Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1

Imo Enoidem Ebukanson; Chukwu Benedict Chidi; Abode Innocent Iriaoghuan

doi:doi:10.11648/j.ijepe.20170603.12

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Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1

Imo Enoidem Ebukanson, Chukwu Benedict Chidi, Abode Innocent Iriaoghuan

Published in International Journal of Energy and Power Engineering (Volume 6, Issue 3)

Received: 8 January 2017 Accepted: 18 January 2017 Published: 7 June 2017

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Abstract

This study presents statistical analysis of electricity generation in Nigeria using two different statistical models, namely; multiple linear regression model and box-Jenkins’ autoregressive model of order 1. Two climatic variables (rainfall and temperature) were used as the explanatory variables. Data on electricity generation in Nigeria between 2002 and 2014 were obtained from the Central Bank of Nigeria Statistical Bulletin while Data on rainfall and temperature between 2002 and 2014 were extracted from the National Bureau of Statistics (NBS) abstract. Test of model fitness and forecasting accuracy were done using generic statistical approach which include coefficient of determination and root mean square error. The prediction accuracy of the two statistical models was compared and the best model was selected. Furthermore, correlation between power generation and the two climatic variables (rainfall and temperature), were carried out and the result reveals that the amount of rainfall has significant and positive relationship with power generation in Nigeria. Specifically, rainfall has correlation value of r = 0.927 with the power generation at probability, p = 0.000 and the relationship was significant at 1% (p<0.01). However, temperature although it is positively correlated, does not significantly affect power generation. Temperature has correlation value of t = 0.136 with power generation at probability, p = 0.658 (p>0.05) and the relationship was significant at 5% (p<0.05). Among the two statistical models, multiple linear regression model was selected as the best model as it gave the highest value of coefficient of determination (r²=99.77%) and the least Root Mean Square Error (60.27%).

Published in	International Journal of Energy and Power Engineering (Volume 6, Issue 3)
DOI	10.11648/j.ijepe.20170603.12
Page(s)	28-33
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), 2024. Published by Science Publishing Group

Keywords

Electricity, Box-Jenkins’ Autoregressive Model, Electricity Generation, Multiple Linear Regression Model, Statistical Analysis of Electricity

References

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[7]	Ayodele, A. S. (2004). Improving and sustaining power (electricity) supply for socio-economic development in Nigeria.
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[10]	Egboh, H. I. (2011). Clean energy in Norway: a case study for Nigerian electricity development.
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[17]	Bennett, C., Stewart, R. A., & Lu, J. (2014). Autoregressive with exogenous variables and neural network short-term load forecast models for residential low voltage distribution networks. Energies, 7 (5), 2938-2960.
[18]	Singh, A., & Mishra, G. C. (2015). Application of Box-Jenkins method and Artificial Neural Network procedure for time series forecasting of prices. Statistics in Transition new series, 1 (16), 83-96.
[19]	Mohamed, Z., & Bodger, P. S. (2004). Forecasting electricity consumption: A Comparison of models for New Zealand.
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[21]	NBS. (2012). National Bureau of Statistics Abstract Available at: http://www.nigerianstat.gov.ng/pdfuploads/annual_abstract_2012.pdf. Accessed on 12^th November 2016.

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APA Style

Imo Enoidem Ebukanson, Chukwu Benedict Chidi, Abode Innocent Iriaoghuan. (2017). Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1. International Journal of Energy and Power Engineering, 6(3), 28-33. https://doi.org/10.11648/j.ijepe.20170603.12

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ACS Style

Imo Enoidem Ebukanson; Chukwu Benedict Chidi; Abode Innocent Iriaoghuan. Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1. Int. J. Energy Power Eng. 2017, 6(3), 28-33. doi: 10.11648/j.ijepe.20170603.12

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AMA Style

Imo Enoidem Ebukanson, Chukwu Benedict Chidi, Abode Innocent Iriaoghuan. Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1. Int J Energy Power Eng. 2017;6(3):28-33. doi: 10.11648/j.ijepe.20170603.12

