<?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>S2218-36202019000400220</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Pronóstico del consumo pico para la gestión energética de la Universidad de Cienfuegos]]></article-title>
<article-title xml:lang="en"><![CDATA[Peak load forecasting for energy management at Cienfuegos University]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Peña Acción]]></surname>
<given-names><![CDATA[Jesús Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Viego Felipe]]></surname>
<given-names><![CDATA[Percy Rafael]]></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[Padrón Padrón]]></surname>
<given-names><![CDATA[Arturo Enrique]]></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>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>11</volume>
<numero>4</numero>
<fpage>220</fpage>
<lpage>228</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2218-36202019000400220&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2218-36202019000400220&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2218-36202019000400220&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La predicción del consumo en el horario de máxima demanda contribuye a mejorar la gestión energética. Se presentan dos modelos de pronóstico del consumo pico y del total diario para la Sede &#8220;Carlos Rafael Rodríguez&#8221; de la Universidad de Cienfuegos. Los modelos desarrollados son de regresión lineal múltiple y uno no lineal, basado en una red neuronal artificial (RNA). Se procesaron los datos provenientes de los metros contadores y se clasificaron los días en aquellos de poca actividad y los de actividad normal. Los resultados muestran buena correspondencia entre las salidas del modelo y las mediciones reales, lo que demuestra la calidad, evaluada a partir de medidas de precisión como el coeficiente de correlación R2 y el porcentaje de error absoluto medio (MAPE). Se demuestra que el modelo no lineal es superior y que se puede emplear en la gestión energética de la Universidad para estimar la estructura de consumo del pico con anticipación.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The peak load forecasting contributes to improve energy management. In this paper, two forecast models of peak consumption and the total daily are presented for Campus &#8220;Carlos Rafael Rodríguez&#8221; of the University of Cienfuegos. The developed models are of multiple linear regression and one of non-linear type. Non-linear model is based on an artificial neural network (ANN). The data from the readings of meters were processed and the days were classified in days of low activity and days of normal activity. The results obtained show good correlation between the model outputs and measurements. It shows model quality evaluated from the correlation coefficient R2 and the medium absolute percentage error (MAPE). It is shown that the non-linear model is better than linear one and it can be used for energy management system at University of Cienfuegos to estimate with anticipation the peak load.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Pronóstico de consumo]]></kwd>
<kwd lng="es"><![CDATA[modelos de regresión lineal múltiple]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[modelos de pronóstico]]></kwd>
<kwd lng="es"><![CDATA[consumo pico]]></kwd>
<kwd lng="es"><![CDATA[consumo de energía]]></kwd>
<kwd lng="en"><![CDATA[Energy forecasting]]></kwd>
<kwd lng="en"><![CDATA[multiple lineal regression models]]></kwd>
<kwd lng="en"><![CDATA[artificial neural network]]></kwd>
<kwd lng="en"><![CDATA[forecasting models]]></kwd>
<kwd lng="en"><![CDATA[peak load]]></kwd>
<kwd lng="en"><![CDATA[energy consumption]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Badri]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Ameli]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Birjandi]]></surname>
<given-names><![CDATA[A. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Application of artificial neural networks and fuzzy logic methods for short-term load forecasting]]></article-title>
<source><![CDATA[Energy Procedia]]></source>
<year>2012</year>
<volume>14</volume>
<page-range>1883-8</page-range></nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bagnasco]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Saviozzi]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Silvestro]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Vinci]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Grillo]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Zennaro]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
</person-group>
<source><![CDATA[Artificial neural network application to load forecasting in a large hospital facility]]></source>
<year>2014</year>
<conf-name><![CDATA[ IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)]]></conf-name>
<conf-loc>Durham </conf-loc>
</nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Berardino]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Nwankpa]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<source><![CDATA[Inclusion of temporal effects in fgorecasting building electrical loads for demand resource planning]]></source>
<year>2013</year>
<conf-name><![CDATA[ IEEE Grenoble PowerTech (POWERTECH)]]></conf-name>
<conf-loc>Grenoble </conf-loc>
</nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fernández]]></surname>
<given-names><![CDATA[R. D.]]></given-names>
</name>
</person-group>
<source><![CDATA[Sistema de gestión y pronóstico de energía eléctrica en la UCF]]></source>
<year>2007</year>
<publisher-loc><![CDATA[Cienfuegos ]]></publisher-loc>
<publisher-name><![CDATA[Universidad de Cienfuegos]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ghofrani]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Ghayekhloo]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Arabali]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Ghayekhloo]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A hybrid short-term load forecasting with a new input selection framework]]></article-title>
