Mostrar el registro sencillo del ítem

Desarrollo de un modelo económico de energía para pronosticar la demanda energética por sectores de consumo en Colombia

dc.contributor.advisorPacheco Sandoval, Leonardo Estebanspa
dc.contributor.authorJaramillo Villarreal, Laura Catalinaspa
dc.coverage.spatialColombiaspa
dc.date.accessioned2020-10-01T14:39:36Z
dc.date.available2020-10-01T14:39:36Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/20.500.12749/7265
dc.description.abstractEl siguiente trabajo de grado se realizo con base al semillero de investigación Prospectiva Energética" y el programa `4+1' entre la Universidad Autónoma de Bucaramanga (Unab) y Oregon Institute of Technology (OIT) para cumplir con el requisito de grado en Ingeniería en Energía en la Unab y establecer una ruta de continuidad hacia estudios de maestría en el exterior con el programa de Master of Science in Renewable Energy Engineering { MSREE de OIT. En cumplimiento parcial del programa `4+1', este trabajo de grado propone el desarrollo de una planifi cación energética en Colombia mediante un modelo económico de energía para pronosticar la demanda de energía por sectores de consumo, ademas, promueve la implementación de análisis prospectivos para estudiar la demanda energética del país. Utilizando el análisis de regresión múltiple, técnicas de prospectiva y \multi-criteria decision-making (MCDM)", este proyecto proporciona una metodología sistemática para identifi car variables económicas que impactan la demanda de energía. Los sectores de transporte, comercial, industrial, residencial, agricultura, minería y construcción se consideran dentro de este estudio para ejecutar la metodología. Los resultados muestran que el sector de minería y construcción no refleja un alto consumo en la demanda total de energía de Colombia y esos sectores están dictados no solo por variables económicas. Además, la demanda de energía residencial, de transporte y comercial está altamente correlacionada con el factor económico.spa
dc.description.sponsorshipOregon Institute of Technology OITspa
dc.description.tableofcontentsLIST OF TABLES iii LIST OF FIGURES iv 1 Introduction 1 1.1 Introduction & Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem De nition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Signi cance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 R&D Objectives & Thesis Contributions . . . . . . . . . . . . . . . . . . . . 5 1.5 R&D Orientation, Methods & Materials . . . . . . . . . . . . . . . . . . . . 7 2 Background & Literature Review 9 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Patent Landscape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 Scienti c Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3 Methodology 21 3.1 Fundamental Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3 Correlation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.4 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.5 Signi cance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.6 MicMac analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.7 Variables con rmation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4 Results and Performance Assessment 37 4.1 Fundamental Analysis of the Macroeconomic Variables . . . . . . . . . . . . 37 4.1.1 Transport Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.1.2 Commercial Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.1.3 Industrial Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.1.4 Residential Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 i 4.1.5 Agriculture Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.1.6 Mining Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.1.7 Construction Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.1.8 Gross Domestic Product, GDP . . . . . . . . . . . . . . . . . . . . . 50 4.1.9 Producer Price Index, PPI . . . . . . . . . . . . . . . . . . . . . . . . 52 4.1.10 Consumer Price Index, CPI . . . . . . . . . . . . . . . . . . . . . . . 