Multiple linear regression model applied to the projection of electricity demand in Colombia
The exigencies as soon as to competitiveness and productivity have influenced in the energetic consumption and the demand of electrical energy in Colombia, reason why at the present time it is of much interest and utility to have access to tools or valid models to reach greater knowledge in which re...
- Autores:
-
García Guiliany, Jesús Enrique
De-La-Hoz-Franco, Emiro
Rodríguez Toscano, Andrés David
De la Hoz Hernández, Juan David
Hernández-Palma, Hugo G.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/6095
- Acceso en línea:
- https://hdl.handle.net/11323/6095
https://doi.org/10.32479/ijeep.7813
https://repositorio.cuc.edu.co/
- Palabra clave:
- Energy consumption
Electric demand
Multiple linear regression model
Consumo de energía
Demanda eléctrica
Modelo de regresión lineal múltiple
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
Multiple linear regression model applied to the projection of electricity demand in Colombia |
| dc.title.translated.spa.fl_str_mv |
Modelo de regresión lineal múltiple aplicado a la proyección de la demanda eléctrica en Colombia |
| title |
Multiple linear regression model applied to the projection of electricity demand in Colombia |
| spellingShingle |
Multiple linear regression model applied to the projection of electricity demand in Colombia Energy consumption Electric demand Multiple linear regression model Consumo de energía Demanda eléctrica Modelo de regresión lineal múltiple |
| title_short |
Multiple linear regression model applied to the projection of electricity demand in Colombia |
| title_full |
Multiple linear regression model applied to the projection of electricity demand in Colombia |
| title_fullStr |
Multiple linear regression model applied to the projection of electricity demand in Colombia |
| title_full_unstemmed |
Multiple linear regression model applied to the projection of electricity demand in Colombia |
| title_sort |
Multiple linear regression model applied to the projection of electricity demand in Colombia |
| dc.creator.fl_str_mv |
García Guiliany, Jesús Enrique De-La-Hoz-Franco, Emiro Rodríguez Toscano, Andrés David De la Hoz Hernández, Juan David Hernández-Palma, Hugo G. |
| dc.contributor.author.spa.fl_str_mv |
García Guiliany, Jesús Enrique De-La-Hoz-Franco, Emiro Rodríguez Toscano, Andrés David De la Hoz Hernández, Juan David Hernández-Palma, Hugo G. |
| dc.subject.spa.fl_str_mv |
Energy consumption Electric demand Multiple linear regression model Consumo de energía Demanda eléctrica Modelo de regresión lineal múltiple |
| topic |
Energy consumption Electric demand Multiple linear regression model Consumo de energía Demanda eléctrica Modelo de regresión lineal múltiple |
| description |
The exigencies as soon as to competitiveness and productivity have influenced in the energetic consumption and the demand of electrical energy in Colombia, reason why at the present time it is of much interest and utility to have access to tools or valid models to reach greater knowledge in which related to the possible future projections. Next, the results of a quantitative study are presented that through the analysis of data collected between 2007 and 2017 that made possible the construction of a multiple linear regression model to estimate the demand of electric energy. These types of instruments currently originate as alternatives to promote management strategies in the energy field in the country. The final results allow to visualize an estimated figure for the next periods which will serve to contrast with the official results and to generate from this information possible lines of intervention in different organisms. |
| publishDate |
2019 |
| dc.date.issued.none.fl_str_mv |
2019-10-10 |
| dc.date.accessioned.none.fl_str_mv |
2020-03-11T12:58:02Z |
| dc.date.available.none.fl_str_mv |
2020-03-11T12:58:02Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/acceptedVersion |
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2146-4553 |
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https://hdl.handle.net/11323/6095 |
