<?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>0186-1042</journal-id>
<journal-title><![CDATA[Contaduría y administración]]></journal-title>
<abbrev-journal-title><![CDATA[Contad. Adm]]></abbrev-journal-title>
<issn>0186-1042</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Contaduría y Administración]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0186-10422023000100077</article-id>
<article-id pub-id-type="doi">10.22201/fca.24488410e.2023.3356</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Hours ahead automed long short-term memory (LSTM) electricity load forecasting at substation level: Newcastle substation]]></article-title>
<article-title xml:lang="es"><![CDATA[Previsión de carga eléctrica automatizada a unas horas adelantadas con el modelo (LSTM) al nivel subestacional: subestación de Newcastle]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Peujio-Jiotsop-Foze]]></surname>
<given-names><![CDATA[Wellcome]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández-del-Valle]]></surname>
<given-names><![CDATA[Adrián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<volume>68</volume>
<numero>1</numero>
<fpage>77</fpage>
<lpage>96</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0186-10422023000100077&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0186-10422023000100077&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0186-10422023000100077&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Nowadays, electrical energy is of vital importance in our lives, every country needs this resource to develop its economy, factories, businesses, and homes are the basis of the economic structure of a country. In the city of Newcastle as in other cities are in constant development growing day by day in terms of industries, homes and businesses, these elements are the ones that consume all the electricity produced in Newcastle. Although Australia has strategically located substations that serve the function of supplying all existing loads with quality power, from time to time the load will exceed the capacity of these substations and will not be able to supply the loads that will arise in the future as the city grows. To find a solution to this problem, we use a deep learning model to improve accuracy. In this paper, a Long Short-Term Memory recurrent neural network (LSTM) is tested on a publicly available 30-minute dataset containing measured real power data for individual zone substations in the Ausgrid supply area data. The performance of the model is comprehensively compared with 4 different configurations of the LSTM. The proposed LSTM approach with 2 hidden layers and 50 neurons outperforms the other configurations with a mean absolute error (MAE) of 0.0050 in the short-term load forecasting task for substations.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Hoy en día la energía eléctrica es de vital importancia en nuestras vidas, todo país necesita de este recurso para desarrollar su economía, las fábricas, los negocios y los hogares son la base de la estructura económica de un país. En la ciudad de Newcastle al igual que en otras ciudades están en constante desarrollo creciendo día a día en cuanto a industrias, hogares y negocios, estos elementos son los que consumen toda la electricidad producida en Newcastle. A pesar de que Australia cuenta con subestaciones estratégicamente ubicadas que cumplen la función de abastecer todas las cargas existentes con energía de calidad, de vez en cuando la carga superará la capacidad de estas subestaciones y no podrá abastecer las cargas que surgirán en el futuro a medida que la ciudad crezca. Para encontrar una solución a este problema, utilizamos un modelo de aprendizaje profundo para mejorar la precisión. En este trabajo, se prueba una red neuronal recurrente de memoria a corto plazo (LSTM) en un conjunto de datos de 30 minutos disponible públicamente que contiene datos de potencia real medidos para subestaciones de zonas individuales, en los datos del área de suministro de Ausgrid. El rendimiento del modelo se compara exhaustivamente con 4 configuraciones diferentes del modelo LSTM. El enfoque LSTM propuesto con 2 capas ocultas y 50 neuronas supera a las otras configuraciones con un error medio absoluto (MAE) de 0,0050 en la tarea de previsión de carga a corto plazo para subestaciones.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[forecasting]]></kwd>
<kwd lng="en"><![CDATA[electric load]]></kwd>
<kwd lng="en"><![CDATA[LSTM]]></kwd>
<kwd lng="en"><![CDATA[substation]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
<kwd lng="es"><![CDATA[previsión]]></kwd>
<kwd lng="es"><![CDATA[carga eléctrica]]></kwd>
<kwd lng="es"><![CDATA[LSTM]]></kwd>
<kwd lng="es"><![CDATA[subestación]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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