53 4.1.11 West texas intermediate, WTI . . . . . . . . . . . . . . . . . . . . . . 55 4.1.12 USD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.1.13 Foreign direct investment in Colombia, FDI . . . . . . . . . . . . . . 57 4.1.14 Trade balance: Imports and Exports . . . . . . . . . . . . . . . . . . 57 4.2 Data Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.3 Correlation Analysis by Sectors of Consumption . . . . . . . . . . . . . . . . 61 4.4 Multi-regression analysis & Macroeconomic Variables Selection . . . . . . . . 70 4.5 Micmac analysis assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6 Energy Based Model by Sectors of Consumption . . . . . . . . . . . . . . . . 83 5 Conclusions 96 5.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 A Macroeconomic Variables Selection Result 111 A.1 Transport sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 A.2 Industrial Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 A.3 Commercial Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 A.4 Residential Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 A.5 Agriculture Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 B Cross-impact matrix applied to MICMAC analysis 116 B.1 Transport Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 B.2 Commercial Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 B.3 Industrial Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 B.4 Residential Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 B.5 Agriculture Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 C Sampling error of the Energy Demand in Colombia 119spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titleDesarrollo de un modelo económico de energía para pronosticar la demanda energética por sectores de consumo en Colombiaspa
dc.title.translatedDevelopment of an economic energy model to forecast energy demand by consumer sectors in Colombiaspa
dc.degree.nameIngeniero en Energíaspa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
dc.rights.localAbierto (Texto Completo)spa
dc.publisher.programPregrado Ingeniería en Energíaspa
dc.description.degreelevelPregradospa
dc.type.driverinfo:eu-repo/semantics/bachelorThesis
dc.type.localTrabajo de Gradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.subject.keywordsEnergy engineeringeng
dc.subject.keywordsTechnological innovationseng
dc.subject.keywordsEnergyeng
dc.subject.keywordsEnergetic resourceseng
dc.subject.keywordsPower supplyeng
dc.subject.keywordsEnergy consumptioneng
dc.subject.keywordsEnergy sectoreng
dc.subject.keywordsEnergy consumptioneng
dc.subject.keywordsEnergy planningeng
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga - UNABspa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.type.hasversioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
dc.relation.references[1] Energy Charter Secretariat, Colombia energy investment report, Brussels, Belgium, 2016.spa
dc.relation.references[2] UPME, 'UPME Unidad de Planeaci on Minero energ etica'. Unidad de Planeacion Energetica de Colombia, Excel Sheet. Retrieved 4 Jun 2020, from http://www1.upme. gov.co/InformacionCifras/Paginas/BalanceEnergetico.aspxspa
dc.relation.references[3] XM S.A. E.S.P. Informe de Operaci on del SIN y Administraci on del Mercado 2013, 2013. Retrieved 4 Jun 2020, fromhttp://informesanuales.xm.com.co/2013/ SitePages/operacion/Default.aspxspa
dc.relation.references[4] Burak Omer Saracoglu, Long Term Electricity Demand & Peak Power Load Forecasting Variables Identi cation & Selection. Science Journal of Circuits, Systems and Signal Processing.2017; 6(2): 18-28. DOI: 10.11648/j.cssp.20170602.13spa
dc.relation.references[5] Kumar Biswajit Debnath, Monjur Mourshed. Forecasting methods in energy planning models, Renewable and Sustainable Energy Reviews. Vol.; 88. No (May.2018); p. 297- 325.spa
