, y las variables relacionadas con energías renovables.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In many systems that are studied and developed in the field of Electrical Engineering, analyzes are carried out that have as one of their main purposes the prediction of their variables, both for planning and decision-making processes. With the advent of Artificial Intelligence, it has been observed how different techniques related to machine learning and optimization have been incorporated into these prediction tasks. Those new techniques generally obtained better results in the estimation of values &#8203;&#8203;than those generated from more traditional techniques. The objective of this research is to review what has been published on predictions of variables in Electrical Engineering systems in the databases EBSCO, SciELO, RedAlyc, Springer Link, IEEE Xplorer, and Google Scholar, given specific temporal and keyworks delimitations for the area. From the analysis of the literature, the trend on the subject was obtained from the most productive years, areas of impact, and most frequent languages. It was observed that the studies developed have grown in recent years and that the areas of greatest impact, according to the number of publications and citations, are the prediction of electricity consumption and production, and the variables related to renewable energy.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Inteligencia Artificial]]></kwd>
<kwd lng="es"><![CDATA[Predicción de variables eléctricas]]></kwd>
<kwd lng="es"><![CDATA[Ingeniería Eléctrica]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[electrical variables prediction]]></kwd>
<kwd lng="en"><![CDATA[Electrical Engineering]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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