<?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>2227-1899</journal-id>
<journal-title><![CDATA[Revista Cubana de Ciencias Informáticas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev cuba cienc informat]]></abbrev-journal-title>
<issn>2227-1899</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ediciones Futuro]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2227-18992022000100144</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Propuesta de modelo predictivo inteligente para una planta fotovoltaica]]></article-title>
<article-title xml:lang="en"><![CDATA[Intelligent predictive model proposal for a photovoltaic plant]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera Casanova]]></surname>
<given-names><![CDATA[Reinier]]></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 contrib-type="author">
<name>
<surname><![CDATA[Bello García]]></surname>
<given-names><![CDATA[Beatriz]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[León Viltres.]]></surname>
<given-names><![CDATA[Lesyani]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<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-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Central &#8220;Marta Abreu&#8221; de Las Villas  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Carretera a Camajuaní Km. 5½. Santa Clara. Villa Clara. Cuba.</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Empresa de Tecnologías de la Información y la Automática UEB ATI VC  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Central &#8220;Marta Abreu&#8221; de Las Villas  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Central &#8220;Marta Abreu&#8221; de Las Villas.  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad Central &#8220;Marta Abreu&#8221; de Las Villas.  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2022</year>
</pub-date>
<volume>16</volume>
<numero>1</numero>
<fpage>144</fpage>
<lpage>162</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992022000100144&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992022000100144&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992022000100144&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El objetivo de este trabajo es presentar el desarrollo de una estructura de modelo dinámico para la predicción de la generación eléctrica en una planta fotovoltaica. Tradicionalmente un modelo de predicción de la generación eléctrica en este tipo de planta se basa en dos modelos, uno para la predicción de la irradiación solar y un segundo modelo para describir la relación de la irradiación solar con la potencia generada. Como variables climatológicas principales se consideran la irradiación solar y la temperatura ambiental, mientras que desde el punto de vista tecnológico se considera la limpieza de la superficie de los paneles solares, así como el punto de operación de la planta, dependiendo del período del año y la hora del día. El modelo presentado considera la irradiación solar y la temperatura ambiente como variables de entrada, al tiempo que se desarrolla la modelación no lineal existente entre la irradiación y la potencia generada, considerando como disturbios la sombra (parcial o no) sobre los módulos y la limpieza de la superficie de los paneles fotovoltaicos. El trabajo presenta una descripción tecnológica de una planta y la caracterización temporal y frecuencial de juegos de datos reales, a partir de lo cual se desarrolla la concepción de la estructura del modelo más adecuado para la aplicación de técnicas basadas en inteligencia artificial, específicamente aprendizaje profundo. Finalmente, el modelo propuesto se utiliza para realizar la predicción directa de la potencia generada basado solamente en datos históricos obtenidos en la planta fotovoltaica de la Universidad Central &#8220;Marta Abreu&#8221; de Las Villas. Los resultados obtenidos para un horizonte de predicción de mediano plazo y para diferentes épocas del año resultan acertados, lo que demuestra la efectividad del método de predicción propuesto.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The objective of this work is to present the development of a dynamic model structure for the prediction of electricity generation in a photovoltaic plant. Traditionally, a model for the prediction of electricity generation in this type of plant is based on two models, one for the prediction of solar irradiance and a second model to describe the relationship between solar irradiance and generated power. The main climatological variables considered are solar irradiation and ambient temperature, while from the technological point of view, the surface cleanliness of the solar panels is considered, as well as the operating point of the plant, depending on the period of the year and the time of day. The model presented considers solar irradiation and ambient temperature as input variables, while developing the non-linear modeling between irradiation and generated power, considering as disturbances the shading (partial or not) on the modules and the cleaning of the surface of the photovoltaic panels. The work presents a technological description of a plant and the temporal and frequency characterization of real data sets, from which the conception of the structure of the most suitable model for the application of techniques based on artificial intelligence, specifically deep learning, is developed. Finally, the proposed model is used to perform the direct prediction of the generated power based only on historical data obtained in the photovoltaic plant of the Central University "Marta Abreu" of Las Villas. The results obtained for a medium-term prediction horizon and for different times of the year are accurate, which demonstrates the effectiveness of the proposed prediction method.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Planta fotovoltaica]]></kwd>
<kwd lng="es"><![CDATA[Modelo planta fotovoltaica]]></kwd>
<kwd lng="es"><![CDATA[Predicción potencia generada]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje profundo]]></kwd>
<kwd lng="en"><![CDATA[Photovoltaic plant]]></kwd>
<kwd lng="en"><![CDATA[Photovoltaic plant model]]></kwd>
<kwd lng="en"><![CDATA[Generated power prediction]]></kwd>
<kwd lng="en"><![CDATA[Deep learning.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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