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:
-
Garcia-Guiliany, Jesús
De-la-hoz-Franco, Emiro
Rodríguez-Toscano, Andrés-David
De-la-Hoz-Hernández, Juan-David
Hernandez-Palma, Hugo G.
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/4773
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/4773
http://www.econjournals.com/index.php/ijeep/article/view/7813/4806
- Palabra clave:
- Energy consumption
Electric demand
Multiple linear regression model
- Rights
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
| id |
USIMONBOL2_4714ab6b537cfc74d970b506bf1521b2 |
|---|---|
| oai_identifier_str |
oai:bonga.unisimon.edu.co:20.500.12442/4773 |
| network_acronym_str |
USIMONBOL2 |
| network_name_str |
Repositorio Digital USB |
| repository_id_str |
|
| dc.title.eng.fl_str_mv |
Multiple linear regression model applied to the projection of electricity demand in 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 |
| 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 |
Garcia-Guiliany, Jesús De-la-hoz-Franco, Emiro Rodríguez-Toscano, Andrés-David De-la-Hoz-Hernández, Juan-David Hernandez-Palma, Hugo G. |
| dc.contributor.author.none.fl_str_mv |
Garcia-Guiliany, Jesús De-la-hoz-Franco, Emiro Rodríguez-Toscano, Andrés-David De-la-Hoz-Hernández, Juan-David Hernandez-Palma, Hugo G. |
| dc.subject.eng.fl_str_mv |
Energy consumption Electric demand Multiple linear regression model |
| topic |
Energy consumption Electric demand Multiple linear regression model |
| 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 |
2020 |
| dc.date.accessioned.none.fl_str_mv |
2020-02-20T20:03:39Z |
| dc.date.available.none.fl_str_mv |
2020-02-20T20:03:39Z |
| dc.date.issued.none.fl_str_mv |
2020 |
| dc.type.eng.fl_str_mv |
article |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.driver.eng.fl_str_mv |
article |
| dc.identifier.issn.none.fl_str_mv |
21464553 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12442/4773 |
| dc.identifier.url.none.fl_str_mv |
http://www.econjournals.com/index.php/ijeep/article/view/7813/4806 |
| identifier_str_mv |
21464553 |
| url |
https://hdl.handle.net/20.500.12442/4773 http://www.econjournals.com/index.php/ijeep/article/view/7813/4806 |
| dc.language.iso.eng.fl_str_mv |
eng |
| language |
eng |
| dc.rights.none.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.uri.none.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
| dc.format.mimetype.spa.fl_str_mv |
pdf |
| dc.publisher.eng.fl_str_mv |
EconJournals |
| dc.source.eng.fl_str_mv |
International Journal of Energy Economics and Policy |
| dc.source.none.fl_str_mv |
Vol. 10 N° 1, (2020) |
| institution |
Universidad Simón Bolívar |
| dc.source.uri.none.fl_str_mv |
http://www.econjournals.com/index.php/ijeep/article/view/7813/4806 |
| bitstream.url.fl_str_mv |
https://bonga.unisimon.edu.co/bitstreams/7d31735b-d30f-4431-8971-bd697419d147/download https://bonga.unisimon.edu.co/bitstreams/06f2e667-a537-4954-82f9-e33b410bccf5/download https://bonga.unisimon.edu.co/bitstreams/5c9f790f-2dac-40d8-9a82-71ace1ad65dc/download https://bonga.unisimon.edu.co/bitstreams/fb3dec90-6b06-4385-8e68-267aa1696f64/download https://bonga.unisimon.edu.co/bitstreams/1d6c0b82-3d62-4f45-92b4-5466ec6c2d82/download https://bonga.unisimon.edu.co/bitstreams/9618ae17-9814-4123-9227-f3bd7e2f647a/download https://bonga.unisimon.edu.co/bitstreams/c6b591aa-bc08-4360-b7fe-25352422b563/download |
| bitstream.checksum.fl_str_mv |
4460e5956bc1d1639be9ae6146a50347 733bec43a0bf5ade4d97db708e29b185 5b36a47370607bf074d6e9ec3dc5fe92 32a28540c859543dca2dcfe63a5bee81 4372c285c64c823e0142d63901a8d8c3 85c68a3cdd97b5291bfc0fb8db17cf21 8b9cd7956a59b50e063955a78564271a |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Digital Universidad Simón Bolívar |
| repository.mail.fl_str_mv |
repositorio.digital@unisimon.edu.co |
| _version_ |
1814076093578084352 |
| spelling |
Garcia-Guiliany, Jesús4e1d5638-e376-463e-86e4-f1fbff0b8f60De-la-hoz-Franco, Emiro494bbd4a-f88a-4706-92c7-7842c0418615Rodríguez-Toscano, Andrés-David4a44eed1-a787-4334-a071-01eb2e3b7953De-la-Hoz-Hernández, Juan-Davidd7115784-fb3b-4bd5-bcd3-77178ca162b6Hernandez-Palma, Hugo G.f491a014-123c-4f76-85ba-2cddc5f6f38c2020-02-20T20:03:39Z2020-02-20T20:03:39Z202021464553https://hdl.handle.net/20.500.12442/4773http://www.econjournals.com/index.php/ijeep/article/view/7813/4806The 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.pdfengEconJournalsAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2International Journal of Energy Economics and PolicyVol. 10 N° 1, (2020)http://www.econjournals.com/index.php/ijeep/article/view/7813/4806Energy consumptionElectric demandMultiple linear regression modelMultiple linear regression model applied to the projection of electricity demand in Colombiaarticlearticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Andrews-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.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.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.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.Banco Mundial. (2017), Sección Indicadores. Available from: https:// www.datos.bancomundial.org/indicador.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.Palma, H.H. (2017), Direccionamiento estratégico para la dinamización del sector salud en el departamento del Atlántico. BIOCIENCIAS, 12(1), 79-84.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.CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/7d31735b-d30f-4431-8971-bd697419d147/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/06f2e667-a537-4954-82f9-e33b410bccf5/download733bec43a0bf5ade4d97db708e29b185MD53ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf383142https://bonga.unisimon.edu.co/bitstreams/5c9f790f-2dac-40d8-9a82-71ace1ad65dc/download5b36a47370607bf074d6e9ec3dc5fe92MD51TEXTMultiplinearregressimodel.pdf.txtMultiplinearregressimodel.pdf.txtExtracted texttext/plain17899https://bonga.unisimon.edu.co/bitstreams/fb3dec90-6b06-4385-8e68-267aa1696f64/download32a28540c859543dca2dcfe63a5bee81MD54PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain18069https://bonga.unisimon.edu.co/bitstreams/1d6c0b82-3d62-4f45-92b4-5466ec6c2d82/download4372c285c64c823e0142d63901a8d8c3MD56THUMBNAILMultiplinearregressimodel.pdf.jpgMultiplinearregressimodel.pdf.jpgGenerated Thumbnailimage/jpeg1880https://bonga.unisimon.edu.co/bitstreams/9618ae17-9814-4123-9227-f3bd7e2f647a/download85c68a3cdd97b5291bfc0fb8db17cf21MD55PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg6305https://bonga.unisimon.edu.co/bitstreams/c6b591aa-bc08-4360-b7fe-25352422b563/download8b9cd7956a59b50e063955a78564271aMD5720.500.12442/4773oai:bonga.unisimon.edu.co:20.500.12442/47732024-08-14 21:51:55.136http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalopen.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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 |