Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia

Para planificar el aumento de la demanda de energía, las empresas de servicios públicos y los gobiernos se basan en modelos de pronóstico. Usando datos históricos y predictivos, los stakeholders determinan la demanda requerida por los accionistas de transmisión y distribución. Una vez determinada la...

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Autores:: Pacheco Sandoval, Leonardo Esteban
González Calderón, William
Suárez Arias, Rafael Enrique

Tipo de recurso:: Investigation report

Fecha de publicación:: 2023

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

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dc.title.spa.fl_str_mv	Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia
dc.title.translated.spa.fl_str_mv	Desarrollo de un modelo basado en energía para proyectar la demanda de energía de Colombia
title	Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia
spellingShingle	Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia Energy consumption Energetic resources Energy demand Energy supply Energetic industry Economic sector Energy based model Consumo de energía Recursos energéticos Demanda de energía Abstecimiento de energía Industria energética Colombia Sector económico Modelo basado en energía
title_short	Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia
title_full	Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia
title_fullStr	Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia
title_full_unstemmed	Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia
title_sort	Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia
dc.creator.fl_str_mv	Pacheco Sandoval, Leonardo Esteban González Calderón, William Suárez Arias, Rafael Enrique
dc.contributor.author.none.fl_str_mv	Pacheco Sandoval, Leonardo Esteban González Calderón, William Suárez Arias, Rafael Enrique
dc.contributor.cvlac.spa.fl_str_mv	Pacheco Sandoval,Leonardo Esteban [ Suárez Arias, Rafael Enrique [0001429372] González Calderón, William [0001367421]
dc.contributor.googlescholar.spa.fl_str_mv	Pacheco Sandoval, Leonardo Esteban [es&oi=ao]
dc.contributor.orcid.spa.fl_str_mv	Pacheco Sandoval,Leonardo Esteban [0000-0001-7262-382X] Suárez Arias, Rafael Enrique [0000-0001-9767-210X]
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Investigación Recursos, Energía, Sostenibilidad - GIRES Grupo de Investigaciones Clínicas
dc.contributor.apolounab.spa.fl_str_mv	Pacheco Sandoval, Leonardo Esteban [leonardo-esteban-pacheco-sandoval] Suárez Arias, Rafael Enrique [rafael-enrique-suarez-arias] González Calderón, William [william-gonzález-calderón]
dc.subject.keywords.spa.fl_str_mv	Energy consumption Energetic resources Energy demand Energy supply Energetic industry Economic sector Energy based model
topic	Energy consumption Energetic resources Energy demand Energy supply Energetic industry Economic sector Energy based model Consumo de energía Recursos energéticos Demanda de energía Abstecimiento de energía Industria energética Colombia Sector económico Modelo basado en energía
dc.subject.lemb.spa.fl_str_mv	Consumo de energía Recursos energéticos Demanda de energía Abstecimiento de energía Industria energética Colombia
dc.subject.proposal.spa.fl_str_mv	Sector económico Modelo basado en energía
description	Para planificar el aumento de la demanda de energía, las empresas de servicios públicos y los gobiernos se basan en modelos de pronóstico. Usando datos históricos y predictivos, los stakeholders determinan la demanda requerida por los accionistas de transmisión y distribución. Una vez determinada la demanda, los interesados establecen el recurso de generación de electricidad más adecuado para satisfacer la demanda de energía. Las curvas de demanda representan la relación entre el precio de un bien (precio unitario) y cuánto están dispuestos a pagar los consumidores por el bien o servicio. En consecuencia, la demanda se describe como elástica cuando la demanda disminuye rápidamente a medida que aumenta el precio, o inelástica cuando la demanda disminuye ligeramente a medida que aumenta el precio. Además, las curvas de demanda muestran vívidamente la influencia de la economía que contribuye a las elecciones de los consumidores. Para ejemplificar esto, se han utilizado curvas de demanda para cuantificar la demanda de nicotina, alcohol, gasolina, combustible E85 y bronceadores artificiales, entre muchos otros bienes [1]. Por lo tanto, han demostrado una buena validez predictiva [2] y han sido útiles en la elaboración de políticas públicas [3]. En consecuencia, las curvas de demanda son la base para cualquier estudio prospectivo. Desde el punto de vista del modelo energético, la demanda de energía es la base para planificar el suministro de generación de energía [4]. En Colombia la demanda de energía se encuentra dividida por sectores de consumo en los que la mayoría de los casos corresponden al sector económico del país.
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-07-28T18:28:02Z
dc.date.available.none.fl_str_mv	2023-07-28T18:28:02Z
dc.date.issued.none.fl_str_mv	2023-03
dc.type.eng.fl_str_mv	Research report
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dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/workingPaper
dc.type.local.spa.fl_str_mv	Informe de investigación
