Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models
American Journal of Software Engineering and Applications
Volume 6, Issue 3, June 2017, Pages: 99-104
Received: Jan. 30, 2017;
Accepted: Mar. 30, 2017;
Published: Jun. 23, 2017
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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
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.
Isaac Amazuilo Ezenugu,
Swinton Chisom Nwokonko,
Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models, American Journal of Software Engineering and Applications.
Vol. 6, No. 3,
2017, pp. 99-104.
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