  Taking uncertainty into account is crucial when making strategic decisions.
To guard against the risk of adverse scenarios, traditional optimisation
techniques incorporate uncertainty on the basis of prior knowledge on its
distribution. In this paper, we show how, based on a limited amount of
historical data, we can generate from a low-dimensional space the underlying
structure of uncertainty that could then be used in such optimisation
frameworks. To this end, we first exploit the correlation between the sources
of uncertainty through a principal component analysis to reduce dimensionality.
Next, we perform clustering to reveal the typical uncertainty patterns, and
finally we generate polyhedral uncertainty sets based on a kernel density
estimation (KDE) of marginal probability functions.
