The large-scale integration of intermittent energy
sources, the introduction of shiftable load elements and the
growing interconnection that characterizes electricity systems
worldwide have led to a significant increase of operational
uncertainty. The construction of suitable statistical models is a
fundamental step towards building Monte Carlo analysis
frameworks to be used for exploring the uncertainty state-space
and supporting real-time decision-making. The main
contribution of the present paper is the development of novel
composite modelling approaches that employ dimensionality
reduction, clustering and parametric modelling techniques with a
particular focus on the use of pair copula construction schemes.
Large power system datasets are modelled using different
combinations of the aforementioned techniques, and detailed
comparisons are drawn on the basis of Kolmogorov-Smirnov
tests, multivariate two-sample energy tests and visual data
comparisons. The proposed methods are shown to be superior to
alternative high-dimensional modelling approaches.