Forecasting Uganda’s Net Electricity Consumption Using a Hybrid PSO-ABC Algorithm

Long-term electricity consumption forecasting is used by decision makers to make decisions regarding system expansion planning. Over the past decade, research on electricity consumption forecasting has reported results as point forecasts. Specifically for long-term forecasting, point forecasts are of little interest because it is hard to use them to assess the financial risk associated with system expansion versus demand variability and forecasting uncertainty. In this study, firstly we use power and quadratic forms to model Uganda’s net electricity consumption using population, gross domestic product, number of subscribers and average electricity price as variables in the forecasting models. We optimize the parameters of the forecasting models using a hybrid algorithm based on particle swarm optimization and artificial bee colony algorithms. Secondly we model the forecast residuals using simple linear regression to obtain 90