Özmeteler, M. BatuM. BatuÖzmetelerBilgic, DeborahDeborahBilgicPan, GuanruGuanruPanKoch, AlexanderAlexanderKochFaulwasser, TimmTimmFaulwasser2024-11-282024-11-282024-06-17European Journal of Control 80: 101066 (2024)https://hdl.handle.net/11420/52173Stochastic predictive control schemes that account for epistemic and aleatoric uncertainties, i.e. lack of model knowledge and stochastic disturbances, are of major interest for multi-energy systems. However, there exists a trade-off between model complexity, computational effort, and accuracy of uncertainty quantification. This paper attempts to assess this trade-off by comparing a recently proposed approach combining Willems’ fundamental lemma with polynomial chaos expansion to a model-based scheme that first propagates uncertainty with PCE and then considers chance constraints in the optimization. The simulation results show that the data-driven scheme yields similar performance and computational efficiency compared to the model-based scheme, with the advantage of avoiding the construction of explicit models.en0947-35802024ElsevierData-driven control | Multi-energy systems | Polynomial chaos | Uncertainty propagation | Uncertainty quantification | Willems’ fundamental lemmaTechnology::600: TechnologyJournal Article10.1016/j.ejcon.2024.101066Journal Article