dc.relation.references[6] UPME, MINMINAS. Proyecci on de la demanda de energ a el ectrica y potencia m axima en Colombia. Retrieved 4 Jun 2020, from http://www.siel.gov.co/siel/ documentos/documentacion/Demanda/UPME_Proyeccion_Demanda_ Energia_Electrica_Junio_2016.spa
dc.relation.references[7] ISA. Home page. Retrieved 4 Jun 2020, from http://www.isa.co/en/Pages/ default.aspxspa
dc.relation.references[8] Pron ostico de Demanda XM. Retrieved 4 Jun 2020, from https://www.xm.com. co/Paginas/Consumo/pronostico-de-demanda.aspxspa
dc.relation.references[9] ECOPETROL, UPME, UNAB, UIS, and UPB. Prospectiva energ etica Colombia 2050 - ISBN 978-958-8956-50-3. Bucaramanga: Universidad Industrial de Santander, 2018spa
dc.relation.references[10] R. Schae er et al., Energy sector vulnerability to climate change: A review, Energy, vol. 38, no. 1, pp. 1-12, February 2012spa
dc.relation.references[11] P. H. Abreu, D. C. Silva, H. Amaro, and R. Magalhaes. Identi cation of Residential Energy Consumption Behaviors, Journal of Energy Engineering, vol. 142, no. 4, p. 04016005, Dec. 2016spa
dc.relation.references[12] DeLlano-Paza, Fernando; Calvo-Silvosaa, Anxo; Iglesias Anteloa, Susana; Soares, Isabel. Energy planning and modern portfolio theory: A review, Renewable and Sustainable Energy Reviews, Vol. 77, pp. 636-651, September 2017spa
dc.relation.references[13] S. Pfenninger, A. Hawkes, and J. Keirstead, Energy systems modeling for twenty- rst century energy challenges, Renewable and Sustainable Energy Reviews, vol. 33, pp. 74-86, May 2014spa
dc.relation.references[14] Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli, Rob J Hyndman. 2016. Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond. International Journal of Forecasting, Issue: 3, Volume: 32, Page: 896- 913spa
dc.relation.references[15] Pohekar, S. D., Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning - A review, Renewable and Sustainable Energy Reviews, 8(4), 365-381. DOI:10.1016/j.rser.2003.12.007spa
dc.relation.references[16] Swan, L. G.; Ugursal, V. I. Modeling of end use energy consumption in the residential sector: A review of modeling techniques, Renewable and Sustainable Energy Reviews. 2009, Vol. 13, Pages 1819-1835.spa
dc.relation.references[17] Han Shih, Suchithra Rajendran. Comparison of Time Series Methods and Machine Learning Algorithms for Forecasting Taiwan Blood Services Foundation's Blood Supply. J Healthc Eng. 2019; 2019: 6123745.doi: 10.1155/2019/61237spa
dc.relation.references[18] Wolfgang Schellong. 2011. Energy Demand Analysis and Forecast. Energy Management Systems.spa
dc.relation.references[19] Dmitry Kucharavy, Roland De Guio. Logistic substitution model and technological forecasting, Procedia Engineering. Vol. 9, 2011, Pages 402-416spa
dc.relation.references[20] Modis, Theodore. Forecasting energy needs with logistics, Technological Forecasting and Social Change. Vol 139, February 2019, Pages 135-143spa
dc.relation.references[21] Mart nez, Viviana; Castillo, O.L. Colombian energy planning - Neither for energy, nor for Colombia, Energy Policy. Vol.; 129. No (2019); p. 1132-1142spa
dc.relation.references[22] Unidad de planeaci on minero energ etica. Plan energ etico nacional Colombia: ideario energ etico 2050. Bogota, Colombia. January 2015.spa
dc.relation.references[23] Jose Andres Suarez Diaz, Development of an Energy-Based Model for Forecasting the Energy Demand of Colombia, Oregon Institute of Technology, June 2019.spa
dc.relation.references[24] Sawa, Toshiyuki; Mori, Shigeki; Yamazaki, Jun, Forecast system and method of electric power demand. U.S. Patent 9852483 B2, 26 December 2017spa
dc.relation.references[25] Harper, Karl E; Kielszewski, Elizabeth; Fox Jr, Thomas C; Manning, Paul B. Distributed Utility Resource Planning And Forecast. U.S. Patent 10250034 B2, 2 April 2019spa
dc.relation.references[26] Dannecker, Lars; Roesch, Philipp, Dynamic Online Energy Forecasting U.S. Patent 9672304 B2, 6 June 2017.spa
dc.relation.references[27] Honjo, Ryoki; Tadano, Taro, Electric Power Management Apparatus And Electric Power Management Method. U.S. Patent 9588145 B2, 7 March 2007.spa
