The behavior of the annual electricity demand and the role of economic growth in Colombia
The electricity demand forecast allows countries to establish long-term plans and objectives for identifying gaps, selecting strategies, and designing the electric power system's architecture. Traditional models use GDP as the primary variable to forecast the electricity demand. The work presen...
- Autores:
-
Grimaldo Guerrero, John William
Candelo Becerra, John Edwin
Balceiro-Alvarez, Bernardo
Cabrera-Anaya, Omar
Silva Ortega, Jorge Iván
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8975
- Acceso en línea:
- https://hdl.handle.net/11323/8975
https://doi.org/10.32479/ijeep.11386
https://repositorio.cuc.edu.co/
- Palabra clave:
- Energy forecasting
Electricity demand
Macroeconomics indicator
Backward
Forward
Stepwise methods
- Rights
- openAccess
- License
- CC0 1.0 Universal
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The behavior of the annual electricity demand and the role of economic growth in Colombia |
| title |
The behavior of the annual electricity demand and the role of economic growth in Colombia |
| spellingShingle |
The behavior of the annual electricity demand and the role of economic growth in Colombia Energy forecasting Electricity demand Macroeconomics indicator Backward Forward Stepwise methods |
| title_short |
The behavior of the annual electricity demand and the role of economic growth in Colombia |
| title_full |
The behavior of the annual electricity demand and the role of economic growth in Colombia |
| title_fullStr |
The behavior of the annual electricity demand and the role of economic growth in Colombia |
| title_full_unstemmed |
The behavior of the annual electricity demand and the role of economic growth in Colombia |
| title_sort |
The behavior of the annual electricity demand and the role of economic growth in Colombia |
| dc.creator.fl_str_mv |
Grimaldo Guerrero, John William Candelo Becerra, John Edwin Balceiro-Alvarez, Bernardo Cabrera-Anaya, Omar Silva Ortega, Jorge Iván |
| dc.contributor.author.spa.fl_str_mv |
Grimaldo Guerrero, John William Candelo Becerra, John Edwin Balceiro-Alvarez, Bernardo Cabrera-Anaya, Omar |
| dc.contributor.author.none.fl_str_mv |
Silva Ortega, Jorge Iván |
| dc.subject.spa.fl_str_mv |
Energy forecasting Electricity demand Macroeconomics indicator Backward Forward Stepwise methods |
| topic |
Energy forecasting Electricity demand Macroeconomics indicator Backward Forward Stepwise methods |
| description |
The electricity demand forecast allows countries to establish long-term plans and objectives for identifying gaps, selecting strategies, and designing the electric power system's architecture. Traditional models use GDP as the primary variable to forecast the electricity demand. The work presents an analysis of the relationship between electricity demand and economic growth, using regression methods with one or more variables. The GDP and sectoral GDP data was provided by Banco de la República de Colombia. The results validate the traditional model and offer alternative models that can relate the economy's different sectors with the electricity demand. |
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2021 |
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2021 |
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2022-01-16T20:33:40Z |
| dc.date.available.none.fl_str_mv |
2022-01-16T20:33:40Z |
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Artículo de revista |
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https://doi.org/10.32479/ijeep.11386 |
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Corporación Universidad de la Costa |
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eng |
