Chance constrained stochastic MPC for building climate control under combined parametric and additive uncertainty. Issue 3 (4th May 2022)

• Record Type: Journal Article
• Authors: Uytterhoeven, Anke; Van Rompaey, Robbe; Bruninx, Kenneth; Helsen, Lieve
• Is Part Of: Journal of building performance simulation. Volume 15:Issue 3(2022)
• Page Start: 410
• Page End: 430
• Publication Date: 2022-05-04
• Subjects: stochastic model predictive control -- chance constraints -- parametric uncertainty -- additive uncertainty -- building climate control -- thermostatically controlled loads; 690.0113
• DOI: 10.1080/19401493.2022.2058087
• Languages: English
• ISSNs: 1940-1493

Abstract

Abstract : This paper presents a chance constrained stochastic model predictive control (SMPC) approach for building climate control under combined parametric and additive uncertainties. The proposed SMPC ap approach enables the quantification, and manipulation, of both the mean and covariance of the stochastic system states and inputs. Its enhanced uncertainty anticipation is shown to induce improved thermal comfort in closed-loop simulations compared to the conventional deterministic MPC (DMPC) and the state-of-the-art SMPC a only accounting for additive uncertainties, at the cost of a maximum relative increase in energy use of 21.6% and 4.2%, respectively. By incorporating the SMPC ap strategy in an integrated optimal control and design (IOCD) approach, its additional added value for obtaining a more appropriate, yet robust, heat supply system sizing is illustrated. Via simulations, size reductions up to 33.3% are shown to be achievable for a terraced single-family dwelling without increasing thermal discomfort compared to an IOCD approach incorporating DMPC.