dc.relation.references[28] Ko Jong-Min; Jung Nam-Joon; Kim Young-Il; Yu In-Hyeob, Load Forecasting Analysis System For Calculating Customer Baseline Load. U.S. Patent 8406935 B2, 26 March 2013.spa
dc.relation.references[29] Ko Jong-Min; Jung Nam-Joon; Kim Young-Il; Yu In-Hyeob, Load Forecasting Analysis System For Calculating Customer Baseline Load. U.S. Patent 8406935 B2, 26 March 2013spa
dc.relation.references[30] Mateus Valencia, Andres Camilo. Energy Crisis in Colombia.Tecnolog a, Investigaci on y Academia, TIA. ISSN: 2344-8288 Vol. 4 No. 2 pp. 74spa
dc.relation.references[31] Amir Hossein Fakehi Khorasani, Somayeh Ahmadi, Mohammad Ali Moradi. The Impact of Energy Conservation Policies on the Projection of Future Energy Demand, Energy Technology and Policy, 2015. 2:1, 104-121, DOI: 10.1080/23317000.2015spa
dc.relation.references[32] United Nation ESCAP: Environment and Natural Resources Development Division: Scrotal Energy Demand Analysis and Longterm Forecast: Methodological Manual. MEDEE-S.No: ST/ESCAP/ 1521, 19spa
dc.relation.references[33] International Atomic Energy Agency, IAEA. Computer Tools for Comparative Assessment of Electricity Generation Options and Strategies. Vienna, Austria.spa
dc.relation.references[34] Stefano Moret, V ctor Codina Girones, Michel Bierlaire, Francois Mar echal. Characterization of input uncertainties in strategic energy planning models, Applied Energy. Vol. 202, 15 September 2017. Pages 597-617spa
dc.relation.references[35] Saboohi, Y. Model for Analysis of Demand for Energy - MADE II. Institute fur Kernenergetik und Energiesysteme (IKE), University of Stuttgart, Technical Report, IKE 8-19, 1989: 0173-6892spa
dc.relation.references[36] O cial LEAP Website. Retrieved 4 Jun 2020, from https://www. energycommunity.org/default.asp?action=introductionspa
dc.relation.references[37] User guide for long-range energy alternative planning system. Boston, MA, 2011spa
dc.relation.references[38] Departamento Administrativo Nacional de Estad stica, DANE. New classi cation of economic activities, Bogota. 2012.spa
dc.relation.references[39] Unidad de planeaci on minero energ etica, Colombian energy balance - BECO, energy, Bogota. 2017. Retrieved 4 Jun 2020, from http://www1.upme.gov.co/ InformacionCifras/Paginas/BECOENERGTICO.aspxspa
dc.relation.references[40] Departamento Administrativo Nacional de Estad stica, DANE. Technical bulletin - Gross Domestic Product (GDP) fourth quarter 2018, 2018spa
dc.relation.references[41] Departamento Administrativo Nacional de Estad stica, DANE. Technical bulletin - Gross Domestic Product (GDP) fourth quarter 2019, 2019spa
dc.relation.references[42] ANDI, Colombia: Balance 2018 and Perspectives 2019. Retrieved 4 Jun 2020 from https://imgcdn.larepublica.co/cms/2018/12/28132344/ ANDI-Balance-y-Perspectivas.pdf?w=auto; 2018spa
dc.relation.references[43] ANDI, Colombia: Balance 2019 and Perspectives 2020. Retrieved 4 Jun 2020, from http://www.andi.com.co/Uploads/ANDI%20-%20Balance%202019% 20y%20Perspectivas%202020%20-%20VF.pdf; 2019.spa
dc.relation.references[44] Departamento nacional de planeaci on, enersinc. Energy Demand Situation in Colombia. Bogota, 2017.spa
dc.relation.references[45] Triantaphyllou, E. (2000). Multi-Criteria Decision Making: A Comparative Study. Dordrecht, The Netherlands: Kluwer Academic Publishers (now Springer). p. 320. ISBN 978-0-7923-6607-2.spa
dc.relation.references[46] Michel Godet. Manuel de prospective strat egique.Tome 1. Dunod, Paris, 2007. ISBN 978-2-10-053161-5spa
dc.relation.references[47] Michel Godet. Manuel de prospective strat egique.Tome 2. Dunod, Paris, 2007. ISBN 978-2-10-053162-2spa
dc.relation.references[48] I. BarCharts, Statistics: parameters, variables, intervals, proportions: the basic principles of statistics for introductory courses. 2005.spa