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https://doi.org/10.32479/ijeep.7813 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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2146-4553 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/6095 https://doi.org/10.32479/ijeep.7813 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
Andrews-Speed, P., Liao, X., Dannreuther, R. (2014), The Strategic Implications of China’s Energy Needs. London: Routledge. Ardila, L.M.C., Cardona, C.J.F. (2017), Structure and current state of the wholesale electricity markets. IEEE Latin America Transactions, 15(4), 669-674. Banco Mundial. (2017), Sección Indicadores. Available from: https:// www.datos.bancomundial.org/indicador. Fabra, N., Reguant, M. (2014), Pass-through of emissions costs in electricity markets. American Economic Review, 104(9), 2872-2899. Government Publications Office. editor. (GPO). (2016), International Energy Outlook 2016: With Projections to 2040. Government Printing Office. Holmberg, K., Erdemir, A. (2017), Influence of tribology on global energy consumption, costs and emissions. Friction, 5(3), 263-284. Informe de Operación del Sistema Interconectado Nacional (SIN). (2017), Demanda de Energía Nacional. Available from: http:// www.informesanuales.xm.com.co/2017/SitePages/operacion/4-1- Demanda-de-energia-nacional.aspx. Kaytez, F., Taplamacioglu, M.C., Cam, E., Hardalac, F. (2015), Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power and Energy Systems, 67, 431-438 Montgomery, D., Peck, E.A., Vining, G. (2012), Introduction to Linear Regression Analysis. Vol. 821. New Jersey: John Wiley and Sons. Nejat, P., Jomehzadeh, F., Taheri, M.M., Gohari, M., Majid, M.Z.A. (2015), A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries). Renewable and Sustainable Energy Reviews, 43, 843-862 Ñustes, W., Riviera, S. (2017), Colombia: territorio de inversión en fuentes no convencionales de energía renovable para la generación eléctrica. Revista Ingeniería, Investigación y Desarrollo, 17, 37-48. Palma, H.H. (2017), Direccionamiento estratégico para la dinamización del sector salud en el departamento del Atlántico. BIOCIENCIAS, 12(1), 79-84. Pukšec, T., Mathiesen, B.V., Novosel, T., Duić, N. (2014), Assessing the impact of energy saving measures on the future energy demand and related GHG (greenhouse gas) emission reduction of Croatia. Energy, 76, 198-209. Sánchez-Villegas, A. (2014), In: Martínez-González, M.A., Faulín, F.J., editors. Bioestadística Amigable. Barcelona: Elsevier. Stephanidis, C. editor. (2018), HCI International 2018 Posters’ Extended Abstracts: 20th International Conference. Vol. 852. HCI International 2018, Las Vegas, NV, USA, Proceedings. Springer. Unidad de Planeación Minera y Energética (UPME). (2015), Plan Energetico Nacional Colombia: Ideario Energético 2050. Available from: http://www1.upme.gov.co/Documents/PEN_ IdearioEnergetico2050.pdf |
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International Journal of Energy Economics and Policy |
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García Guiliany, Jesús EnriqueDe-La-Hoz-Franco, EmiroRodríguez Toscano, Andrés DavidDe la Hoz Hernández, Juan DavidHernández-Palma, Hugo G.2020-03-11T12:58:02Z2020-03-11T12:58:02Z2019-10-102146-4553https://hdl.handle.net/11323/6095https://doi.org/10.32479/ijeep.7813Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The exigencies as soon as to competitiveness and productivity have influenced in the energetic consumption and the demand of electrical energy in Colombia, reason why at the present time it is of much interest and utility to have access to tools or valid models to reach greater knowledge in which related to the possible future projections. Next, the results of a quantitative study are presented that through the analysis of data collected between 2007 and 2017 that made possible the construction of a multiple linear regression model to estimate the demand of electric energy. These types of instruments currently originate as alternatives to promote management strategies in the energy field in the country. The final results allow to visualize an estimated figure for the next periods which will serve to contrast with the official results and to generate from this information possible lines of intervention in different organisms.Las exigencias en cuanto a