<?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>1815-5901</journal-id>
<journal-title><![CDATA[Ingeniería Energética]]></journal-title>
<abbrev-journal-title><![CDATA[Energética]]></abbrev-journal-title>
<issn>1815-5901</issn>
<publisher>
<publisher-name><![CDATA[Universidad Tecnológica de La Habana José Antonio Echeverría, Cujae]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1815-59012023000300073</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Sistema para predicción de la generación en bloques de plantas fotovoltaicas]]></article-title>
<article-title xml:lang="en"><![CDATA[System for prediction of generation in blocks of photovoltaic plants]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera Fernández]]></surname>
<given-names><![CDATA[Francisco B.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Limonte Ruiz]]></surname>
<given-names><![CDATA[Alberto A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alvarez Morales]]></surname>
<given-names><![CDATA[Michel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Tamayo]]></surname>
<given-names><![CDATA[Jesús G.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Central &#8220;Marta Abreu&#8221; de Las Villas  ]]></institution>
<addr-line><![CDATA[Villa Clara ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Empresa Tecnologías de la Información y la Automática  ]]></institution>
<addr-line><![CDATA[Villa Clara ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>44</volume>
<numero>3</numero>
<fpage>73</fpage>
<lpage>79</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1815-59012023000300073&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1815-59012023000300073&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1815-59012023000300073&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La predicción de la generación energética en plantas fotovoltaicas conectadas a un sistema eléctrico es objeto de constante estudio y desarrollo. En objetivo de este trabajo es desarrollar un método para la predicción de la generación a corto plazo. Esta predicción se realiza a partir de la aplicación de programas de predicción para plantas fotovoltaicas desarrollados basados en redes neuronales recurrentes y convolucionales. Como datos de entrada se consideran irradiación solar y temperatura ambiente. Se aplican métodos experimentales, utilizando los datos históricos del comportamiento de estas variables, y aplicando diferentes métodos de preprocesamiento de estos datos y postprocesamiento de las predicciones básicas, lo cual proporciona predicciones con mayor exactitud. Como principal resultado se obtiene un método de predicción, con una mejor exactitud en las horas centrales del día. Se presentan los resultados de las predicciones para un grupo de plantas demostrándose la factibilidad de aplicación del método.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The prediction of energy generation in photovoltaic plants connected to an electrical system is the subject of constant study and development. The objective of this work is to develop a method for short-term generation prediction. This prediction is made from the application of prediction programs for photovoltaic plants developed based on recurrent and convolutional neural networks. Solar irradiation and ambient temperature are considered as input data. Experimental methods are applied, using historical data on the behavior of these variables, and applying different methods of pre-processing these data and post-processing the basic predictions, which provides predictions with greater accuracy. The main result is a prediction method, with better accuracy in the central hours of the day. The results of the predictions for a group of plants are presented, demonstrating the feasibility of applying the method.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[modelado de plantas fotovoltaicas]]></kwd>
<kwd lng="es"><![CDATA[predicción de generación]]></kwd>
<kwd lng="en"><![CDATA[modeling of photovoltaic plants]]></kwd>
<kwd lng="en"><![CDATA[generation forecasting]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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