Modelling short term probabilistic electricity demand in South Africa

Mokhele, Molete
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Electricity demand in South Africa exhibit some randomness and has some important implications on scheduling of generating capacity and maintenance plans. This work focuses on the development of a short term probabilistic forecasting model for the 19:00 hours daily demand. The model incorporates deterministic influences such as; temperature effects, maximum electricity demand, dummy variables which include the holiday effects, weekly and monthly seasonal effects. A benchmark model is developed and an out-of-sample comparison between the two models is undertaken. The study further assesses the residual demand analysis for risk uncertainty. This information is important to system operators and utility companies to determine the number of critical peak days as well as scheduling load flow analysis and dispatching of electricity in South Africa. Keywords: Semi-parametric additive model, generalized Pareto distribution, extreme value mixture modelling, non stationary time series, electricity demand
Dissertation submitted for Masters of Science degree in Mathematical Statistics in the Faculty of Science, School of Statistics and Actuarial Science, University of the Witwatersrand Johannesburg May 2016