Multi-variable Optimization of HVAC System Using a Genetic Algorithm

Abstract

Geothermal is a fast-growing alternative heat source for HVAC systems due to higher energy efficiency than conventional heating and cooling systems. However, the initial cost of using a ground source HVAC system is higher compared to an air source system. Studies about system design and operation are necessary to reduce the initial cost and ensure that the ground source heat pump system has high efficiency, resulting in a lower total life-time cost. In this study, a multi-variable evolutionary computation algorithm is proposed for generating optimal parameters for a geothermal source HVAC system. The system consists of borehole heat exchangers (BHE), a water-to-water heat pump and a fan coil unit (FCU) and it was modeled and simulated using MATLAB. The design parameters were calculated by minimizing the energy consumption. These parameters include cold water supply temperature, FCU air flow rate and cooling water flow rate for the underground heat exchangers. In addition, based on an experimental building, a case study was presented. Using this model, the optimal set points were calculated and used as a designed system. Energy consumption of this system was reduced by about 10% compared to the system operated with a fixed supply cold water temperature (7°C). Furthermore, the relationship between system energy consumption and heat pump capacity was expressed. The result showed that the energy consumption increased due to decrease in PLR when the capacity is increased.