期刊名称：International Journal of Energy Economics and Policy
电子版ISSN：2146-4553
出版年度：2022
卷号：12
期号：4
页码：217-225
DOI：10.32479/ijeep.12901
语种：English
出版社：EconJournals
摘要：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.
关键词：Bayesian;Extreme Value Theory;Generalised Pareto Distribution;Markov-chain-Monte-Carlo;Peaks-Over-Threshold
