Data-driven chance-constrained optimization based on Gaussian Mixture Models

Alberto Corredera Barbado, Carlos Ruiz Mora and Francisco Javier Prieto Fernández

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: In this work, we present a novel approach based on the combination of Gaussian Mixture Models (GMM) and Chance-Constrained Optimization (CCO). The method proposed deals with the often difficult task of deriving exact estimates of the individual constraint quantiles in the presence of uncertainty for some or all of the optimization problem parameters. Although COO solution methods have been extensively studied when uncertainty is assumed to be normally distributed, only approximate solutions are considered when the uncertainty is not normal. We propose a reformulation of the COO problem that provides exact and tractable solutions. The performance of the method has been studied under different GMM parameterizations in simulated and real-world applications.

Keywords: OR; in; energy; Data-driven; Chance-constrained; optimization; Quantile; estimation; Gaussian; Mixture (search for similar items in EconPapers)
Date: 2025-03-17
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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:46291

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