Automatic Simualtion Method in HVAC&R System (Primary)
My Ph.D. research developed an automatic simulation method by machine learning that predicts the performance and energy consumption of HVAC&R systems. That method consists of a model that predicts the operation control of the HVAC&R system according to the load, a hybrid performance model of each device, and a link detection model using a graph network method. All these models have been applied with customized machine learning methods in each model.
Transportation Energy Saving of Office Buildings by Retrofit Period
The changes in maximum cooling and heating load through retrofit were quantitatively verified compared to that of the initial design of the building based on previous study results, and flow rates of cool and hot water were determined by re-calculating the capacity of the heat-source equipment. The calculation results verified that the transportation power decreased by up to approximately 10% when oversized pipes were re-used from the existing water-pipe system. Additionally, when the capacity of the heat-source equipment was decreased, reasonable measures considering remodeling, construction duration, and cost were derived.
Mist-spray Outdoor Units
This study aimed to increase efficiency of outdoor units by evaporating and cooling intake air through mist spray at the intake port surface in the outdoor unit. The measurements results showed that total power consumption of misting outdoor unit compared to that of conventional outdoor units was reduced by 21% approximately, and total power consumption of the entire system including pump was reduced by 16.7%. In addition, the operating cost including water use was reduced by 13.5% approximately.
Saving Consumption of a water-cooled VRF system
This study aims to improve the performance of outdoor units in the VRF by reducing the temperature of cooling water to a lower temperature than the existing setup value and verify this through actual measurements. A new algorithm for a cooling tower operation was developed and applied to a six-week operation during the summer to analyze the effect of the change in setup value. The result showed that power consumption of the cooling tower system and VRF outdoor unit was reduced by 24% and 5.9%.