<source><![CDATA[Energy]]></source>
<year>2015</year>
<volume>81</volume>
<page-range>1-10</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gómez Sarduy]]></surname>
<given-names><![CDATA[J. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Gregio di Santo]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Saidel]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Linear and non linear methods for prediction of peak load at University of Sao Paulo]]></article-title>
<source><![CDATA[Measurement]]></source>
<year>2016</year>
<volume>78</volume>
<page-range>187-201</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[González]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[Propuesta de Indicadores de Gestión para la Universidad de Cienfuegos]]></source>
<year>2015</year>
<publisher-loc><![CDATA[Cienfuegos ]]></publisher-loc>
<publisher-name><![CDATA[Universidad de Cienfuegos]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hao]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Dipti]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Abbas]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Uncertainty handling using neural network-based prediction intervals for electrical load forecasting]]></article-title>
<source><![CDATA[Energy]]></source>
<year>2014</year>
<page-range>916-25</page-range></nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hernández]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A survey on electric power demand forecasting: future trends in smart grids, microgrids and smart buildings]]></article-title>
<source><![CDATA[IEEE Commun. Surveys Tutorials]]></source>
<year>2014</year>
<volume>16</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>1460-95</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Honga]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[White]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Weather station selection for electric load forecasting]]></article-title>
<source><![CDATA[Int. J. Forecast.]]></source>
<year>2015</year>
<volume>31</volume>
<page-range>286-95</page-range></nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hooshmand]]></surname>
<given-names><![CDATA[R.-A.]]></given-names>
</name>
<name>
<surname><![CDATA[Amooshahi]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Parastegari]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A hybrid intelligent algorithm based short-term load forecasting approach]]></article-title>
<source><![CDATA[Electric Power Energy Systems]]></source>
<year>2013</year>
<volume>45</volume>
<page-range>313-24</page-range></nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kulkarni]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Simon]]></surname>
<given-names><![CDATA[S. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Sundareswaran]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A spiking neural network (SNN) forecast engine for short-term electrical load forecasting]]></article-title>
<source><![CDATA[Application Soft Computing]]></source>
<year>2013</year>
<volume>13</volume>
<page-range>3628-35</page-range></nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mirowski]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Demand forecasting in smart grids]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Ho]]></surname>
<given-names><![CDATA[T. K.]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[C. N.]]></given-names>
</name>
</person-group>
<source><![CDATA[Bell Labs Technology Journal]]></source>
<year>2014</year>
<volume>18</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>135-58</page-range></nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Motamedi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Zareipour]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Rosehart]]></surname>
<given-names><![CDATA[W. D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Electricity price and demand forecasting in samrt grids]]></article-title>
<source><![CDATA[IEEE Transaction on Smart Grid]]></source>
<year>2012</year>
<volume>3</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>664-74</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ortiz Beviá]]></surname>
<given-names><![CDATA[M. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Ruiz de Elvira]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Alvarez García]]></surname>
<given-names><![CDATA[F. J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The influence of meteorological variability on the mid-term evolution of the electric load]]></article-title>
<source><![CDATA[Energy]]></source>
<year>2014</year>
<volume>76</volume>
<page-range>850-6</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ortíz]]></surname>
<given-names><![CDATA[H. J.]]></given-names>
</name>
</person-group>
<source><![CDATA[Realización de un Diagnóstico Energético de Nivel 1 en la &#8220;Universidad de Ciencias Pedagógicas&#8221; de Cienfuegos]]></source>
<year>2016</year>
<publisher-loc><![CDATA[Cienfuegos ]]></publisher-loc>
<publisher-name><![CDATA[Universidad de Cienfuegos]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Saravanan]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Kannan]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Thangaraj]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[India's electricity demand forecast isung regression analysis and artifical neural networks based on principal components]]></article-title>
<source><![CDATA[ICTACT Journal of Soft Computing]]></source>
<year>2012</year>
<volume>2</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>365-70</page-range></nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Xiao]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Hou]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A combined model based on data pre-analysis and weight coeficients optimization for electrical load forecasting]]></article-title>
<source><![CDATA[Energy]]></source>
<year>2015</year>
<volume>82</volume>
<page-range>1-26</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