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@article{10.11648/j.ijepe.20170603.12,
  author = {Imo Enoidem Ebukanson and Chukwu Benedict Chidi and Abode Innocent Iriaoghuan},
  title = {Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1},
  journal = {International Journal of Energy and Power Engineering},
  volume = {6},
  number = {3},
  pages = {28-33},
  doi = {10.11648/j.ijepe.20170603.12},
  url = {https://doi.org/10.11648/j.ijepe.20170603.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20170603.12},
  abstract = {This study presents statistical analysis of electricity generation in Nigeria using two different statistical models, namely; multiple linear regression model and box-Jenkins’ autoregressive model of order 1. Two climatic variables (rainfall and temperature) were used as the explanatory variables. Data on electricity generation in Nigeria between 2002 and 2014 were obtained from the Central Bank of Nigeria Statistical Bulletin while Data on rainfall and temperature between 2002 and 2014 were extracted from the National Bureau of Statistics (NBS) abstract. Test of model fitness and forecasting accuracy were done using generic statistical approach which include coefficient of determination and root mean square error. The prediction accuracy of the two statistical models was compared and the best model was selected. Furthermore, correlation between power generation and the two climatic variables (rainfall and temperature), were carried out and the result reveals that the amount of rainfall has significant and positive relationship with power generation in Nigeria. Specifically, rainfall has correlation value of r = 0.927 with the power generation at probability, p = 0.000 and the relationship was significant at 1% (p<0.01). However, temperature although it is positively correlated, does not significantly affect power generation. Temperature has correlation value of t = 0.136 with power generation at probability, p = 0.658 (p>0.05) and the relationship was significant at 5% (p<0.05). Among the two statistical models, multiple linear regression model was selected as the best model as it gave the highest value of coefficient of determination (r2=99.77%) and the least Root Mean Square Error (60.27%).},
 year = {2017}
}

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TY  - JOUR
T1  - Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1
AU  - Imo Enoidem Ebukanson
AU  - Chukwu Benedict Chidi
AU  - Abode Innocent Iriaoghuan
Y1  - 2017/06/07
PY  - 2017
N1  - https://doi.org/10.11648/j.ijepe.20170603.12
DO  - 10.11648/j.ijepe.20170603.12
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 28
EP  - 33
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20170603.12
AB  - This study presents statistical analysis of electricity generation in Nigeria using two different statistical models, namely; multiple linear regression model and box-Jenkins’ autoregressive model of order 1. Two climatic variables (rainfall and temperature) were used as the explanatory variables. Data on electricity generation in Nigeria between 2002 and 2014 were obtained from the Central Bank of Nigeria Statistical Bulletin while Data on rainfall and temperature between 2002 and 2014 were extracted from the National Bureau of Statistics (NBS) abstract. Test of model fitness and forecasting accuracy were done using generic statistical approach which include coefficient of determination and root mean square error. The prediction accuracy of the two statistical models was compared and the best model was selected. Furthermore, correlation between power generation and the two climatic variables (rainfall and temperature), were carried out and the result reveals that the amount of rainfall has significant and positive relationship with power generation in Nigeria. Specifically, rainfall has correlation value of r = 0.927 with the power generation at probability, p = 0.000 and the relationship was significant at 1% (p<0.01). However, temperature although it is positively correlated, does not significantly affect power generation. Temperature has correlation value of t = 0.136 with power generation at probability, p = 0.658 (p>0.05) and the relationship was significant at 5% (p<0.05). Among the two statistical models, multiple linear regression model was selected as the best model as it gave the highest value of coefficient of determination (r2=99.77%) and the least Root Mean Square Error (60.27%).
VL  - 6
IS  - 3
ER  -

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Author Information

Imo Enoidem Ebukanson

Department of Electrical/Electronic and Computer Engineering, Faculty of Engineering, University of Uyo, Uyo, Nigeria
Chukwu Benedict Chidi

Department of Electrical/Electronic Engineering Imo State Polytechnic, Umuagwo, Owerri, Nigeria
Abode Innocent Iriaoghuan

Department of Electrical/Electronic Engineering Imo State Polytechnic, Umuagwo, Owerri, Nigeria

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APA Style

Imo Enoidem Ebukanson, Chukwu Benedict Chidi, Abode Innocent Iriaoghuan. (2017). Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1. International Journal of Energy and Power Engineering, 6(3), 28-33. https://doi.org/10.11648/j.ijepe.20170603.12

