Abstract

The air-conditioning systems in buildings, requiring strict space humidity control, are usually very energy intensive, where significant energy would be wasted if the system is not properly designed and controlled. Conventional design method in such buildings usually selects the air-conditioning systems based on certain cooling load and experiences without comprehensively considering the control strategies involved. This paper, therefore, proposes a novel optimal design method to size the air-conditioning systems by quantifying the input uncertainties for cooling load calculation and adopting the “adaptive full-range decoupled ventilation strategy” (ADV strategy). The main objective of the proposed design method is to minimize the life-cycle total cost of air-conditioning systems adopting the alternative decision-making criteria. A hybrid genetic algorithm and particle swarm optimization algorism (GA-PSO) is used for design optimization. Results show that the proposed method can minimize the life-cycle costs of air-conditioning systems and provides promising solutions for designers to make better compromised decisions.

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