Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models

Isaac Amazuilo Ezenugu; Swinton Chisom Nwokonko; Idorenyin Markson

doi:doi:10.11648/j.ajsea.20170603.17

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Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models

Isaac Amazuilo Ezenugu, Swinton Chisom Nwokonko, Idorenyin Markson

Published in American Journal of Software Engineering and Applications (Volume 6, Issue 3)

Received: 30 January 2017 Accepted: 30 March 2017 Published: 23 June 2017

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Abstract

In this paper statistical analysis of the residential electricity demand in Nigeria is presented Particularly, multiple regression model with one period lagged and quadratic regression model without interactions were used to estimate residential electricity consumption and to forecast long- term residential demand for electricity based on annual data over the period 2006–2014. For the regression models’ explanatory variable, population which is a socio economic variable is used along with temperature which is a climatic variable are used. The results showed that the quadratic regression model without interactions was more accurate due to the fact that it has the highest coefficient of determinant of 93.87 and the least value of Root Mean Square Error (RMSE) of 52.77as compared to the multiple regression model with one period lagged of the dependent variable with coefficient of determinant of 93.50 and RMSE of 53.16. The quadratic regression model was then selected and used to forecast the residential electricity demand in Nigeria for the years 2015 to 2029.

Published in	American Journal of Software Engineering and Applications (Volume 6, Issue 3)
DOI	10.11648/j.ajsea.20170603.17
Page(s)	99-104
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Quadratic Regression Model, Regression Model Without Interactions, Multiple Linear Regression Model, Forecasting, Residential Electricity Demand

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APA Style

Isaac Amazuilo Ezenugu, Swinton Chisom Nwokonko, Idorenyin Markson. (2017). Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models. American Journal of Software Engineering and Applications, 6(3), 99-104. https://doi.org/10.11648/j.ajsea.20170603.17

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ACS Style

Isaac Amazuilo Ezenugu; Swinton Chisom Nwokonko; Idorenyin Markson. Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models. Am. J. Softw. Eng. Appl. 2017, 6(3), 99-104. doi: 10.11648/j.ajsea.20170603.17

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AMA Style

Isaac Amazuilo Ezenugu, Swinton Chisom Nwokonko, Idorenyin Markson. Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models. Am J Softw Eng Appl. 2017;6(3):99-104. doi: 10.11648/j.ajsea.20170603.17

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@article{10.11648/j.ajsea.20170603.17,
  author = {Isaac Amazuilo Ezenugu and Swinton Chisom Nwokonko and Idorenyin Markson},
  title = {Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models},
  journal = {American Journal of Software Engineering and Applications},
  volume = {6},
  number = {3},
  pages = {99-104},
  doi = {10.11648/j.ajsea.20170603.17},
  url = {https://doi.org/10.11648/j.ajsea.20170603.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20170603.17},
  abstract = {In this paper statistical analysis of the residential electricity demand in Nigeria is presented Particularly, multiple regression model with one period lagged and quadratic regression model without interactions were used to estimate residential electricity consumption and to forecast long- term residential demand for electricity based on annual data over the period 2006–2014. For the regression models’ explanatory variable, population which is a socio economic variable is used along with temperature which is a climatic variable are used. The results showed that the quadratic regression model without interactions was more accurate due to the fact that it has the highest coefficient of determinant of 93.87 and the least value of Root Mean Square Error (RMSE) of 52.77as compared to the multiple regression model with one period lagged of the dependent variable with coefficient of determinant of 93.50 and RMSE of 53.16. The quadratic regression model was then selected and used to forecast the residential electricity demand in Nigeria for the years 2015 to 2029.},
 year = {2017}
}

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TY  - JOUR
T1  - Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models
AU  - Isaac Amazuilo Ezenugu
AU  - Swinton Chisom Nwokonko
AU  - Idorenyin Markson
Y1  - 2017/06/23
PY  - 2017
N1  - https://doi.org/10.11648/j.ajsea.20170603.17
DO  - 10.11648/j.ajsea.20170603.17
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 99
EP  - 104
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20170603.17
AB  - In this paper statistical analysis of the residential electricity demand in Nigeria is presented Particularly, multiple regression model with one period lagged and quadratic regression model without interactions were used to estimate residential electricity consumption and to forecast long- term residential demand for electricity based on annual data over the period 2006–2014. For the regression models’ explanatory variable, population which is a socio economic variable is used along with temperature which is a climatic variable are used. The results showed that the quadratic regression model without interactions was more accurate due to the fact that it has the highest coefficient of determinant of 93.87 and the least value of Root Mean Square Error (RMSE) of 52.77as compared to the multiple regression model with one period lagged of the dependent variable with coefficient of determinant of 93.50 and RMSE of 53.16. The quadratic regression model was then selected and used to forecast the residential electricity demand in Nigeria for the years 2015 to 2029.
VL  - 6
IS  - 3
ER  -

