Energy Prospective Model For The Forecast of Future Scenarios in Colombia
Escenarios de sectores de consumo de energía
DOI:
https://doi.org/10.37467/revtechno.v14.4835Keywords:
Energy consumption, Energy, Future scenarios, Model, Forecast Prospective, Consumer sectorsAbstract
This investigation presents the development of a prospective model for the forecast of energy consumption scenarios of the consumption sectors in Colombia, based on the economic factor of the country.
The study implements a multiple regression analysis, together with multi-criteria decision making to establish an integrated methodology and forecast the behavior of future scenarios of energy demand by the final consumption sectors. The transport, commercial, industrial, residential, agriculture, mining and construction sectors were taken as a study.
References
Unidad de Planeación Energética de Colombia UPME (2020). UPME Unidad de Planeación Minero energética. Excel Sheet. http://www1.upme.gov.co/InformacionCifras/Paginas/BalanceEnergetico.aspx
F. de Llano-Paz, A. Calvo-Silvosa, S. I. Antelo, and I. Soares. (2017), “Energy planning and modern portfolio theory: A review,” Renewable and Sustainable Energy Reviews, vol. 77, pp. 636–651.
PROCOLOMBIA (2015), “Electric Power in Colombia,” PROCOLOMBIA, Colombia Energy Sector Outlook Year 2015.
Swan, L. G. (2009),; Ugursal, V. I. Modeling of end use energy consumption in the residential sector: A review of modeling techniques, Renewable and Sustainable Energy Reviews. Vol. 13, Pages 1819-1835.99
Han Shih, Suchithra Rajendran (2019). 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: 10.1155/2019/6123745
Wolfgang Schellong. (2011),. Energy Demand Analysis and Forecast. Energy Management Systems.
C. Congress, “Law 1715 - Integracion de las Energias renovables no coonvencionales al Sistema Energetico Nacional,”
Website GERENCIA (2014), ¿QUÉ ES PROSPECTIVA? Colombia. En Línea: http://www.degerencia.com/articulo/que-es-prospectiva
Soms, Esteban, Guido de la Torre A. (2005). Prospectiva y construccón de escenarios para el Desarrollo territorial. En Línea:
http://www.desarrollosocialyfamilia.gob.cl/btca/txtcompleto/mideplan/cuad3-prospect.desterrit.pdf)
Paez Andrés, et all. (2017). Future scenarios and trends of energy demand in Colombia using long-range energy alternative planning.
J. M. Santos and T. Jimeenz (2016), “Final Agreement to End the Armed Conflict and Build a Stable and Lasting Peace National Government of Colombia | Commander-in-chief FARC-EP.,” Capital of the Republic of Cuba.
ECOPETROL, UPME, UNAB, UIS, and UPB (2018), Prospectiva energética Colombia 2050 - ISBN 978-958-8956-50-3. Bucaramanga: Universidad Industrial de Santander, 2018.
R. Schaeffer et al. (2012), “Energy sector vulnerability to climate change: A review,” Energy, vol. 38, no. 1, pp. 1–12.
P. H. Abreu, D. C. Silva, H. Amaro, and R. Magalhães(2016), “Identification of Residential Energy Consumption Behaviors,” Journal of Energy Engineering, vol. 142, no. 4, p. 04016005.
S. Pfenninger, A. Hawkes, and J. Keirstead (2014), “Energy systems modeling for twenty-first century energy challenges,” Renewable and Sustainable Energy Reviews, vol. 33, pp. 74–86.
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: 896-913
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.2003.12.007
Han Shih, Suchithra Rajendran (2019). 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: 10.1155/2019/6123745
Wolfgang Schellong. (2011). Energy Demand Analysis and Forecast. Energy Management Systems.
Zárate, M. T. N., & Vidal, A. H. (2016, June). Colombia Energy Investment Report. In Presentación Reporte sobre el sector Energético en Colombia-Carta Internacional de la Energía. Energy Charter Secretariat.
XM S.A. E.S.P. Informe de Operaci ́on del SIN y Administración del Mercado 2013, (2013). Retrieved 4 Jun 2020, from http://informesanuales.xm.com.co/2013/ SitePages/operacion/Default.aspx
Burak Omer Saracoglu (2017), Long Term Electricity Demand & Peak Power Load Forecasting Variables Identification & Selection. Science Journal of Circuits, Systems and Signal Processing.2017; 6(2): 18-28. DOI: 10.11648/j.cssp.20170602.13
United Nation ESCAP (1995): Environment and Natural Resources Development Division: Scrotal Energy Demand Analysis and Longterm Forecast: Methodological Manual. MEDEE-S.No: ST/ESCAP/ 1521, 1995.
