Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data

Abstract

It is important to predict extreme electricity demand in power utilities as the uncertainties in the future of electricity demand distribution have to be taken into consideration to achieve the desired goals. The study focused on the prediction of extremely high conditional quantiles (between 0.95 and 0.9999) and extremely low quantiles (between 0.001 and 0.05) of electricity demand using South African data. The paper discusses a comparative analysis of the additive quantile regression model with an extremal mixture model and a nonlinear quantile regression model. The estimated quantiles at each level were then combined using the median approach. The comparisons were carried out using daily peak electricity demand data ranging from January 1997 to May 2014. Proper scoring rules were used to compare the three models, and the model with the smallest score was preferred. The results could be useful to system operators including decision-makers in power utility companies by giving insights and guidance for future electricity demand patterns. The prediction of extremely high quantiles of daily peak electricity demand could help system operators know the possible largest demand that will enable them to supply adequate electricity to consumers and shift demand to off-peak periods. The prediction of extreme conditional quantiles of daily peak electricity demand in the context of South Africa using additive quantile regression, nonlinear quantile regression, and extremal mixture models has not been performed previously to the best of our knowledge.

Prediction of intensity and scale variation trend of tropical cyclones combined the quantile regression with grey power model

“…However, most of the past studies have focused on numerical simulations (Chen et al 2020), with a predictability limit of two weeks (Mingkui et al 2020) and a lack of medium to long-term predictions of intensity and scale, mainly because the intensity and scale of TCs are influenced by various complex factors, such as sea surface temperature, atmospheric circulation, and topography , making predictions extremely complex (Woodruff et al 2013;Korty et al 2015).Some scholars have conducted analytical and revealing studies from a statistical perspective. As a statistical method, quantile regression provided a comprehensive view of the conditional distribution of the dependent variable and has been widely applied in finance, statistics, and other fields (Maswanganyi et al 2021;Monteiro et al 2012). In the climate field, Xiao et al ( 2010) studied the frequency of typhoons that made landfall in China before 2008 using statistical analysis.…”