Paper Conference

Proceedings of Building Simulation 2021: 17th Conference of IBPSA


Data-driven estimation of parametric uncertainty of reduced order RC models for building climate control

Anke Uytterhoeven 1,2, Ina De Jaeger 1,3, Kenneth Bruninx 1,2, Dirk Saelens 1,3, Lieve Helsen 1,2
1 EnergyVille, Genk, Belgium
2 KU Leuven, Mechanical Engineering Department, Leuven, Belgium
3 KU Leuven, Civil Engineering Department, Leuven, Belgium

Abstract: 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.
Keywords: Building climate control, model predictive control (MPC), building model, RC model, parametric uncertainty
Pages: 653 - 660