Forecasting Electricity Consumption In Nigeria’s Commercial Sector: A Linear Regression Approach

The increasing level of electricity consumption in the commercial sector of Nigeria’s economy calls for accurate and plain understanding of the key variables dictating the electricity consumption profile. Such information is critical to day-to-day electricity market operations, policy formulation as well as the nation’s energy sector capacity planning initiatives. In this paper, some of the notable variables selected from literature were studied in order to ascertain their present influence on the annual electricity consumption in the commercial sector of Nigeria’s economy. The study initially selected seven independent variables from literature being the most frequently used in previous studies. They are temperature, relative humidity, electricity price, rainfall, gross domestic product; total electricity generated and total primary energy resources available. In the course of model selection, only four out of the seven variables were shown to be significant enough to be included in the final subset of variables needed to produce the most appropriate model. Those four final variables are rainfall, total electricity delivered, total primary energy and population. The model selection exercise was done using R software based on Akaike Information Criterion (AIC). The data are all annual data frame from 1990–2014. Electricity consumptions models are then developed and analysed using multiple linear regression analysis. The model shows high predictive capacity with a root mean squared error (RMSE) of 0.049 and a probability value of 2.2 x 10-16. The average annual growth rate of electricity demand is estimated to be 5.44%.