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Modelo predictivo del consumo de gas natural, en el mercado industrial del eje cafetero.
dc.contributor.advisor | Mejía Salazar, María Helena | |
dc.contributor.author | Zuleta García, Luis Alejandro | |
dc.date.accessioned | 2021-08-31T13:56:43Z | |
dc.date.available | 2021-08-31T13:56:43Z | |
dc.date.issued | 2021-08-27 | |
dc.identifier.uri | https://repositorio.ucaldas.edu.co/handle/ucaldas/17093 | |
dc.description | Ilustraciones, gráficas | spa |
dc.description.abstract | spa: La energía desempeña un papel crítico en el mundo entero teniendo en cuenta que la sociedad requiere de ella para llevar a cabo cualquier tipo de actividad en la modernidad. En los últimos años el incremento de consumo energético ha sido constante en los diferentes sectores consumidores imponiendo diversos retos ambientales, logísticos y regulatorios. El sector industrial como uno de los sectores con mayor participación en el consumo global, emplea el gas natural como fuente de energía para soportar procesos de manufactura y transformación de materia prima. En este escenario, la predicción del consumo de gas natural en el sector industrial cobra gran importancia para los actores involucrados en las operaciones logísticas, que buscan garantizar el suministro y transporte confiable y asequible a los diferentes sectores consumidores diariamente, a su vez reduciendo el impacto ambiental. El proceso logístico del gas natural está sujeto a un conjunto de regulaciones y condiciones impuestas dentro del marco de la cadena de suministro en la región del eje cafetero colombiano, las cuales exigen estimaciones de consumo en el corto plazo. Múltiples estudios han abordado la predicción de gas natural, sin embargo estos han sido orientados a predecir el consumo global, analizando diferentes tipos de consumidores de manera conjunta y en su mayoría enfocándose en un horizonte de tiempo a mediano y largo plazo. En este trabajo se propone el uso de técnicas de machine learning para la predicción del consumo de gas natural del sector industrial en la región. Las técnicas implementadas incluyen Random Forest, Máquinas de Vectores de Soporte y Redes Neuronales Recurrentes, específicamente la arquitectura de gran memoria a corto plazo o LSTM usada ampliamante en el campo de deep learning. Para cada industria se construyeron un conjunto de modelos a partir de los datos históricos de consumo industrial en un periodo de cinco años, y de acuerdo a su desempeño se seleccionó el mejor modelo para cada industria. La evaluación del desempeño se realizó utilizando la métrica de precisión denominada raíz del error cuadrático medio (RMSE, del inglés Root-mean-squared error). De acuerdo a lo anterior, los resultados obtenidos para cada modelo son presentados, con lo cual es posible evidenciar que las Redes Neuronales Recurrentes mostraron un desempeño superior a las demás técnicas propuestas. Adicionalmente, se presentan las predicciones diarias para un periodo de 350 días, demostrando que dicha técnica puede ser adoptada para la predicción confiable del consumo de gas natural del sector industrial en un periodo de tiempo a corto plazo. Finalmente, como parte de esta investigación se presentan las conclusiones y las recomendaciones para emprender acciones en trabajos futuros, que permitan explorar aspectos adicionales para modelar el consumo de gas natural. | spa |
dc.description.abstract | eng: Energy plays a critical role worldwide. Society requires any form of energy to carry on every modern life activity. In recent years, continuous energy consumption in different markets has imposed environmental, logistics, and policy challenges. The industry is one of the sectors with the largest share of global energy consumption, supporting its manufacturing and raw material transformation process through natural gas usage. Consequently, industrial natural gas consumption forecasting is extremely important for stakeholders involved in logistics operations, which seek to guarantee reliable and affordable supply and transport to the different consumers daily while decreasing environmental impacts. The logistics process is regulated by a set of policies and conditions contained in the local supply chain framework. These regulations