International Atomic Energy Agency, IAEA (1995). Computer Tools for Comparative Assess- ment of Electricity Generation Options and Strategies. Vienna, Austria. 1995.
Stefano Moret, V ́ıctor Codina Girones, Michel Bierlaire, Francois Mar ́echal (2017). Character- ization of input uncertainties in strategic energy planning models, Applied Energy. Vol. 202, 15 September 2017. Pages 597-617
Saboohi, Y (2006). Model for Analysis of Demand for Energy - MADE II. Institute fur Kernen- ergetik und Energiesysteme (IKE), University of Stuttgart, Technical Report, IKE 8-19, 1989: 0173-6892
Dementjeva, N., & Siirde, A. (2009). Energy planning models analysis and their adaptability for Estonian energy sector. TUT Press.
Ates, S. A. (2015). Energy efficiency and CO2 mitigation potential of the Turkish iron and steel industry using the LEAP (long-range energy alternatives planning) system. Energy, 90, 417-428
Debnath, K. B., & Mourshed, M. (2018). Forecasting methods in energy planning models. Renewable and Sustainable Energy Reviews, 88, 297-325.
Mateus Valencia, Andres Camilo, (2016). Energy Crisis in Colombia.Tecnolog ́ıa, Investigacio ́n y Academia, TIA. ISSN: 2344-8288 Vol. 4 No. 2 pp. 74-81.
Espinoza Sebastian, et all. (2015). Implementació n de Prospectiva Energé tica como Estrategia Prioritaria para la Soberaní a Energé tica y Sostenibilidad Nacional.
Xia, E. & Ahad, M. (2018). Oil demand forecasting for China: fresh evidence from structural time series analysis.
Li, W. & Gao, S. (2018). Prospective on energy related carbon emissions peak integrating optimized intelligent algorithm with dry process technique application for China’s cement industry.
Zengab, M. et all. (2013). The prospective of nuclear power in China.
Fontalvo, J. et all. (2018). ISelf-Generation Prospective in Ecuador using the LEAP Model.
Xie, N. & Alan, P. (2014). Forecasting energy consumption in China following instigation of an energy-saving policy.
Chen, Y. et all. (2019). Impacts of stochastic forecast errors of renewable energy generation and load demands on microgrid operation.
Gülesin, S. (2016). Forecasting the energy demand of Turkey with a NN based on an improved Particle Swarm Optimization.
Roth, A.-N., (2002). Polí ticas pú blicas. Formulació n, Implementació n Y Evaluació n. (Bogotá). http://refhub.elsevier.com/S0301-4215(19)30189-2/sref47
Dincer, I., (1999). Environmental impacts of energy. Energy Policy 27, 845–854. http://refhub.elsevier.com/S0301-4215(19)30189-2/sref16
Official CREG Website. Retrived 5 feb 2021, from https://www.creg.gov.co/sites/default/files/marco_regulatorio_sector_energia.pdf
Official Cámara de Comercio de Cali, Retrived 5 feb 2021, from https://www.ccc.org.co/file/2016/04/Ritmo-Bioenergia-Bioenergia.pdf.
C. Alonso, “Modelo de Regresion lineal Multiple - Econometria Universidad Carlos III de Madrid,” p. 40.
A. C. Rencher and G. B. Schaalje (2008). Linear models in statistics, 2nd ed. Hoboken, N.J: Wiley-Interscience,.
P. Toro, A. Garcia, C. Aguilar, J. Perea, and R. Vera (2010), Modelos Econometricos Para el Desarrollo de Funciones de Produccion - ISSN: 1698-4226 DT 13, Vol. 1/2010, Universidad de Cordoba. .
C. Alonso (2008), “Modelo de Regression lineal Multiple - Econometria Universidad Carlos III de Madrid,” p. 40.
R. M. Granados, “Montero Granados. R (2016): Modelos de regresión lineal múltiple. Documentos de Trabajo en Economía Aplicada. Universidad de Granada. España.,” p. 61.
D. Cardona, M. Rivera, J. González, and E. Cárdenas (2014), “Estimación y predicción con el modelo de regresión cúbica aplicado a un problema de salud,” Ingeniería Solidaria, vol. 10, no. 17, pp. 153–160.
