Abstract
The objective of this study was to develop a heating, ventilation, and air conditioning (HVAC) system optimization control strategy involving fan coil unit (FCU) temperature control for energy conservation in chilled water systems to enhance the operating efficiency of HVAC systems. The proposed control strategy involves three techniques, which are described as follows. The first technique is an algorithm for dynamic FCU temperature setting, which enables the FCU temperature to be set in accordance with changes in the outdoor temperature to satisfy the indoor thermal comfort for occupants. The second technique is an approach for determining the indoor cold air demand, which collects the set FCU temperature and converts it to the refrigeration ton required for the chilled water system; this serves as the control target for ensuring optimal HVAC operation. The third technique is a genetic algorithm for calculating the minimum energy consumption for an HVAC system. The genetic algorithm determines the pump operating frequency associated with minimum energy consumption per refrigeration ton to control energy conservation. To demonstrate the effectiveness of the proposed HVAC system optimization control strategy combining FCU temperature control, this study conducted a field experiment. The results revealed that the proposed strategy enabled an HVAC system to achieve 39.71% energy conservation compared with an HVAC system operating at full load.
Highlights
Air conditioning, lighting, and ventilation systems constitute approximately 50% of the total power consumption of a building
The chilled water system provides chilled water to the cooling coils of the fan coil unit (FCU), and the water in the coils is subsequently converted to cold air that is distributed to each area of the building
Most of the abovementioned documents are optimized for ice water systems, or use the simulation method to estimate the comfort of the indoor ice-water system energy consumption comparison and prediction, but do not combine the indoor real heat demand and HVAC power consumption
Summary
Air conditioning, lighting, and ventilation systems constitute approximately 50% of the total power consumption of a building. Model predictive control (MPC) is comprehensively used to determine indoor HVAC temperature settings and comfortable temperature values by employing optimal control methods to reduce energy consumption and maintain indoor thermal comfort. Castilla et al [9] proposed a hierarchical control method that entails the use of a non-linear MPC strategy to predict control The purpose of this method is to maintain indoor comfort while preventing an HVAC system from entering high-load operation and to optimize efficiency during low-load operation. Most of the abovementioned documents are optimized for ice water systems, or use the simulation method to estimate the comfort of the indoor ice-water system energy consumption comparison and prediction, but do not combine the indoor real heat demand and HVAC power consumption. This paper aims to solve the optimal control strategy of the chilled water system of HVAC buildings and approach the request of the occupants for indoor thermal comfort. The effectiveness of this energy conservation algorithm was tested through a field experiment
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