Vaes, Julien; Charitopoulos, Vassilis M; (2023) A data-driven uncertainty modelling and reduction approach for energy optimisation problems. In: Kokossis, Antonios C and Georgiadis, Michael C and Pistikopoulos, Efstratios, (eds.) Computer Aided Chemical Engineering. (pp. 1161-1167). Elsevier: Amsterdam, The Netherlands.

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Abstract

Taking uncertainty into account is crucial when making strategic decisions. To guard against the risk of adverse scenarios, traditional optimisation techniques incorporate uncertainty based on prior knowledge on its distribution. In this paper, we show how, based on limited historical data, we can generate from a low-dimensional space the underlying structure of uncertainty. To this end, we first exploit the correlation between the sources of uncertainty through a principal component analysis (PCA) 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.

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