Abstract

A model predictive control strategy (MPC) based on fuzzy optimization is proposed in this work for a multi-room heating, ventilation and air conditioning (HVAC) system. The proposed strategy aims to minimize energy consumption, while requiring different thermal conditions for each room. For this system, the combination of MPC and fuzzy optimization arises as a suitable control strategy, due to the benefits given by the use of fuzzy constraints for the managment of thermal comfort.The soft constraint scheme provided by the fuzzy optimization allows to reduce the power consumption of HVAC systems. This is achieved by allowing some constraint violations in specific cases where the proper operation of the system is not compromised. In this context, the main contribution of this work is the introduction of a new framework where the thermal requirements of several rooms can be managed by fuzzy constraints, which are handled as additional terms in the objective function of the MPC. The optimization problem of the MPC is nonlinear, and it is solved with a suitable particle swarm optimization (PSO) method. Simulations results show the effectiveness of the proposed controller to reduce the energy consumption compared with a classical MPC implementation, while maintaining constraint satisfaction in appropriate levels.