HVAC system optimization for energy management by evolutionary programming

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)220-231
Journal / PublicationEnergy and Buildings
Issue number3
Publication statusPublished - Mar 2006


Energy management of heating, ventilating and air-conditioning (HVAC) systems is a primary concern in building projects, since the energy consumption in electricity has the highest percentage in HVAC among all building services installations and electric appliances. Without sacrifice of thermal comfort, to reset the suitable operating parameters, such as the chilled water temperature and supply air temperature, would have energy saving with immediate effect. For the typical commercial building projects, it is not difficult to acquire the reference settings for efficient operation. However, for some special projects, due to the specific design and control of the HVAC system, conventional settings may not be necessarily energy-efficient in daily operation. In this paper, the simulation-optimization approach was proposed for the effective energy management of HVAC system. Due to the complicated interrelationship of the entire HVAC system, which commonly includes the water side and air side systems, it is necessary to suggest optimum settings for different operations in response to the dynamic cooling loads and changing weather conditions throughout a year. A metaheuristic simulation-EP (evolutionary programming) coupling approach was developed using evolutionary programming, which can effectively handle the discrete, non-linear and highly constrained optimization problems, such as those related to HVAC systems. The effectiveness of this simulation-EP coupling suite was demonstrated through the establishment of a monthly optimum reset scheme for both the chilled water and supply air temperatures of the HVAC installations of a local project. This reset scheme would have a saving potential of about 7% as compared to the existing operational settings, without any extra cost. © 2005 Elsevier B.V. All rights reserved.

Research Area(s)

  • Energy management, Evolutionary algorithm, Evolutionary programming, HVAC system, Optimization