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Alfalah, O., Alhumaidan, L., Baglan, D. (2020), The demand for electricity in Kuwait: Acointegration analysis. International Journal of Energy Economics and Policy, 10(6), 9-13. Andrade-Becerra, A., Marenco-Rua, S., Grimaldo-Guerrero, J.W., Noriega-Angarita, E.M., Silva-Ortega, J.I. (2019), Economic assessment of changes in the regulation of the transmission activity in Colombia. Journal of Engineering Science and Technology Review, 12(6), 11-16. BRC. (2021), Producto Interno Bruto (PIB), Banco de la República (Banco Central de Colombia). Available from: https://www.banrep.gov.co/ es/estadisticas/producto-interno-bruto-pib. Carlini, E.M., Schroeder, R., Birkebæk, J.M., Massaro, F. (2019), EU transition in power sector: How RES affects the design and operations of transmission power systems. In Electric Power Systems Research, 169, 74-91. Derksen, S., Keselman, H.J. (1992), Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. British Journal of Mathematical and Statistical Psychology, 45(2), 265-282. Ferguson, R., Wilkinson, W., Hill, R. (2000), Electricity use and economic development. Energy Policy, 28(13), 923-934. Ghalehkhondabi, I., Ardjmand, E., Weckman, G.R., Young, W.A. (2017), An overview of energy demand forecasting methods published in 2005-2015. Energy Systems, 8(2), 411-447. Gironès, V.C., Moret, S., Maréchal, F., Favrat, D. (2015), Strategic energy planning for large-scale energy systems: A modelling framework to aid decision-making. Energy, 90(1), 173-186. Günay, M.E. (2016), Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey. Energy Policy, 90, 92-101. Hanusz, Z., Tarasinska, J., Zielinski, W. (2016), Shapiro-wilk test with known mean. Revstat Statistical Journal, 14(1), 89-100. Ivan Silva, J., Valencia, G., Cardenas Escorcia, Y., Silva-Ortega, J.I., Cervantes-Bolivar, B., Isaac-Millan, I.A., Cardenas-Escorcia, Y., Valencia-Ochoa, G. (2018), Demand energy forecasting using genetic algorithm to guarantee safety on electrical transportation system. Chemical Engineering Transactions, 67, 787-792. Kery, M., Royle, J. (2016), Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS. Vol. 2. Tamil Nadu: Dynamic and Advanced. Ley 1715. (2014), Available from: http://www.secretariasenado.gov.co/ senado/basedoc/ley_1715_2014.html. Li, Y., Jones, B. (2020), The use of extreme value theory for forecasting long-term substation maximum electricity demand. IEEE Transactions on Power Systems, 35(1), 128-139. Lumbreras, S., Ramos, A. (2016), The new challenges to transmission expansion planning. Survey of recent practice and literature review. In Electric Power Systems Research, 134, 19-29. Maaouane, M., Zouggar, S., Krajačić, G., Zahboune, H. (2021), Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods. Energy, 225, 120270. Madrigal, J.A., Eras, J.J.C., Herrera, H.H., Santos, V.S., Morejón, M.B. (2018), Energy planning for fuel oil saving in an industrial laundry. Ingeniare, 26(1), 86-96. Moradijoz, M., Moghaddam, M.P., Haghifam, M.R. (2018), A flexible active distribution system expansion planning model: A risk-based approach. Energy, 145, 442-457. Narisetty, N.N. (2019), Bayesian model selection for high-dimensional data. In: Handbook of Statistics. Amsterdam, Netherlands: Elsevier. Nepal, R., Paija, N. (2019), Energy security, electricity, population and economic growth: The case of a developing South Asian resourcerich economy. Energy Policy, 132, 771-781. Niharika, V.S., Mukherjee, V. (2016), Transmission expansion planning: A review. 