Simulation-based receding-horizon supervisory control of HVAC system

Optimizing the operations of a HVAC system in response to the dynamic loads and varying weather conditions throughout a year can result in substantial energy savings.  However, the problems related to HVAC system optimization are always discrete, non-linear and highly constrained. So, a simulation-based optimization approach for a HVAC system is proposed. To minimize the energy consumption while maintaining the corresponding indoor thermal comfort, a model is developed using TRNSYS to simulate an office building and its HVAC system and using MATLAB genetic algorithm toolbox to solve the optimization problem and search for optimal control settings in a receding-horizon manner. The controllable input variables include supply air temperature and chilled water temperature. The results show that 24.1% energy use can be saved by  optimizing the operation of the  HVAC system.