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ACS Style

Imo Enoidem Ebukanson; Chukwu Benedict Chidi; Abode Innocent Iriaoghuan. Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1. Int. J. Energy Power Eng. 2017, 6(3), 28-33. doi: 10.11648/j.ijepe.20170603.12

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AMA Style

Imo Enoidem Ebukanson, Chukwu Benedict Chidi, Abode Innocent Iriaoghuan. Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1. Int J Energy Power Eng. 2017;6(3):28-33. doi: 10.11648/j.ijepe.20170603.12

Copy | Download

@article{10.11648/j.ijepe.20170603.12,
  author = {Imo Enoidem Ebukanson and Chukwu Benedict Chidi and Abode Innocent Iriaoghuan},
  title = {Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1},
  journal = {International Journal of Energy and Power Engineering},
  volume = {6},
  number = {3},
  pages = {28-33},
  doi = {10.11648/j.ijepe.20170603.12},
  url = {https://doi.org/10.11648/j.ijepe.20170603.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20170603.12},
  abstract = {This study presents statistical analysis of electricity generation in Nigeria using two different statistical models, namely; multiple linear regression model and box-Jenkins’ autoregressive model of order 1. Two climatic variables (rainfall and temperature) were used as the explanatory variables. Data on electricity generation in Nigeria between 2002 and 2014 were obtained from the Central Bank of Nigeria Statistical Bulletin while Data on rainfall and temperature between 2002 and 2014 were extracted from the National Bureau of Statistics (NBS) abstract. Test of model fitness and forecasting accuracy were done using generic statistical approach which include coefficient of determination and root mean square error. The prediction accuracy of the two statistical models was compared and the best model was selected. Furthermore, correlation between power generation and the two climatic variables (rainfall and temperature), were carried out and the result reveals that the amount of rainfall has significant and positive relationship with power generation in Nigeria. Specifically, rainfall has correlation value of r = 0.927 with the power generation at probability, p = 0.000 and the relationship was significant at 1% (p<0.01). However, temperature although it is positively correlated, does not significantly affect power generation. Temperature has correlation value of t = 0.136 with power generation at probability, p = 0.658 (p>0.05) and the relationship was significant at 5% (p<0.05). Among the two statistical models, multiple linear regression model was selected as the best model as it gave the highest value of coefficient of determination (r2=99.77%) and the least Root Mean Square Error (60.27%).},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1
AU  - Imo Enoidem Ebukanson
AU  - Chukwu Benedict Chidi
AU  - Abode Innocent Iriaoghuan
Y1  - 2017/06/07
PY  - 2017
N1  - https://doi.org/10.11648/j.ijepe.20170603.12
DO  - 10.11648/j.ijepe.20170603.12
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 28
EP  - 33
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20170603.12
AB  - This study presents statistical analysis of electricity generation in Nigeria using two different statistical models, namely; multiple linear regression model and box-Jenkins’ autoregressive model of order 1. Two climatic variables (rainfall and temperature) were used as the explanatory variables. Data on electricity generation in Nigeria between 2002 and 2014 were obtained from the Central Bank of Nigeria Statistical Bulletin while Data on rainfall and temperature between 2002 and 2014 were extracted from the National Bureau of Statistics (NBS) abstract. Test of model fitness and forecasting accuracy were done using generic statistical approach which include coefficient of determination and root mean square error. The prediction accuracy of the two statistical models was compared and the best model was selected. Furthermore, correlation between power generation and the two climatic variables (rainfall and temperature), were carried out and the result reveals that the amount of rainfall has significant and positive relationship with power generation in Nigeria. Specifically, rainfall has correlation value of r = 0.927 with the power generation at probability, p = 0.000 and the relationship was significant at 1% (p<0.01). However, temperature although it is positively correlated, does not significantly affect power generation. Temperature has correlation value of t = 0.136 with power generation at probability, p = 0.658 (p>0.05) and the relationship was significant at 5% (p<0.05). Among the two statistical models, multiple linear regression model was selected as the best model as it gave the highest value of coefficient of determination (r2=99.77%) and the least Root Mean Square Error (60.27%).
VL  - 6
IS  - 3
ER  -

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