Forecasting Uncertainty Intervals for Return Period of Extreme Daily Electricity Consumption

Abstract

The use of extreme value theory (EVT) is usually aimed at quantifying the asymptotic behaviour of extreme quantiles. The generalised Pareto distribution (GPD) with peaks-over-threshold (POT) approach is applied to bootstrap uncertainty intervals for the return periods of extreme daily electricity consumption in South Africa. The leeway of extremes on daily electricity consumption studied here is the impetus behind this study. To examine the effect of a time-based and extreme non-stationary trend in a dataset, a non-stationary GPD is cast-off in computing the shape parameter and, this resulted in the establishment of a type III GPD known as a Weibull class for the South African electricity sector. Results of this study revealed a non-stationary trend with a prediction power of 89.6% for the winter season and 85.65% non-winter season. This means that EVT provides a robust basis for statistical modelling of extreme values. Furthermore, a base for future researchers for conducting studies on emerging markets, more specifically in the South African context has also been contributed.

Keywords

• Bayesian; Extreme Value Theory; Generalised Pareto Distribution; Markov-chain-Monte-Carlo; Peaks-Over-Threshold.