Abstract:
Energy demand forecasting is a vital tool for energy management, maintenance
planning, environmental security, and investment decision-making in liberalised
energy markets. The mini-dissertation investigates ways to anticipate power usage
using hierarchical time series and South African data. Approaches such as topdown,
bottom-up, and optimal combination are applied. Top-down forecasting is
based on disaggregating total series projections and spreading them down the hierarchy
based on historical data proportions. The bottom-up strategy aggregates
individual projections at lower levels, whereas the optimal combination methodology
optimally combines bottom forecasts. An out-of-sample prediction performance
evaluation was performed to assess the models’ predicting ability. The best
model was chosen using mean absolute percentage error. The top-down technique
based on predicted proportions (Top-down forecasted proportions) was superior to
the optimal combination and bottom-up approach. To integrate forecasts and build
prediction ranges for the proposed models, linear quantile regression, linear regression,
simple average, and median were used. The best set of forecasts was picked
based on the prediction interval normalised average width. At 95%, the best model
based on the prediction interval normalised average width was a simple average.