<?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>1405-7743</journal-id>
<journal-title><![CDATA[Ingeniería, investigación y tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. invest. y tecnol.]]></abbrev-journal-title>
<issn>1405-7743</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Ingeniería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-77432018000400003</article-id>
<article-id pub-id-type="doi">10.22201/fi.25940732e.2018.19n4.033</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Predicción de potencia eólica utilizando técnicas modernas de Inteligencia Artificial]]></article-title>
<article-title xml:lang="en"><![CDATA[Wind power forecasting using Artificial Intelligence tools]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ibargüengoytia-González]]></surname>
<given-names><![CDATA[Pablo Héctor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Reyes-Ballesteros]]></surname>
<given-names><![CDATA[Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Borunda-Pacheco]]></surname>
<given-names><![CDATA[Mónica]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García-López]]></surname>
<given-names><![CDATA[Uriel Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Nacional de Electricidad y Energías Limpias  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Nacional de Electricidad y Energías Limpias  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Nacional de Electricidad y Energías Limpias  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Instituto Nacional de Electricidad y Energías Limpias  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<volume>19</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-77432018000400003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-77432018000400003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-77432018000400003&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Existe una tendencia mundial por el uso de energías limpias en sustitución de combustibles fósiles, dado el constante incremento de la demanda energética y el interés por preservar el medio ambiente. De las energías renovables en el ámbito mundial, la energía eólica es la que ha tenido más crecimiento en los últimos años. Sin embargo, en el caso de México se tienen ciertas dificultades aún para extender su uso en ciertas regiones del territorio nacional. Una dificultad es la de conocer con suficiente anterioridad cuánta energía se dispondrá para inyectar a la red eléctrica. El presente trabajo describe el desarrollo de una tecnología de Inteligencia Artificial (IA) para el pronóstico de potencia eólica basado en información meteorológica de varios años. Específicamente, se realizó una investigación detallada sobre el uso potencial de las Redes Bayesianas para estas aplicaciones de pronóstico. Se identificó la necesidad de considerar el tiempo en el proceso de pronóstico y se realizó una propuesta novedosa basada en Redes Bayesianas Dinámicas (RBD). El sistema de pronóstico se probó con datos meteorológicos del centro regional de tecnología eólica (CERTE) del Instituto Nacional de Electricidad y Energías Limpias (INEEL) en Oaxaca, México. Se compararon los resultados con predicciones hechas con series de tiempo y se encontraron resultados satisfactorios confirmando que las Redes Bayesianas Dinámicas son una herramienta prometedora para la predicción de la potencia eólica.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract There is a worldwide trend of using clean energy instead of fossil fuels, given the steady increase in energy demand and the interest in preserving the environment. Wind energy is the renewable energy which has grown the most at global level in recent years. However, there are still some difficulties to extend its use elsewhere in the country. One of these challenges is the difficulty of knowing in advance how much energy will be available to inject into the grid. This paper describes the development of a technique of Artificial Intelligence (AI) for wind power forecast using weather information for several years. Specifically, a detailed research on the potential use of Bayesian Networks for these forecasting applications was made. The need to consider the time in the forecasting process was identified and a new proposal of Dynamic Bayesian Networks (DBN) was performed. The forecast system with Dynamic Bayesian Networks was tested with data from the regional center for wind technology (CERTE) of the National Institute of Electricity and Clean Energies (INEEL) in Oaxaca, Mexico. Our results were satisfactorily compared with forecasting results from time series techniques indicating that DBN is a promising tool for wind power forecasting.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Energía eólica]]></kwd>
<kwd lng="es"><![CDATA[pronóstico de potencia]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia Artificial]]></kwd>
<kwd lng="es"><![CDATA[Redes Bayesianas Dinámicas]]></kwd>
<kwd lng="en"><![CDATA[Wind energy]]></kwd>
<kwd lng="en"><![CDATA[power forecast]]></kwd>
<kwd lng="en"><![CDATA[Artificial Intelligence]]></kwd>
<kwd lng="en"><![CDATA[Dynamic Bayesian Networks]]></kwd>
</kwd-group>
</article-meta>
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