dc.relation.references[49] J. C. A. Guti errez and J. F. A. Mahecha, Rep ublica de Colombia Ministerio de Minas y Energ a Unidad de Planeaci on Minero Energ etica, UPME. Subdirecci on de Planeaci on Energ etica - Grupo de Demanda Energ etica, p. 51spa
dc.relation.references[50] A. F. Paez et al., Future Scenarios and Trends of Energy Demand in Colombia using Long-range Energy Alternative Planning, vol. 7, no. 5, p. 13, 2017spa
dc.relation.references[51] Schober, Patrick MD, PhD, MMedStat; Boer, Christa PhD, MSc; Schwarte, Lothar A. MD, PhD, MBA.Correlation Coe cients: Appropriate Use and Interpretation, Anesthesia Analgesia: May 2018 - Volume 126 - Issue 5 - p 1763-1768spa
dc.relation.references[52] Richard Taylor.Interpretation of the Correlation Coe cient: A Basic ReviewVol 6, Issue 1, 1990spa
dc.relation.references[53] C. Alonso, Modelo de Regresion lineal Multiple - Econometria Universidad Carlos III de Madridspa
dc.relation.references[54] Patricia Moreno, Juan Manuel Rodriguez Poo, Alexandra Soberon. ECONOMETRIA I. El Modelo de Regresion Lineal Simple. Retrieved 4 Jun 2020, from https://ocw.unican.es/pluginfile.php/1127/course/section/ 1352/Ppt_Ch2_G942_14-15.pdfspa
dc.relation.references[55] Velasco Sotomayor,G. ; Wisniewski, P. (2002) Probabilidad y Estad stica para Ingenier a y Ciencias. Editorial Thomson.spa
dc.relation.references[56] Mendenhall,W. ; Wackerly,D.; Shea er, R.(2004) Estad stica Matem atica con Aplicaciones. Grupo Editorial Iberoam ericaspa
dc.relation.references[57] Garc a, R. (2004). Inferencia estad stica y dise~no de experimentos. Buenos Aires: Eudebaspa
dc.relation.references[58] Montgomery,D. ; Peck,E. y Vinning, G. (2002). Introducci on al An alisis de Regresi on Lineal. Editorial C.E.C.S.A.spa
dc.relation.references[59] Peter Gri thsa, Jack Needleman. Statistical signi cance testing and p-values: Defending the indefensible: A discussion paper and position statement. International Journal of Nursing Studies. Volume 99, November 2019, 103384spa
dc.relation.references[60] Ronald L. Wassersteina* Nicole A. Lazara. The ASA's Statement on p-Values: Context, Process, and Purpose. Pages 129-133. DOI = https://doi.org/10.1080/00031305.2016.1154108spa
dc.relation.references[61] Leenen I. La prueba de la hip otesis nula y sus alternativas: revisi on de algunas cr ticas y su relevancia para las ciencias m edicas. Inv Ed Med 2012; 1(4), Pages 225-234.spa
dc.relation.references[62] Y a~nez S. La estad stica una ciencia del siglo XX. R.A. Fisher. Rev Colomb Estad stica 2000; 23(2), Pages 1-14.spa
dc.relation.references[63] Sharon Einav, Ph.D, M.D, Michael O'Connor, M.D. P-values and signi cance: The null hypothesis that they are not related is correct. Journal of Critical Care. Volume 54, December 2019, Pages 159-162spa
dc.relation.references[64] Eur J Vasc Endovasc Surg. Signi cance and Limitations of the p Value. Education Section: Associate Editors Florian Dick and Gert de Borst. 2015, Volume 50, Page 815spa
dc.relation.references[65] Sterne JAC, Davey Smith G. Sifting the evidence | what's wrong with signi cance tests? BMJ 2001; 322, Pages 226-231.spa
dc.relation.references[66] Michel godet. De la anticipaci on a la acci on, manual de prospectiva y estrategia. Retrieved 4 Jun 2020, from https://administracion.uexternado.edu. co/matdi/clap/De%20la%20anticipaci%C3%B3n%20a%20la%20acci%C3% B3n.pdfspa
dc.relation.references[67] Unidad de planeaci on minero energ etica. Caracterizaci on energ etica del sector residencial urbano y rural en Colombia. volumen I - Metodolog a y An alisis. Bogota, CO. March 2012spa
dc.relation.references[68] Departamento nacional de planeaci on, enersinc. Energy Demand Situation in Colombia. Bogota, 2017spa
dc.relation.references[69] Unidad de planeaci on minero energ etica, UPME. Plan transitorio de abastecimiento de gas natural. Bogot a, noviembre de 2016.spa
dc.relation.references[70] Rep ublica de Colombia - Ministerio de Minas y Energ a, Unidad de Planeaci on Minero Energ etica. Plan de Abastecimiento de Gas Natural Documento de Trabajo. Bogot a, diciembre de 2013.spa
