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Volume 6, Issue 3, June 2017, Page: 28-33
Statistical Analysis of Electricity Generation in Nigeria Using Multiple Linear Regression Model and Box-Jenkins’ Autoregressive Model of Order 1
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
Received: Jan. 8, 2017;       Accepted: Jan. 18, 2017;       Published: Jun. 7, 2017
DOI: 10.11648/j.ijepe.20170603.12      View  2457      Downloads  145
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%).
Keywords
Electricity, Box-Jenkins’ Autoregressive Model, Electricity Generation, Multiple Linear Regression Model, Statistical Analysis of Electricity
To cite this article
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, International Journal of Energy and Power Engineering. Vol. 6, No. 3, 2017, pp. 28-33. doi: 10.11648/j.ijepe.20170603.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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