<?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>2218-3620</journal-id>
<journal-title><![CDATA[Revista Universidad y Sociedad]]></journal-title>
<abbrev-journal-title><![CDATA[Universidad y Sociedad]]></abbrev-journal-title>
<issn>2218-3620</issn>
<publisher>
<publisher-name><![CDATA[Editorial "Universo Sur"]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2218-36202021000100253</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Pronóstico de la generación eléctrica de sistemas fotovoltaicos. Un inicio en Cuba desde la universidad]]></article-title>
<article-title xml:lang="en"><![CDATA[Electrical generation forecast of photovoltaic systems. First steps by Cuban Universities]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[GómezRodríguez]]></surname>
<given-names><![CDATA[Marco Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gómez Sarduy]]></surname>
<given-names><![CDATA[Julio Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lorenzo Ginori]]></surname>
<given-names><![CDATA[Juan Valentín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fonte González]]></surname>
<given-names><![CDATA[Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Sánchez]]></surname>
<given-names><![CDATA[Zaid]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Cienfuegos &#8220;Carlos Rafael Rodríguez&#8221;  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Central &#8220;Marta Abreu&#8221; de Las Villas  ]]></institution>
<addr-line><![CDATA[ Santa Clara]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,OBE Provincial Cienfuegos  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>02</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>02</month>
<year>2021</year>
</pub-date>
<volume>13</volume>
<numero>1</numero>
<fpage>253</fpage>
<lpage>265</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2218-36202021000100253&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2218-36202021000100253&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2218-36202021000100253&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La generación solar fotovoltaica está asociada con una alta variabilidad debido a la intermitencia de la radiación solar y otros parámetros climáticos. Esto dificulta la planificación de la generación. Por tanto, el pronóstico preciso a corto plazo de este tipo de generadores es importante para los sistemas de potencia. Este trabajo se refiere al esfuerzo que desde la academia se desarrolla en Cuba para desarrollar este tipo de predictores en el marco del proyecto &#8220;Conectando conocimientos&#8221; y la colaboración entre la Universidad Central &#8220;Marta Abreu&#8221; de Las Villas y la Universidad de Cienfuegos &#8220;Carlos Rafael Rodríguez&#8221;. Se describe un modelo híbrido que combina transformada wavelet con redes neuronales artificiales para pronosticar la generación de potencia fotovoltaica para el día siguiente, a partir de datos históricos del Sistema de Supervisión y Adquisición de Datos (SCADA) y de variables meteorológicas locales. Se trabajó en entorno Matlab y se desarrollaron varias redes neuronales de regresión generalizada y del tipo feedforward backpropagation variando sus parámetros para seleccionar las de mejor desempeño. El modelo se desarrolló y validó para un parque de generación fotovoltaica de 5.5 MW. La precisión se compara con el modelo persistente, revelando mejoras en el rango del 6.66% al 49.71%.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Solar photovoltaic generation is marked by high variability due to the intermittency of solar radiation and other climatic parameters. This makes generation planning difficult. Therefore, accurate short-term forecasting of this type of generators is a crucial factor for power systems. This paper explains the efforts made by Cuban experts and academics in the field to develop predictors within the framework of the Connecting Knowledge project, jointly conducted with Marta Abreu Central University of Las Villas and Carlos Rafael Rodriguez University of Cienfuegos. It describes a hybrid model that combines wavelet transform with artificial neural networks to forecast photovoltaic power generation for the following day, by analyzing recorded data provided by the Data Monitoring and Collection System and local meteorological variables. Several generalized regression and feedforward backpropagation neural networks were developed in Matlab environment; their parameters were altered in order to determine the one that performed the best. The resulting model was developed and validated for a 5.5 MW photovoltaic generation park, which accuracy was compared with the generalized model, revealing improvements rates in the range of 6.66% to 49.71%.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Generación fotovoltaica]]></kwd>
<kwd lng="es"><![CDATA[modelo de predicción]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[transformada wavelet]]></kwd>
<kwd lng="es"><![CDATA[fuentes renovables de energía]]></kwd>
<kwd lng="en"><![CDATA[Photovoltaic generation]]></kwd>
<kwd lng="en"><![CDATA[forecasting models]]></kwd>
<kwd lng="en"><![CDATA[artificial neural networks]]></kwd>
<kwd lng="en"><![CDATA[wavelet transform]]></kwd>
<kwd lng="en"><![CDATA[renewable energy sources]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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