dc.relation.references[71] ANDI. 07 agroindustria, Hacia la transformaci on de la cadena de valor agroindustrial. 2018spa
dc.relation.references[72] FINAGRO. Perspectiva del sector agropecuario Colombiano. Bogota, August 2014. Retrieved 4 Jun 2020, from https://www.finagro.com.co/sites/default/ files/2014_09_09_perspectivas_agropecuarias.pdfspa
dc.relation.references[73] Rilong Fei, Boqiang Lin. Estimates of energy demand and energy saving potential in China's agricultural sector. Energy 135 (2017) 865-875spa
dc.relation.references[74] Ministerio de minas y energ a. Sector minero en Colombia, crecimiento sostenible y competitividad.spa
dc.relation.references[75] ANDI. Jaime Mauricio Concha. Una mirada al sector minero energ etico. 26 de septiembre de 2018spa
dc.relation.references[76] Andr es Casta~no, Marcelo Lu n, Miguel Atienz. A structural path analysis of Chilean mining linkages between 1995 and 2011. What are the channels through which extractive activity a ects the economy?. Resources policies 60 (2019) 106-117spa
dc.relation.references[77] Lizhen Huanga, Guri Krigsvolla, Fred Johansena, Yongping Liua, Xiaoling Zhang. Carbon emission of global construction sector. Renewable and Sustainable Energy Reviews. 81 (2018) 1906{1916spa
dc.relation.references[78] Carlos Oliveira Cruza, Patr cia Gasparb, Jorge de Brito. On the concept of sustainable sustainability: An application to the Portuguese construction sector. Journal of Building Engineering. Volume 25, September 2019, 100836spa
dc.relation.references[79] Stephan B. Bruns, Christian Gross and David I. Stern. Is There Really Granger Causality Between Energy Use and Output? Alemania, agosto de 2013. Retrieved 4 Jun 2020, from https://www.fcn.eonerc.rwth-aachen.de/global/show_ document.asp?id=aaaaaaaaaagvwasspa
dc.relation.references[80] Jaganath Behera. Examined the energy-led growth hypothesis in India: evidence from time series analysis. En: Energy Economics Letters. Vol.; 2. No (2015); DOI: 10.18488/journal.82/2015.2.4/82.4.46.65spa
dc.relation.references[81] Stephan B. Bruns, Christian Gross. What if energy time series are not independent? Implications for energy-GDP causality analysis. Energy Economics. Vol.; 40, No (Nov.2013); p.753-759spa
dc.relation.references[82] Christian Gross. Explaining the (non-) causality between energy and economic growth in the U.S.|A multivariate sectoral analysis. Energy Economics. Vol.; 34, No (Mar.2012); p.489-499spa
dc.relation.references[83] Zachariadis, Theodoros. Exploring the relationship between energy use and economic growth with bivariate models: new evidence from G-7 countries. En: Energy Economics. Vol.; 29, No (May.2007); p.1233{1253spa
dc.relation.references[84] Bowden, N., Payne, J.E. The causal relationship between U.S. energy consumption and real output: a disaggregated analysis. J. Policy Model. Vol.;31. No (2009); p.180{188.spa
dc.relation.references[85] Departamento Administrativo Nacional de Estad stica. Producto Interno Bruto (PIB) Hist oricos. En l nea. 2018. 18 de abril de 2019. Retrieved 4 Jun 2020, from https://www.dane.gov.co/index.php/estadisticas-por-tema/ cuentas-nacionales/cuentas-nacionales-trimestrales/ historicos-producto-interno-bruto-pib#base-2005spa
dc.relation.references[86] Understanding the energy- GDP elasticity: A sectoral approach - Paul J. Burke , Zsuzsanna Csereklyei - Energy Economics - 58 2016 199/210 M.spa
dc.relation.references[87] Comisi on de regulaci on de energ a y gas. Resoluci on CREG 119 de 2007. Costo unitario de prestaci on del servicio de energ a el ectrica. Capitulo III, 21 de diciembre de 2007.spa
dc.relation.references[88] Banco de Occidente. An alisis macroecon omico y sectorial: coyuntura y perspectivas Junio - 2018. Retrieved 4 Jun 2020, from https://www.bancodeoccidente.com.co/wps/wcm/connect/ banco-de-occidente/0f02cfa3-83c9-4f7e-bb2d-7ee32e20a4eb/ informe-sectorial-anif-jul-2018.pdf?MOD=AJPERES&CVID=mijQdGxspa
dc.relation.references[89] Mercado de Energ a y Tarifas. Codensaspa
