Forecasting Nigeria's Electricity Demand and Energy Efficiency Potential Under Climate Uncertainty

The increasing population and socio-economic growth of Nigeria, coupled with the current, unmet electricity demand, requires the need for power supply facilities expansion. Of all Nigeria’s electricity consumption by sector, the residential sector is the largest and growing at a very fast rate. To meet this growing demand, an accurate estimation of the demand into the future that will guide policy makers to adequately plan for the expansion of electricity supply and distribution, and energy efficiency standards and labeling must be made. To achieve this, a residential electricity demand forecast model that can correctly predict future demand and guide the construction of power plants including cost optimization of building these power infrastructures is needed.

Modelling electricity demand in developing countries is problematic because of scarcity of data and methodologies that adequately consider detailed disaggregation of household appliances, energy efficiency improvements, and stock uptakes. This dissertation addresses these gaps and presents methodologies that can carry out a detailed disaggregation of household appliances, a more accurate electricity demand projection, peak load reduction, energy savings, economic, and environmental benefits of energy efficiency in the residential sector of Nigeria.

This study adopts a bottom-up and top-down approach (hybrid) supplemented with hourly end-use demand profile to model residential electricity consumption. and project efficiency improvement through the introduction of energy efficiency standards and labelling (EE S&L) under two scenarios (Business As Usual and Best Available Technology). A consumer life-cycle cost analysis was also conducted to determine the cost-effectiveness of introducing EE S& L to consumers.

The results show significant savings in energy and carbon emissions, increased cooling demand due to climate uncertainty, and negative return on investment and increase lifecycle costs to consumers who purchase more efficient appliances. These results are subject to some level of uncertainties that are mainly caused by the input data. The uncertainties were analyzed based on a Monte Carlo Simulation. The uncertainties that were considered including the type of distributions applied to them were outlined and the result of the outputs were presented.