Zachariadis, Theodoros (2007). 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.1233–1253
Bowden, N., Payne, J.E. (2009) The causal relationship between U.S. energy consumption and real output: a disaggregated analysis. J. Policy Model. Vol.;31. No (2009); p.180–188.
Departamento Administrativo Nacional de Estadística. Producto Interno Bruto (PIB) Históricos. {En línea}. 2018. {18 de abril de 2019}. Disponible en: https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-trimestrales/historicos-producto-interno-bruto-pib#base-2005
Otero Prada, Diego Fernando (2019). The energy-mining sector and the Colombian economy. {En línea}. 2012, p.37-38. Disponible en: http://www.indepaz.org.co/wp-content/uploads/2012/04/El-sector-energ%C3%A9tico-minero-y-la-econom%C3%ADa-colombiana.pdf
Comisión de regulación de energía y gas (2007). Resolución CREG 119 de 2007. Costo unitario de prestación del servicio de energía eléctrica. Capitulo III, 21 de diciembre de 2007.
Comisión de regulación de energía y gas (2011.), Resolución CREG 034 de 2001.Precio de Reconciliación Positiva de los Generadores. Artículo 1.
Sterne JAC, Davey Smith G. (2001) Sifting the evidence—what’s wrong with significance tests? BMJ 2001; 322, Pages 226-231
Godet, M. (1993). De la anticipación a la acción, Manual de prospectiva y estrategia. Disponible en: https://administracion.uexternado.edu.co/matdi/clap/De%20la%20anticipaci%C3%B3n%20a%20la%20acci%C3%B3n.pdf
Ministerio de Energía y Minas Oficina Técnica de Energía, (1998) Tabla de conversión de unidades, p 146. http://www.minem.gob.pe/minem/archivos/file/Hidrocarburos/balances/balan-ener-util1998/tabla.pdf
Behera, J. (2015). Examined the energy-led growth hypothesis in India: Evidence from time series analysis. Energy Economics Letters, 2(4), 46-56.
Bruns, S. B., & Gross, C. (2013). What if energy time series are not independent? Implications for energy-GDP causality analysis. Energy Economics, 40, 753-759.
Gross, C. (2012). Explaining the (non-) causality between energy and economic growth in the US—A multivariate sectoral analysis. Energy Economics, 34(2), 489-499.
Jaramillo Villarreal, L. C. (2020). Desarrollo de un modelo económico de energía para pronosticar la demanda energética por sectores de consumo en Colombia. Disponible en: 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=mijQdGx
Schulte, I., & Heindl, P. (2017). Price and income elasticities of residential energy demand in Germany. Energy Policy, 102, 512-528.
EIA, Energy Information Administration, (2019) What drives crude oil prices: Overview. {En línea}. 2019. {18 de abril de 2019}. Disponible en: https://www.eia.gov/finance/markets/crudeoil/
Robert J. Myers a,∗, Stanley R. Johnsonb, Michael Helmar c, Harry Baumes d(2018), Long-run and short-run relationships between oil prices, producer prices, and consumer prices: What can we learn from a permanent-transitory decomposition? - The Quarterly Review of Economics and Finance 67 175–190
Humaira Yasmeen ∗ , Ying Wang, Hashim Zameer, Yasir Ahmed Solangi (2019), Does oil price volatility influence real sector growth? Empirical evidence from Pakistan - --- Energy Reports 5 688–703
Banco de la República, Departamento de Cambios Internacionales. Inversión Extranjera Directa en Colombia. {En línea}. 2012. {1 de mayo de 2019}. Disponible en:http://www.banrep.gov.co/sites/default/files/publicaciones/archivos/ce_dcin_inversionextranjera.pdf.
Chongmei Wang, Chu Jiayu (2019). Analyzing on the Impact Mechanism of Foreign Direct Investment(FDI) to Energy Consumption. En: Energy Procedia. Vol.; 159; p. 515-520
Keeley, A. R., & Ikeda, Y. (2017). Determinants of foreign direct investment in wind energy in developing countries. Journal of Cleaner Production, 161, 1451-1458.
Dane, (2021) comercio-internacional balanza comercial. Disponible en: https://www.dane.gov.co/index.php/estadisticas-por-tema/comercio-internacional/balanza-comercial
Katircioglu, S. T. (2013). Interactions between energy and imports in Singapore: empirical evidence from conditional error correction models. Energy Policy, 63, 514-520.
Fedoseeva, S., & Zeidan, R. (2018). How (a) symmetric is the response of import demand to changes in its determinants? Evidence from European energy imports. Energy Economics, 69, 379-394.
