<?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>0120-6230</journal-id>
<journal-title><![CDATA[Revista Facultad de Ingeniería Universidad de Antioquia]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.fac.ing.univ. Antioquia]]></abbrev-journal-title>
<issn>0120-6230</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ingeniería, Universidad de Antioquia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-62302022000100009</article-id>
<article-id pub-id-type="doi">10.17533/udea.redin.20200584</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Electricity demand forecasting in industrial and residential facilities using ensemble machine learning]]></article-title>
<article-title xml:lang="es"><![CDATA[Predicción de demanda eléctrica en instalaciones industriales y residenciales utilizando aprendizaje automático combinado]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Porteiro]]></surname>
<given-names><![CDATA[Rodrigo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández-Callejo]]></surname>
<given-names><![CDATA[Luis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nesmachnow]]></surname>
<given-names><![CDATA[Sergio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de la República  ]]></institution>
<addr-line><![CDATA[Montevideo ]]></addr-line>
<country>Uruguay</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Valladolid Departamento de Ingeniería Agrícola y Forestal ]]></institution>
<addr-line><![CDATA[Soria ]]></addr-line>
<country>Spain</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>
<numero>102</numero>
<fpage>9</fpage>
<lpage>25</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-62302022000100009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-62302022000100009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-62302022000100009&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT This article presents electricity demand forecasting models for industrial and residential facilities, developed using ensemble machine learning strategies. Short term electricity demand forecasting is beneficial for both consumers and suppliers, as it allows improving energy efficiency policies and the rational use of resources. Computational intelligence models are developed for day-ahead electricity demand forecasting. An ensemble strategy is applied to build the day-ahead forecasting model based on several one-hour models. Three steps of data preprocessing are carried out, including treating missing values, removing outliers, and standardization. Feature extraction is performed to reduce overfitting, reducing the training time and improving the accuracy. The best model is optimized using grid search strategies on hyperparameter space. Then, an ensemble of 24 instances is generated to build the complete day-ahead forecasting model. Considering the computational complexity of the applied techniques, they are developed and evaluated on the National Supercomputing Center (Cluster-UY), Uruguay. Three different real data sets are used for evaluation: an industrial park in Burgos (Spain), the total electricity demand for Uruguay, and demand from a distribution substation in Montevideo (Uruguay). Standard performance metrics are applied to evaluate the proposed models. The main results indicate that the best day ahead model based on ExtraTreesRegressor has a mean absolute percentage error of 2.55% on industrial data, 5.17% on total consumption data and 9.09% on substation data.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Este artículo presenta modelos de pronóstico de demanda eléctrica industrial y residencial, aplicando aprendizaje automático combinado. El pronóstico de demanda eléctrica a corto plazo beneficia a consumidores y proveedores, ya que permite mejorar las políticas de eficiencia energética y el uso racional de los recursos. Se desarrollan modelos de inteligencia computacional para el pronóstico diario de demanda eléctrica y una estrategia híbrida para construir el modelo de pronóstico diario basado en modelos para la próxima hora. Se aplican tres métodos de preprocesamiento de datos: tratamiento de valores perdidos, eliminación de valores atípicos y estandarización. Se aplica extracción de características para reducir el sobreajuste y el tiempo de entrenamiento, mejorando la precisión. El mejor modelo se optimiza mediante búsqueda de grilla en el espacio de hiperparámetros. Luego se genera un conjunto de 24 instancias para construir el modelo de pronóstico completo para el día siguiente. Las técnicas aplicadas se desarrollan y evalúan en el Centro Nacional de Supercomputación (Cluster-UY), Uruguay. Se utilizan tres conjuntos de datos reales para la evaluación: un parque industrial en Burgos (España), la demanda eléctrica total de Uruguay y la demanda de una subestación de distribución en Montevideo (Uruguay). Se aplican métricas estándar para evaluar los modelos propuestos. Los resultados indican que el mejor modelo, basado en ExtraTreesRegressor, tiene un error porcentual medio de 2, 55% en datos industriales, 5, 17% en consumo total y 9, 09% en subestación.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Energy]]></kwd>
<kwd lng="en"><![CDATA[forecasting]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="es"><![CDATA[Energía]]></kwd>
<kwd lng="es"><![CDATA[pronóstico]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
</kwd-group>
</article-meta>
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