Multi-objective optimization of HVAC system with an evolutionary computation algorithm

A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables — supply air temperature and supply air duct static pressure set points — are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system.

► A data-mining approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system is presented.
► The data used in the project has been collected from an experiment conducted at an energy research facility.
► The approach presented in the paper leads to accomplishing significant energy savings without compromising the indoor air quality.
► The energy savings are accomplished by computing set points for the supply air temperature and the supply air duct static pressure.