dc.relation.references[90] Comisi on de regulaci on de energ a y gas. Resoluci on CREG 034 de 2001. Precio de Reconciliaci on Positiva de los Generadores. Art culo 1, 13 de marzo de 2011.spa
dc.relation.references[91] Price and income elasticities of residential energy demand in Germany - Isabella Schultea, Peter Heindlb, - Energy Policy { Energy Policy 102 (2017) 512{528spa
dc.relation.references[92] Santiago Arango-Aramburo, Patricia Jaramillo, Yris Olaya, Ricardo Smith, Oscar J. Restrepo a, Adrian Saldarriaga-Isaza, Jessica Arias-Gaviria, Juan F. Parra, Erik R. Larsen, Luz M. Gomez-Rios, Lady Y. Castellanos. Simulating mining policies in developing countries: The case of Colombia. En: Socio-Economic Planning Sciences. Vol.; 60. No (Dic.2017); p.99-113.spa
dc.relation.references[93] M onica Espinosa Valderrama, Angela In es Cadena Monroy, Eduardo Behrentz Valencia. Challenges in greenhouse gas mitigation in developing countries: A case study of the Colombian transport sector. En: Energy Policy. Vol.; 124. No (Ene.2019); p.111-122spa
dc.relation.references[94] Energy Information Administration, eia. What drives crude oil prices: Overview. En l nea. 2019. 18 de abril de 2019. Retrieved 4 Jun 2020, from https://www.eia.gov/ finance/markets/crudeoil/spa
dc.relation.references[95] Long-run and short-run relationships between oil prices, producer prices, and consumer prices: What can we learn from a permanent-transitory decomposition? - Robert J. Myers a,, Stanley R. Johnsonb, Michael Helmar c, Harry Baumes d { The Quarterly Review of Economics and Finance 67 (2018) 175{190spa
dc.relation.references[96] Does oil price volatility in uence real sector growth? Empirical evidence from Pakistan - Humaira Yasmeen , Ying Wang, Hashim Zameer, Yasir Ahmed Solangi| Energy Reports 5 (2019) 688{703spa
dc.relation.references[97] Di Mo, Rakesh Gupta, Bin Li, Tarlok Singh. The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets. En: Economic Modelling. Vol.; 70. No (Abr.2018); p. 543-560.spa
dc.relation.references[98] Rihab Bedouia, Sana Braeikb, St ephane Goutted, Khaled Guesmi. On the study of conditional dependence structure between oil, gold and USD exchange rates. En: International Review of Financial Analysis. Vol.; 59. No (Oct.2018); p. 134-146.spa
dc.relation.references[99] Rihab Bedouia, Sana Braieka, Khaled Guesmi, Julien Chevallier. On the conditional dependence structure between oil, gold and USD exchange rates: Nested copula based GJR-GARCH model. En: Energy Economics. Vol.; 80. No (May.2019); p. 876-889.spa
dc.relation.references[100] The economic value of co-movement between oil price and exchange rate using copulabased GARCH models{ Chih-Chiang Wu a, , Huimin Chung b , Yu-Hsien Chang{ Energy Economics 34 (2012) 270{282spa
dc.relation.references[101] Engle, Robert F., GARCH 101: An Introduction to the Use of Arch/Garch Models in Applied Econometrics (October 2001). NYU Working Paper No. FIN-01-030spa
dc.relation.references[102] Banco de la Rep ublica, Departamento de Cambios Internacionales. Inversi on Extranjera Directa en Colombia. En l nea. 2012. 1 de mayo de 2019. Retrieved 4 Jun 2020, from http://www.banrep.gov.co/sites/default/files/ publicaciones/archivos/ce_dcin_inversionextranjera.pdfspa
dc.relation.references[103] Banco de la rep ublica. Bolet n de indicadores econ omicos. 29 de abril de 2019spa
dc.relation.references[104] ChongmeiWang, Chu Jiayu. Analyzing on the Impact Mechanism of Foreign Direct Investment( FDI) to Energy Consumption. En: Energy Procedia. Vol.; 159. No (Feb.2019); p. 515-520.spa
dc.relation.references[105] Alexander RyotaKeeley, YuichiIkeda. Determinants of foreign direct investment in wind energy in developing countries. En: Journal of Cleaner Production. Vol.; 161. No (Sep.2017); p. 1451-1458spa
dc.relation.references[106] DANE. Balanza comercial. Retrieved 4 Jun 2020, from https://www.dane.gov. co/index.php/estadisticas-por-tema/comercio-internacional/ balanza-comercialspa