2016 International Conference on Energy Efficient Technologies for Sustainability, 2016, 350-355. Oree, V., Sayed Hassen, S.Z., Fleming, P.J. (2017), Generation expansion planning optimisation with renewable energy integration: A review. In Renewable and Sustainable Energy Reviews, 69, 790-803. Paez, A.F., Maldonado, Y.M., Castro, A.O., Hernandez, N., Conde, E., Pacheco, L., Gonzalez, W., Sotelo, O. (2017), Future scenarios and trends of energy demand in Colombia using long-range energy alternative planning. International Journal of Energy Economics and Policy, 7(5), 178-190. R Foundation for Statistical Computing. (2016), RStudio, Open Source and Professional Software for Data Science Teams-RStudio. Available from: https://www.rstudio.com. Rafati, H.H.M., Jalili, M., Davari, H., Maknoon, R. (2016), Prediction of Iran’s Annual Electricity Demand: Artificial Intelligence Approaches. India: Proceedings-2015 11th International Conference on Innovations in Information Technology, IIT. p373-377. Sahraei, M.A., Duman, H., Çodur, M.Y., Eyduran, E. (2021), Prediction of transportation energy demand: Multivariate adaptive regression splines. Energy, 224, 120090. XM. (2021), XM-compañía Expertos en Mercados. Available from: http:// www.xm.com.co/Paginas/Home.aspx. Yuan, M., Lin, Y. (2006), Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(1), 49-67. Zabrodin, E.E., Lokhtin, I.P., Sidorova, A.A., Chernyshov, A.S. (2020), Mechanisms of forward-backward correlations in the multiplicity of particles in ultrarelativistic heavy-ion collisions. Journal of Experimental and Theoretical Physics, 130(5), 660-665. |
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Grimaldo Guerrero, John WilliamCandelo Becerra, John EdwinBalceiro-Alvarez, BernardoCabrera-Anaya, OmarSilva Ortega, Jorge Ivánvirtual::751-12022-01-16T20:33:40Z2022-01-16T20:33:40Z20212146-4553https://hdl.handle.net/11323/8975https://doi.org/10.32479/ijeep.11386Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The electricity demand forecast allows countries to establish long-term plans and objectives for identifying gaps, selecting strategies, and designing the electric power system's architecture. Traditional models use GDP as the primary variable to forecast the electricity demand. The work presents an analysis of the relationship between electricity demand and economic growth, using regression methods with one or more variables. The GDP and sectoral GDP data was provided by Banco de la República de Colombia. The results validate the traditional model and offer alternative models that can relate the economy's different sectors with the electricity demand.Grimaldo Guerrero, John William-will be generated-orcid-0000-0002-1632-5374-600Silva Ortega, Jorge I-will be generated-orcid-0000-0002-7813-0142-600Candelo Becerra, John Edwin-will be generated-orcid-0000-0002-9784-9494-600Balceiro-Alvarez, BernardoCabrera-Anaya, Omarapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2International Journal of Energy Economics and Policyhttps://econjournals.com/index.php/ijeep/article/view/11386Energy forecastingElectricity demandMacroeconomics indicatorBackwardForwardStepwise methodsThe behavior of the annual electricity demand and the role of economic growth in ColombiaArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionAlfalah, O., Alhumaidan, L., Baglan, D. (2020), The demand for electricity in Kuwait: Acointegration analysis. International Journal of Energy Economics and Policy, 10(6), 9-13.Andrade-Becerra, A., Marenco-Rua, S., Grimaldo-Guerrero, J.W., Noriega-Angarita, E.M., Silva-Ortega, J.I. (2019), Economic assessment of changes in the regulation of the transmission activity in Colombia. Journal of Engineering Science and Technology Review, 12(6), 11-16.BRC. (2021), Producto Interno Bruto (PIB), Banco de la República (Banco Central de Colombia). Available from: https://www.banrep.gov.co/ es/estadisticas/producto-interno-bruto-pib.Carlini, E.M., Schroeder, R., Birkebæk, J.M., Massaro, F. (2019), EU transition in power sector: How RES affects the design and operations of transmission power systems. In Electric Power Systems Research, 169, 74-91.Derksen, S., Keselman, H.J. (1992), Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. British Journal of Mathematical and Statistical Psychology, 45(2), 265-282.Ferguson, R., Wilkinson, W., Hill, R. (2000), Electricity use and economic development. Energy Policy, 28(13), 923-934.Ghalehkhondabi, I., Ardjmand, E., Weckman, G.R., Young, W.A. (2017), An