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Current model predictive control (MPC) applications for residential space heating typically rely upon accurate building models, obtained via extensive data acquisition and/or experts’ knowledge. However, in the context of older residential buildings, one needs to rely upon sparse, publicly available data. Therefore, the aim of this paper is to come up with an estimate of the parametric uncertainty of building controller models in case neither detailed information about the building thermal properties nor experts’ knowledge is available. In addition, the impact of this uncertainty on the optimal space heating strategy is investigated. The results show that the considered approach gives rise to rather large parametric uncertainty. The obtained variation in model parameters is shown to markedly affect the optimal space heating control, both in terms of dynamic effects (i.e., peak demand and timing) and yearly energy use, thereby indicating the need for improved data acquisition and/or dedicated control strategies that operate robustly under uncertainty. ispartof: Proceedings of Building Simulation 2021: 17th International Conference of the International Building Performance Simulation Association ispartof: Building Simulation 2021 Conference location:Bruges date:1 Sep - 3 Sep 2021 status: published |