dc.relation.references[107] Interactions between energy and imports in Singapore: Empirical evidence from conditional error correction models{ Salih Turan Katircioglu{ Energy Policy 63 (2013) 514{520spa
dc.relation.references[108] How (a)symmetric is the response of import demand to changes in its determinants? Evidence from European energy imports{ Svetlana Fedoseeva a, Rodrigo Zeidan{Energy Economics 69 (2018) 379{394spa
dc.relation.references[109] Bustos, 2011. Trade liberalization ,exports, and technology upgrading: evidence on the impact of MERCOSUR on Argentinian rms. Am. Econ. Rev. 101 (1), 304-340spa
dc.relation.references[110] Lileeva, A., Tre er, D., 2010. Improved Access to foreign markets raises plant-level productivity for some plants. Q.J.Econ. 125 (3), 1051-1099.spa
dc.relation.references[111] Roy, J., Yasar, M., 2015. Energy e ciency and exporting: evidence from rm-level data. Energy Econ. 52, 127-135spa
dc.relation.references[112] Sadorsky, P., 2012. Energy consumption, output and trade in South American. Energy Econ. 34 (2), 476-488.7spa
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001478220*
dc.contributor.googlescholarhttps://scholar.google.es/citations?hl=es&user=yZ1HEiIAAAAJ*
dc.contributor.orcidhttps://orcid.org/0000-0001-7262-382X*
dc.contributor.scopushttps://www.scopus.com/authid/detail.uri?authorId=56117105700*
dc.contributor.researchgatehttps://www.researchgate.net/profile/Leonardo_Esteban_Pacheco_Sandoval*
dc.subject.lembIngeniería en energíaspa
dc.subject.lembInnovaciones tecnológicasspa
dc.subject.lembEnergíaspa
dc.subject.lembRecursos energéticosspa
dc.subject.lembAbastecimiento de energíaspa
dc.subject.lembConsumo de energíaspa
dc.subject.lembSector energéticospa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.description.abstractenglishThe following degree work was carried out based on the Prospectiva Energética research hotbed "and the` 4 + 1 'program between the Autonomous University of Bucaramanga (Unab) and the Oregon Institute of Technology (OIT) to meet the engineering degree requirement in Energy at the Unab and establish a continuity path towards master's studies abroad with the Master of Science in Renewable Energy Engineering program {ILO MSREE. In partial fulfillment of the '4 + 1' program, this degree project proposes the development of an energy planning in Colombia through an economic energy model to forecast energy demand by consumption sectors, in addition, it promotes the implementation of prospective analyzes to study the country's energy demand. Using multiple regression analysis, prospective techniques, and \ multi-criteria decision-making (MCDM) ", this project provides a systematic methodology to identify economic variables that impact energy demand. The transportation, commercial, industrial, Residential, agriculture, mining and construction are considered within this study to execute the methodology.The results show that the mining and construction sector does not re There is a high consumption in Colombia's total energy demand and these sectors are dictated not only by economic variables. In addition, the demand for energy residential, transportation and commercial is highly correlated with the economic factor.eng
dc.subject.proposalConsumo de energíaspa
dc.subject.proposalPlanificación energéticaspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TP
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.coverage.campusUNAB Campus Bucaramangaspa
dc.description.learningmodalityModalidad Presencialspa


Ficheros en el ítem

Thumbnail
Nombre:
2020_Tesis_Laura_Catalina_Jara ...
Tamaño:
5.001Mb
Formato:
PDF
Descripción:
Tesis
Ver/
Thumbnail
Nombre:
2020_Licencia_Catalina_Jaramil ...
Tamaño:
156.3Kb
Formato:
PDF
Descripción:
Licencia
Ver/

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Atribución-NoComercial-SinDerivadas 2.5 Colombia
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución-NoComercial-SinDerivadas 2.5 Colombia