require natural gas consumption predictions in a short term. Several studies have approached the natural gas forecasting subject, however, they explore the global consumption forecasting, including different consumer sectors jointly in a mid- and long-term time horizon. This work proposes multiple machine learning techniques for forecasting natural gas consumption of the industrial sector in the Colombian coffee region. The proposed techniques include Random Forest, Support Vector Machine, and Recurrent Neural Network, specifically the long short term memory (LSTM) architecture which is used widely in the the deep learning field. A set of models for each industry were built from the consumption historical data in five years. According to its performance, the best model for each industry was selected. The RMSE (Root-mean-squared error) metric was used to evaluate the accuracy of the predictions and thereof the models themselves. Based on above results, the RMSE is presented for each industry, concluding that Recurrent Neural Networks showed superior performance than the other proposed techniques. Additionally, the predictions for 350 days are shown, demonstrating this technique can be used to reliable forecast natural gas consumption of the industrial sector in a short-term time horizon. Finally, the conclusions and recommendations for taking action in future works are presented, which can lead to additional aspects for forecasting natural gas consumption. | eng |
dc.description.tableofcontents | RESUMEN II ABSTRACT III CONTENIDO VI Lista de figuras IX Lista de tablas XI 1. INTRODUCCIÓN 1.1. Planteamiento del problema/1.1.1. Contexto ambiental / 1.1.2. Cadena de suministro del gas natural / 1.2. Justificación / 1.3. Objetivos / 1.3.1. Objetivo general / 1.3.2. Objetivos / 2. MARCO DE REFERENCIA/ 2.1. Análisis predictivo/2.2. Análisis predictivo de la demanda/2.3. Predicción del consumo de gas natural/ 2.4. Trabajo propuesto/ 3. METODOLOGÍA/ 3.1. Recolección y entendimiento / 3.1.1. Industria 1 /3.1.2. Industria 2 / 3.1.3. Industria 2/ 3.1.4. Industria 3 / 3.1.5. Industria 5 /3.1.6. Industria 6 / 3.1.7. Industria 7 / 3.1.8. Industria 8 /3.2. Tratamiento de datos/3.3. Descripción detallada del proceso/3.3.1. Implementación Random / 3.3.2. Implementación regresión basada en máquina de vectores de soporte /3.3.3. Implementación redes neuronales artificiales - LSTM / CONTENIDO 4. RESULTADOS 56 4.1. Resultados Random Forest / 4.2. Resultados máquina de vectores de soporte / 4.3. Resultados redes neuronales artificiales recurrentes - LSTM / 4.4. Resultados generales . . . . . . . / 4.4.1. Predicción del consumo industrial para el año 2019/5. CONCLUSIONES Y RECOMENDACIONES / 5.1. Conclusiones/5.2. Recomendaciones / BIBLIOGRAFIA . | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.language.iso | spa | spa |
dc.title | Modelo predictivo del consumo de gas natural, en el mercado industrial del eje cafetero. | spa |
dc.type | Trabajo de grado - Maestría | spa |
dc.description.degreelevel | Maestría | spa |
dc.identifier.instname | Universidad de Caldas | spa |
dc.identifier.reponame | Repositorio institucional Universidad de Caldas | spa |
dc.identifier.repourl | https://repositorio.ucaldas.edu.co/ | spa |
dc.publisher.faculty | Facultad de Ingeniería | spa |
dc.publisher.place | Manizales | spa |
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dc.rights.accessrights | info:eu-repo/semantics/closedAccess | spa |
dc.subject.lemb | Energía | |
dc.subject.lemb | Gases naturales | |
dc.subject.proposal | Eficiencia energía | spa |
dc.subject.proposal | Consumo gas natural industrial | spa |
dc.subject.proposal | Predicción consumo | spa |
dc.subject.proposal | Redes neuronales recurrentes | spa |
dc.subject.proposal | LSTM | spa |
dc.subject.proposal | Deep learning | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
dc.type.redcol | https://purl.org/redcol/resource_type/TM | spa |
dc.type.version | info:eu-repo/semantics/publishedVersion | spa |
oaire.version | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
oaire.accessrights | http://purl.org/coar/access_right/c_14cb | spa |
dc.description.degreename | Magister en Ingeniería Computacional | spa |
dc.publisher.program | Maestría en Ingeniería Computacional | spa |