Optimization of HVAC control system strategy using two-objective genetic algorithm

Intelligent building technology for building operation, called the optimization process, is developed and validated in this paper. The optimization process using a multi-objective genetic algorithm will permit the optimal operation of the building's mechanical systems when installed in parallel with a building's central control system. Using this proposed optimization process, the supervisory control strategy setpoints, such as supply air temperature, supply duct static pressure, chilled water supply temperature, minimum outdoor ventilation, reheat (or zone supply air temperature), and zone air temperatures are optimized with respect to energy use and thermal comfort. HVAC system steady-state models developed and validated against the monitored data of the existing VAV system are used for energy use and thermal comfort calculations. The proposed optimization process is validated on an existing VAV system for two summer months. Many control strategies applied in a multi-zone HVAC system are also tested and evaluated for one summer day.