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Author Information

Isaac Amazuilo Ezenugu

Department of Electrical/Electronic Engineering, Imo State University, Owerri, Nigeria
Swinton Chisom Nwokonko

Department of Electrical/Electronic Engineering, Imo State University, Owerri, Nigeria
Idorenyin Markson

Department of Mechanical Engineering, University of Uyo, Uyo, Nigeria

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APA Style

Isaac Amazuilo Ezenugu, Swinton Chisom Nwokonko, Idorenyin Markson. (2017). Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models. American Journal of Software Engineering and Applications, 6(3), 99-104. https://doi.org/10.11648/j.ajsea.20170603.17

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ACS Style

Isaac Amazuilo Ezenugu; Swinton Chisom Nwokonko; Idorenyin Markson. Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models. Am. J. Softw. Eng. Appl. 2017, 6(3), 99-104. doi: 10.11648/j.ajsea.20170603.17

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AMA Style

Isaac Amazuilo Ezenugu, Swinton Chisom Nwokonko, Idorenyin Markson. Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models. Am J Softw Eng Appl. 2017;6(3):99-104. doi: 10.11648/j.ajsea.20170603.17

Copy | Download

@article{10.11648/j.ajsea.20170603.17,
  author = {Isaac Amazuilo Ezenugu and Swinton Chisom Nwokonko and Idorenyin Markson},
  title = {Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models},
  journal = {American Journal of Software Engineering and Applications},
  volume = {6},
  number = {3},
  pages = {99-104},
  doi = {10.11648/j.ajsea.20170603.17},
  url = {https://doi.org/10.11648/j.ajsea.20170603.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20170603.17},
  abstract = {In this paper statistical analysis of the residential electricity demand in Nigeria is presented Particularly, multiple regression model with one period lagged and quadratic regression model without interactions were used to estimate residential electricity consumption and to forecast long- term residential demand for electricity based on annual data over the period 2006–2014. For the regression models’ explanatory variable, population which is a socio economic variable is used along with temperature which is a climatic variable are used. The results showed that the quadratic regression model without interactions was more accurate due to the fact that it has the highest coefficient of determinant of 93.87 and the least value of Root Mean Square Error (RMSE) of 52.77as compared to the multiple regression model with one period lagged of the dependent variable with coefficient of determinant of 93.50 and RMSE of 53.16. The quadratic regression model was then selected and used to forecast the residential electricity demand in Nigeria for the years 2015 to 2029.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models
AU  - Isaac Amazuilo Ezenugu
AU  - Swinton Chisom Nwokonko
AU  - Idorenyin Markson
Y1  - 2017/06/23
PY  - 2017
N1  - https://doi.org/10.11648/j.ajsea.20170603.17
DO  - 10.11648/j.ajsea.20170603.17
T2  - American Journal of Software Engineering and Applications
JF  - American Journal of Software Engineering and Applications
JO  - American Journal of Software Engineering and Applications
SP  - 99
EP  - 104
PB  - Science Publishing Group
SN  - 2327-249X
UR  - https://doi.org/10.11648/j.ajsea.20170603.17
AB  - In this paper statistical analysis of the residential electricity demand in Nigeria is presented Particularly, multiple regression model with one period lagged and quadratic regression model without interactions were used to estimate residential electricity consumption and to forecast long- term residential demand for electricity based on annual data over the period 2006–2014. For the regression models’ explanatory variable, population which is a socio economic variable is used along with temperature which is a climatic variable are used. The results showed that the quadratic regression model without interactions was more accurate due to the fact that it has the highest coefficient of determinant of 93.87 and the least value of Root Mean Square Error (RMSE) of 52.77as compared to the multiple regression model with one period lagged of the dependent variable with coefficient of determinant of 93.50 and RMSE of 53.16. The quadratic regression model was then selected and used to forecast the residential electricity demand in Nigeria for the years 2015 to 2029.
VL  - 6
IS  - 3
ER  -

Copy | Download