Modelling of Nigerian Residential Electricity Consumption Using Multiple Regression Model with One Period Lagged Dependent Variable

• Main Authors: Runcie Amlabu, Nseobong I. Okpura, Anthony Mfonobong Umoren
• Format: Article
• Language: English
• Published: Varepsilon Ltd. 2017-04-01
• Series: Mathematical and Software Engineering
• Subjects: Multiple Regression; Regression Analysis; Error Analysis; Least Square Method; Sum of Square Error; Forecast; Residential Electricity Demand
• Online Access: http://varepsilon.com/index.php/mse/article/view/40

This paper presents the modelling and forecasting of residential electricity consumption in Nigeria based on nine years (2006 and 2014) data and  multiple regression model with one period lagged dependent variable. A Socio economic parameter (population), and climatic parameter (annual average temperature) are used as explanatory variables in modelling the and  forecasting of residential electricity consumption in Nigeria. The results of the multiple regression analysis applied to the data arrived at the model with the least sum of square error as  E ̂_t= -36.2458+ 9.7202P_t-12.0265T_t+0.1540E_(t-1), where t is the year; E ̂_t   is the predicted residential electricity demand in MW/h;  P_t is the annual population in millions; T_t  is the average annual temperature in °C and  E_(t-1) is the residential electricity demand in the year before year t. The error analysis gave coefficient of determinant of 0.913, adjusted coefficient of determination of 0.86 and Root Mean Square Error of 61.86. The forecast results  gave 5.11% annual average increase in the electric power demand of the residential sector with respect to  the 2014 electricity consumption data. Such results presented in this paper are useful for effective planning of power supply to the residential sector in Nigeria.