competitividad y productividad han influido en el consumo energético y la demanda de energía eléctrica en Colombia, por lo que en la actualidad es de mucho interés y utilidad tener acceso a herramientas o modelos válidos para alcanzar un mayor conocimiento en lo relacionado con Las posibles proyecciones futuras. A continuación, se presentan los resultados de un estudio cuantitativo que a través del análisis de los datos recopilados entre 2007 y 2017 eso hizo posible la construcción de un modelo de regresión lineal múltiple para estimar la demanda de energía eléctrica. Este tipo de instrumentos actualmente se originan como alternativas para promover estrategias de gestión en el campo de la energía en el país. Los resultados finales permiten visualizar una cifra estimada para el Próximos períodos que servirán para contrastar con los resultados oficiales y generar a partir de esta información posibles líneas de intervención en diferentes organismos.García Guiliany, Jesús Enrique-will be generated-orcid-0000-0003-3777-3667-600De-La-Hoz-Franco, Emiro-will be generated-orcid-0000-0002-4926-7414-600Rodríguez Toscano, Andrés DavidDe la Hoz Hernández, Juan David-will be generated-orcid-0000-0002-4025-8538-600Hernández-Palma, Hugo G.engInternational Journal of Energy Economics and PolicyCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Energy consumptionElectric demandMultiple linear regression modelConsumo de energíaDemanda eléctricaModelo de regresión lineal múltipleMultiple linear regression model applied to the projection of electricity demand in ColombiaModelo de regresión lineal múltiple aplicado a la proyección de la demanda eléctrica en 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/acceptedVersionAndrews-Speed, P., Liao, X., Dannreuther, R. (2014), The Strategic Implications of China’s Energy Needs. London: Routledge.Ardila, L.M.C., Cardona, C.J.F. (2017), Structure and current state of the wholesale electricity markets. IEEE Latin America Transactions, 15(4), 669-674.Banco Mundial. (2017), Sección Indicadores. Available from: https:// www.datos.bancomundial.org/indicador.Fabra, N., Reguant, M. (2014), Pass-through of emissions costs in electricity markets. American Economic Review, 104(9), 2872-2899.Government Publications Office. editor. (GPO). (2016), International Energy Outlook 2016: With Projections to 2040. Government Printing Office.Holmberg, K., Erdemir, A. (2017), Influence of tribology on global energy consumption, costs and emissions. Friction, 5(3), 263-284.Informe de Operación del Sistema Interconectado Nacional (SIN). (2017), Demanda de Energía Nacional. Available from: http:// www.informesanuales.xm.com.co/2017/SitePages/operacion/4-1- Demanda-de-energia-nacional.aspx.Kaytez, F., Taplamacioglu, M.C., Cam, E., Hardalac, F. (2015), Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power and Energy Systems, 67, 431-438Montgomery, D., Peck, E.A., Vining, G. (2012), Introduction to Linear Regression Analysis. Vol. 821. New Jersey: John Wiley and Sons.Nejat, P., Jomehzadeh, F., Taheri, M.M., Gohari, M., Majid, M.Z.A. (2015), A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries). Renewable and Sustainable Energy Reviews, 43, 843-862Ñustes, W., Riviera, S. (2017), Colombia: territorio de inversión en fuentes no convencionales de energía renovable para la generación eléctrica. Revista Ingeniería, Investigación y Desarrollo, 17, 37-48.Palma, H.H. (2017), Direccionamiento estratégico para la dinamización del sector salud en el departamento del Atlántico. BIOCIENCIAS, 12(1), 79-84.Pukšec, T., Mathiesen, B.V., Novosel, T., Duić, N. (2014), Assessing the impact of energy saving measures on the future energy demand and related GHG (greenhouse gas) emission reduction of Croatia. Energy, 76, 198-209.Sánchez-Villegas, A. (2014), In: Martínez-González, M.A., Faulín, F.J., editors. Bioestadística Amigable. Barcelona: Elsevier.Stephanidis, C. editor. (2018), HCI International 2018 Posters’ Extended Abstracts: 20th International Conference. Vol. 852. HCI International 2018, Las Vegas, NV, USA, Proceedings. Springer.Unidad de Planeación Minera y Energética (UPME). (2015), Plan Energetico Nacional Colombia: Ideario Energético 2050. 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