Tail Quantile Estimation of Heteroskedastic Intraday Increases in Peak Electricity Demand

Modelling of intraday increases in peak electricity demand using an autoregressive moving average-exponential gener-alized autoregressive conditional heteroskedastic—generalized single Pareto (ARMA-EGARCH-GSP) approach is dis-cussed in this paper. The developed model is then used for extreme tail quantile estimation using daily peak electricity demand data from South Africa for the period, years 2000 to 2011. The advantage of this modelling approach lies in its ability to capture conditional heteroskedasticity in the data through the EGARCH framework, while at the same time estimating the extreme tail quantiles through the GSP modelling framework. Empirical results show that the ARMA-EGARCH-GSP model produces more accurate estimates of extreme tails than a pure ARMA-EGARCH model