overview of energy demand forecasting methods published in 2005-2015. Energy Systems, 8(2), 411-447.Gironès, V.C., Moret, S., Maréchal, F., Favrat, D. (2015), Strategic energy planning for large-scale energy systems: A modelling framework to aid decision-making. Energy, 90(1), 173-186.Günay, M.E. (2016), Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey. Energy Policy, 90, 92-101.Hanusz, Z., Tarasinska, J., Zielinski, W. (2016), Shapiro-wilk test with known mean. Revstat Statistical Journal, 14(1), 89-100.Ivan Silva, J., Valencia, G., Cardenas Escorcia, Y., Silva-Ortega, J.I., Cervantes-Bolivar, B., Isaac-Millan, I.A., Cardenas-Escorcia, Y., Valencia-Ochoa, G. (2018), Demand energy forecasting using genetic algorithm to guarantee safety on electrical transportation system. Chemical Engineering Transactions, 67, 787-792.Kery, M., Royle, J. (2016), Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance and Species Richness in R and BUGS. Vol. 2. Tamil Nadu: Dynamic and Advanced.Ley 1715. (2014), Available from: http://www.secretariasenado.gov.co/ senado/basedoc/ley_1715_2014.html.Li, Y., Jones, B. (2020), The use of extreme value theory for forecasting long-term substation maximum electricity demand. IEEE Transactions on Power Systems, 35(1), 128-139.Lumbreras, S., Ramos, A. (2016), The new challenges to transmission expansion planning. Survey of recent practice and literature review. In Electric Power Systems Research, 134, 19-29.Maaouane, M., Zouggar, S., Krajačić, G., Zahboune, H. (2021), Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods. Energy, 225, 120270.Madrigal, J.A., Eras, J.J.C., Herrera, H.H., Santos, V.S., Morejón, M.B. (2018), Energy planning for fuel oil saving in an industrial laundry. Ingeniare, 26(1), 86-96.Moradijoz, M., Moghaddam, M.P., Haghifam, M.R. (2018), A flexible active distribution system expansion planning model: A risk-based approach. Energy, 145, 442-457.Narisetty, N.N. (2019), Bayesian model selection for high-dimensional data. In: Handbook of Statistics. Amsterdam, Netherlands: Elsevier.Nepal, R., Paija, N. (2019), Energy security, electricity, population and economic growth: The case of a developing South Asian resourcerich economy. Energy Policy, 132, 771-781.Niharika, V.S., Mukherjee, V. (2016), Transmission expansion planning: A review. 2016 International Conference on Energy Efficient Technologies for Sustainability, 2016, 350-355.Oree, V., Sayed Hassen, S.Z., Fleming, P.J. (2017), Generation expansion planning optimisation with renewable energy integration: A review. In Renewable and Sustainable Energy Reviews, 69, 790-803. Paez, A.F., Maldonado, Y.M., Castro, A.O., Hernandez, N., Conde, E.,Pacheco, L., Gonzalez, W., Sotelo, O. (2017), Future scenarios and trends of energy demand in Colombia using long-range energy alternative planning. International Journal of Energy Economics and Policy, 7(5), 178-190.R Foundation for Statistical Computing. (2016), RStudio, Open Source and Professional Software for Data Science Teams-RStudio. Available from: https://www.rstudio.com.Rafati, H.H.M., Jalili, M., Davari, H., Maknoon, R. (2016), Prediction of Iran’s Annual Electricity Demand: Artificial Intelligence Approaches. India: Proceedings-2015 11th International Conference on Innovations in Information Technology, IIT. p373-377.Sahraei, M.A., Duman, H., Çodur, M.Y., Eyduran, E. (2021), Prediction of transportation energy demand: Multivariate adaptive regression splines. Energy, 224, 120090.XM. (2021), XM-compañía Expertos en Mercados. Available from: http:// www.xm.com.co/Paginas/Home.aspx.Yuan, M., Lin, Y. (2006), Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(1), 49-67.Zabrodin, E.E., Lokhtin, I.P., Sidorova, A.A., Chernyshov, A.S. (2020), Mechanisms of forward-backward correlations in the multiplicity of particles in ultrarelativistic heavy-ion collisions. 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