Abstract:
Generally, short term load forecasting is essential for any power generating
utility. In this dissertation the main objective was to develop short term
load forecasting models for the peak demand periods (i.e. from 18:00 to
20:00 hours) in South Africa using. Quantile semi-parametric additive models
were proposed and used to forecast electricity demand during peak hours.
In addition to this, forecasts obtained were then used to nd an optimal
number of generating units to commit (switch on or o ) daily in order to
produce the required electricity demand at minimal costs. A mixed integer
linear programming technique was used to nd an optimal number of units
to commit. Driving factors such as calendar e ects, temperature, etc. were
used as predictors in building these models. Variable selection was done
using the least absolute shrinkage and selection operator (Lasso). A feasible
solution to the unit commitment problem will help utilities meet the demand
at minimal costs. This information will be helpful to South Africa's national
power utility, Eskom.