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spelling	Pacheco Sandoval, Leonardo Esteban74816790-4dae-46e7-9c12-c14a2704a7d2González Calderón, Williamf902a2c3-b585-4a03-b9b3-029bcf795699Suárez Arias, Rafael Enrique29933aae-f3d5-48dc-af9c-e6f8bd9c5fe1Pacheco Sandoval,Leonardo Esteban [Suárez Arias, Rafael Enrique [0001429372]González Calderón, William [0001367421]Pacheco Sandoval, Leonardo Esteban [es&oi=ao]Pacheco Sandoval,Leonardo Esteban [0000-0001-7262-382X]Suárez Arias, Rafael Enrique [0000-0001-9767-210X]Grupo de Investigación Recursos, Energía, Sostenibilidad - GIRESGrupo de Investigaciones ClínicasPacheco Sandoval, Leonardo Esteban [leonardo-esteban-pacheco-sandoval]Suárez Arias, Rafael Enrique [rafael-enrique-suarez-arias]González Calderón, William [william-gonzález-calderón]Bucaramanga (Santander, Colombia)UNAB Campus Bucaramanga2023-07-28T18:28:02Z2023-07-28T18:28:02Z2023-03http://hdl.handle.net/20.500.12749/20824instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coPara planificar el aumento de la demanda de energía, las empresas de servicios públicos y los gobiernos se basan en modelos de pronóstico. Usando datos históricos y predictivos, los stakeholders determinan la demanda requerida por los accionistas de transmisión y distribución. Una vez determinada la demanda, los interesados establecen el recurso de generación de electricidad más adecuado para satisfacer la demanda de energía. Las curvas de demanda representan la relación entre el precio de un bien (precio unitario) y cuánto están dispuestos a pagar los consumidores por el bien o servicio. En consecuencia, la demanda se describe como elástica cuando la demanda disminuye rápidamente a medida que aumenta el precio, o inelástica cuando la demanda disminuye ligeramente a medida que aumenta el precio. Además, las curvas de demanda muestran vívidamente la influencia de la economía que contribuye a las elecciones de los consumidores. Para ejemplificar esto, se han utilizado curvas de demanda para cuantificar la demanda de nicotina, alcohol, gasolina, combustible E85 y bronceadores artificiales, entre muchos otros bienes [1]. Por lo tanto, han demostrado una buena validez predictiva [2] y han sido útiles en la elaboración de políticas públicas [3]. En consecuencia, las curvas de demanda son la base para cualquier estudio prospectivo. Desde el punto de vista del modelo energético, la demanda de energía es la base para planificar el suministro de generación de energía [4]. En Colombia la demanda de energía se encuentra dividida por sectores de consumo en los que la mayoría de los casos corresponden al sector económico del país.CHAPTER 1 Introduction .......................................................................................................... 5 1.1 Introduction & Project Background ......................................................................................... 5 1.2 Problem Definition ...................................................................................................................... 6 CHAPTER 2 Literature Review ................................................................................................. 8 EPM & Forecasting conceptual approach ............................................................................................ 8 Trajectory of the Colombian Energy Sector ...................................................................................... 14 Current Energy Panorama of Colombia ............................................................................................ 19 CHAPTER 3 Methodology........................................................................................................ 23 3.1. Objective of Study .......................................................................................................................... 26 3.2 Fundamental Analysis .................................................................................................................... 27 3.3 Data Gathering ................................................................................................................................ 29 3.4 Data Selection Criteria ................................................................................................................... 30 3.5 Dataset Definition ............................................................................................................................ 32 3.6 Statistical Significance .................................................................................................................... 33 3.6.1 Variation of Dataset .......................................................................................................... 37 3.6.2 Combination Analysis ....................................................................................................... 39 3.6.3 Significance Test ................................................................................................................ 44 3.7 Significance Analysis ...................................................................................................................... 49 3.8 Confirmation of Variables.............................................................................................................. 60 3.9 Influence of Dependence Analysis ................................................................................................. 65 CHAPTER 4 Applicability and Result Assessment ................................................................ 71 4.1 Applicability .............................................................................................................................. 72 4.1.1 Trading Energy Factor ..................................................................................................... 72 4.1.1 Environmental Factor ....................................................................................................... 74 4.1.2 Demographic Factor ......................................................................................................... 77 4.1.3 Economic Factor ............................................................................................................... 79 4.2 Result Assessment ..................................................................................................................... 82 CHAPTER 5 Conclusions .......................................................................................................... 91 References ...................................................................................................................................... 