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

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dc.contributor.apolounabPacheco Sandoval, Leonardo Esteban [leonardo-esteban-pacheco-sandoval]spa
dc.contributor.apolounabSuárez Arias, Rafael Enrique [rafael-enrique-suarez-arias]spa
dc.contributor.apolounabGonzález Calderón, William [william-gonzález-calderón]spa
dc.contributor.authorPacheco Sandoval, Leonardo Esteban
dc.contributor.authorGonzález Calderón, William
dc.contributor.authorSuárez Arias, Rafael Enrique
dc.contributor.cvlacPacheco Sandoval,Leonardo Esteban [spa
dc.contributor.cvlacSuárez Arias, Rafael Enrique [0001429372]spa
dc.contributor.cvlacGonzález Calderón, William [0001367421]spa
dc.contributor.googlescholarPacheco Sandoval, Leonardo Esteban [es&oi=ao]spa
dc.contributor.orcidPacheco Sandoval,Leonardo Esteban [0000-0001-7262-382X]spa
dc.contributor.orcidSuárez Arias, Rafael Enrique [0000-0001-9767-210X]spa
dc.contributor.researchgroupGrupo de Investigación Recursos, Energía, Sostenibilidad - GIRESspa
dc.contributor.researchgroupGrupo de Investigaciones Clínicasspa
dc.coverage.campusUNAB Campus Bucaramangaspa
dc.coverage.spatialBucaramanga (Santander, Colombia)spa
dc.date.accessioned2023-07-28T18:28:02Z
dc.date.available2023-07-28T18:28:02Z
dc.date.issued2023-03
dc.description.abstractPara 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.spa
dc.description.abstractenglishTo 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.spa
dc.description.learningmodalityModalidad Presencialspa
dc.description.tableofcontentsCHAPTER 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 ...................................................................................................................................... 1spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga - UNABspa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.identifier.urihttp://hdl.handle.net/20.500.12749/20824
dc.language.isospaspa
dc.publisher.facultyFacultad Ingenieríaspa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.subject.keywordsEnergy consumptionspa
dc.subject.keywordsEnergetic resourcesspa
dc.subject.keywordsEnergy demandspa
dc.subject.keywordsEnergy supplyspa
dc.subject.keywordsEnergetic industryspa
dc.subject.keywordsEconomic sectorspa
dc.subject.keywordsEnergy based modelspa
dc.subject.lembConsumo de energíaspa
dc.subject.lembRecursos energéticosspa
dc.subject.lembDemanda de energíaspa
dc.subject.lembAbstecimiento de energíaspa
dc.subject.lembIndustria energéticaspa
dc.subject.lembColombiaspa
dc.subject.proposalSector económicospa
dc.subject.proposalModelo basado en energíaspa
dc.titleDevelopment of an Energy-Based Model for Forecasting the Energy Demand of Colombiaspa
dc.title.translatedDesarrollo de un modelo basado en energía para proyectar la demanda de energía de Colombiaspa
dc.typeResearch reporteng
dc.type.coarhttp://purl.org/coar/resource_type/c_18ws
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driverinfo:eu-repo/semantics/workingPaperspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.localInforme de investigaciónspa
dc.type.redcolhttp://purl.org/redcol/resource_type/IFI

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Grupo de Investigación Recursos, Energía, Sostenibilidad - GIRES