This study presents prediction of electricity generation in Nigeria using two different statistical models, namely; exponential regression and Cobb-Douglas models. 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. The best model was then used to forecast electric power generation in Nigeria for the next fifteen years (2015-2029). Among the two statistical models, Cobb-Douglas model was selected as the best model as it gave the highest value of coefficient of determination (r2=99.85%) and the least Root Mean Square Error (48.57%). Furthermore, the Cobb-Douglas model was used to forecast the electric power generation from 2015 to 2029. The forecasted data shows that power generation in Nigeria in 2029 will stand at 3446.85MWh as against the value of 3249MWh in 2014.
Isaac A. Ezenugu, Swinton C. Nwokonko, and Idorenyin Markson
Mathematical and Software Engineering, Vol 3, Iss 2, Pp 173-182 (2017)
Electricity, Multiple Linear Regression Model, Quadratic Regression Model, Regression Model with Interactions, Statistical Analysis, Forecasting of Residential Electricity Demand, Electronic computers. Computer science, and QA75.5-76.95
In this paper statistical analysis of residential electricity demand in Nigeria is presented. Multiple linear regression model and quadratic regression model with interactions are applied to estimate residential electricity consumption and to forecast long-term residential electricity demand in Nigeria. Population and temperature are used as explanatory variables. The results show the Quadratic Regression with interaction has RMSE of 52.77 and r-square value of 0.9389 which indicates that 93.89% of the variation in residential electricity consumption is explained by the model. On the other hand , the multiple linear regression model has RMSE of 69.97 and r-square value of 0.873 which indicates that 87.3% of the variation in residential electricity consumption is explained by the model. Essentially, the quadratic regression model with interaction with lower RMSE and higher r-square value is selected and then used to forecast the residential electricity demand in Nigeria from 2015 to 2029. From the results, the Residential Electricity Consumption in Nigeria will reach 6521.09 MW/h in the year 2029. Furthermore, the results show that population has a positive sign and it is significant in the short run and in the long run forecasting. On the other hand, the result also revealed insignificant moderately weak relationship between residential electricity consumption and temperature.