1To plan for increasing energy demand utilities and governments rely on forecasting models. Using historical and predictive data, stakeholders determine the demand required by transmission and distribution shareholders. Once the demand is determined, stakeholders establish the electricity generation resource that will be best suited to meet the energy demand. The demand curves represent the relationship between the price of a good (unit price) and how much consumers are willing to pay for the good or service. Correspondingly demand is described as elastic when demand quickly decreases as price increases, or inelastic when the demand slightly decreases as price increases. Furthermore, the demand curves vividly display the influence of economics that contribute to consumer choices. To exemplify this, demand curves have been used to quantify demand for nicotine, alcohol, gasoline, E85 fuel, and artificial sun tanning amongst many other goods [1]. Hence they have demonstrated good predictive validity [2] and have been useful in crafting public policies [3]. Correspondingly demand curves are the basis for any prospective study. From an energy model point of view, energy demand is the foundation for planning the energy generation supply [4]. In Colombia, the energy demand is divided by sectors of consumption in which most cases correspond to the economic sector of the country. The Energy-Mining Planning Unit of Colombia (UPME) has identified the representative energy consumption sectors over the total energy demand of Colombia. In 2017 the energy demand of Colombia was driven by 17.48% Residential, 5.21% Commercial, 33.19% Industrial, 34.99% Transportation, 4.18% Non-Identified, and 1.51% Non-Energetic with 3.44% utilized by agricultural, mining, and construction sectors [5]. Each of these sectors is comprised of different factors with unique variables that dictate the behavior of each energy sector and thus, the overall energy demand of the country. The understanding of these variables is an important problem for the economy of the world due to the unobserved influence that the variables have at the time of planning the energy demand of any country. Thus researchers have been actively developing mathematical and statistical 6 techniques to untangle the relationship of each variable with regards to the energy demand [6]. However, as is the case of Colombia, many developing countries have founded their energy planning decisions in economic variables; ignoring sociocultural and environmental factors that own a certain level of influence at the time of representing a real approximation of the behavior of the energy demand [6]. Thus energy planning entities have disclosed different evaluations about the impact of social and environmental factors demonstrating that current energy planning practices must be improved [7]. To provide a better understanding of the energy demand and thus the decision-making that is reflected in the economic growth of any country [6,8], researchers also emphasize in the importance of Energy Planning Models (EPMs). EPM is a type of forecasting approach that countries and stakeholders rely on making appropriate decisions in terms of the formulation of energy policies and the sustainability of the energy sector[9]. Consequently, the selection of a forecasting methodology is dictated by the data availability, the objectives of the planning exercise, and the conceptual approach of the selected methodology. Currently, EPMs can be divided into five categories: Energy Information Systems, Systems Macroeconomics, Energy Supply, Energy Demand, and Integrated Models [9]. Although EPMs are worldwide tools designed to focus on energy demand and load forecasting this study has found that EPMs’ applicability is focused on developing nations where the study of new factors have been added into energy planning practices, putting aside those developing countries that have not moved from outdated EPM techniques [9]. Given the factors that influence the focus of EPMs in the light of developing an interdisciplinary, international work, this master project will define an energy planning methodology that will allow Colombia, as well as other developing countries, to improve their current EPM practices. Furthermore, this research aims to create an energy-based model that will be used to untangle the variables dictating the behavior of the social, environmental, economic, trading, and energy transformation factors that represent the energy demand of Colombia, using the residential energy sector of the country as a planning exercise.Modalidad Presencialapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Development of an Energy-Based Model for Forecasting the Energy Demand of ColombiaDesarrollo de un modelo basado en energía para proyectar la demanda de energía de ColombiaResearch reportinfo:eu-repo/semantics/workingPaperInforme de investigaciónhttp://purl.org/coar/resource_type/c_18wshttp://purl.org/coar/resource_type/c_8042info:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/IFIUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaEnergy consumptionEnergetic resourcesEnergy demandEnergy supplyEnergetic industryEconomic sectorEnergy based modelConsumo de energíaRecursos energéticosDemanda de energíaAbstecimiento de energíaIndustria energéticaColombiaSector económicoModelo basado en energía[1] E. 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Starmer, Department of Statistics and Operations Research - The University of North Carolina at Chapel Hill. - Statistics and Operations https://statquest.org/.ORIGINAL2023_Development_of_an_.pdf2023_Development_of_an_.pdfInformeapplication/pdf1794967https://repository.unab.edu.co/bitstream/20.500.12749/20824/1/2023_Development_of_an_.pdfac478999a2e7ba49783bbe5eea3aa2d6MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8829https://repository.unab.edu.co/bitstream/20.500.12749/20824/2/license.txt3755c0cfdb77e29f2b9125d7a45dd316MD52open accessTHUMBNAIL2023_Development_of_an_.pdf.jpg2023_Development_of_an_.pdf.jpgIM Thumbnailimage/jpeg6811https://repository.unab.edu.co/bitstream/20.500.12749/20824/3/2023_Development_of_an_.pdf.jpgb364182ab6d946a54b6af6920f2cbb19MD53open access20.500.12749/20824oai:repository.unab.edu.co:20.500.12749/208242024-01-18 10:05:05.854open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.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

Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia

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