Evolutionary algorithms for multi-objective optimization in HVAC system control strategy

The supervisory control strategy set points for an existing HVAC system could be optimized using a two-objective evolutionary algorithm. The set points for the supply air temperature, the supply duct static pressure, the chilled water temperature, and the zone temperatures are the problem variables, while energy use and thermal comfort are the objective functions. Different evolutionary algorithm methods for two-objective optimization in HVAC systems are evaluated. It was concluded that controlled elitist non-dominated sorting genetic algorithms offer great potential for finding the Pareto-optimal solutions of investigated problems. The results also showed that the on-line implementation of optimization process could save energy by 19.5%. The two-objective optimization could also help control daily energy use while bringing about further energy use savings as compared to a one-objective optimization.