Archive
Special Issues
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
Volume 6, Issue 3, June 2017, Pages: 28-33
Received: Jan. 8, 2017; Accepted: Jan. 18, 2017; Published: Jun. 7, 2017
Authors
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
Article Tools
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
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
References
[1]
Wara, S. T. (2012). Electricity Provision And Manageme1 In Nigeria: Challenges And Prospects.
[2]
Oji, J. O., Idusuyi, N., & Kareem, B. (2012). Coal power utilization as an energy mix option for Nigeria: a review. American Academic & Scholarly Research Journal, 4 (4), 1.
[3]
Anumaka, M. C. (2012). Scenario of Electricity in Nigeria. International Journal of Engineering and Innovative Technology (IJEIT), 1 (6), 176-183.
[4]
Anumaka, M. C. (2012). Scenario of Electricity in Nigeria. International Journal of Engineering and Innovative Technology (IJEIT), 1 (6), 176-183.
[5]
Madueme, T. I. (2002). Analysis of Electricity Load Demand in Nigeria. Nigerian Journal of Engineering Management. 3 (2): 76.
[6]
Ogumodede O. B. (2005). Consumers’ Expectations on Service Delivery of PHCN: A Study of Lagos and Ibadan Metropolis. Unpublished MBA Thesis, Imo State University.
[7]
Ayodele, A. S. (2004). Improving and sustaining power (electricity) supply for socio-economic development in Nigeria.
[8]
Lionel, E. (2013). The dynamic analysis of electricity supply and economic development: Lessons from Nigeria. Journal of Sustainable Society, 2 (1), 1-11.
[9]
Wara, S. T., Abayomi-Alli, A., Umo, N. D., Oghogho, I., & Odikayor, C. (2009). An impact assessment of the Nigerian power sector reforms. In Advanced Materials Research (Vol. 62, pp. 147-152). Trans Tech Publications.
[10]
Egboh, H. I. (2011). Clean energy in Norway: a case study for Nigerian electricity development.
[11]
Iwayemi, A. (2008). Investment in electricity generation and transmission in Nigeria: issues and options. International Association for Energy Economics, 37-42.
[12]
Hinman, J., & Hickey, E. (2009). Modeling and forecasting short-term electricity load using regression analysis. Journal of IInstitute for Regulatory Policy Studies [электронный ресурс].
[13]
Cho, H., Goude, Y., Brossat, X., & Yao, Q. (2013). Modeling and forecasting daily electricity load curves: a hybrid approach. Journal of the American Statistical Association, 108 (501), 7-21.
[14]
Safa, M., Allen, J., & Safa, M. (2014, January). Predicting Energy Usage Using Historical Data and Linear Models. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 31, p. 1). Vilnius Gediminas Technical University, Department of Construction Economics & Property.
[15]
Adhikari, R., & Agrawal, R. K. (2013). An introductory study on time series modeling and forecasting. arXiv preprint arXiv: 1302.6613.
[16]
Roken, R. M., & Badri, M. A. (2006). Time Series Models for Forecasting Monthly Electricity Peak Load for Dubai. Chancellor's Undergraduate Research Award.
[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.
[20]
CBN. (2006). Central Bank of Nigeria, Statistical Bulletin, Vol. 17.
[21]
NBS. (2012). National Bureau of Statistics Abstract Available at: http://www.nigerianstat.gov.ng/pdfuploads/annual_abstract_2012.pdf. Accessed on 12th November 2016.
PUBLICATION SERVICES