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A robust pbkp_redictive control strategy for building HVAC systems based on interval fuzzy models

Abstract:

A Robust MPC strategy for Heating, Ventilation and Air Conditioning Systems (HVAC) is proposed in this work. The typical control objective of minimizing energy consumption while maintaining user comfort is considered in this work. Robust MPC is naturally suited for HVAC systems with the aforementioned control goal because it is a control strategy that considers process constraints and the optimization of a performance index, and explicitly handles uncertainty. In this system, the uncertainty comes from the ambient temperature and internal loads pbkp_redictions, which are the main factors driving the thermal dynamics. Thus, effectively pbkp_redicting their future behaviour and uncertainty aids for the quality of the control system. In this context, the main contribution of this work is the introduction of a new framework that uses fuzzy interval models for pbkp_redicting bounds of uncertain variables in a Robust MPC formulation. These bounds are constructed so that the future values of the relevant variables are within them with a predefined probability. In this work, fuzzy interval models are trained and used to pbkp_redict the future system disturbances, and these are in turn used to provide bounds for the pbkp_redictions of room temperatures of the HVAC system. Simulation results show the effectiveness of the proposed strategy, in terms of yielding higher percentage of constraints satisfaction when compared to a classical MPC method. Additionally, it is shown that an appropriate compromise between the system performance and the rate of constraint satisfaction can be achieved by varying the coverage probability of the fuzzy interval models.