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...

Full description

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
Description
Summary: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.