Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%).

An air handling unit utilizes economizer control to reduce cooling energy consumption by intaking outdoor air (OA) at lower temperatures. This control modulates OA intake rates based on the OA temperature, adjusting to maximum and partial rates when the OA temperature is below the maximum limit set-point (MLSP), and to minimum rates when it exceeds the MLSP. The MLSP acts as a baseline for determining OA intake rates. However, current MLSPs do not account for the specific OA conditions in South Korea, leading to the intake of unnecessarily warm OA or underutilization of available cooler OA, both of which negatively impact cooling energy performance. Therefore, this study aims to identify the optimal MLSP for OA conditions in South Korea. Through evaluation of cooling energy performance and the indoor thermal environment at various MLSP, it was determined that an MLSP of 22 °C facilitates the lowest cooling energy consumption without adversely affecting the indoor thermal environment. Implementing this MLSP resulted in 5.9% energy savings compared to Case #1 (baseline). The findings indicate that setting an MLSP according to local OA conditions is crucial for maximizing energy savings through economizer control.

This study aimed to identify the optimal outdoor air (OA) intake temperature range for the application of a dry-bulb temperature-based air-side economizer in data centers. To this end, a series of parametric simulations was conducted using EnergyPlus (version 24.1.0) and a previously validated small-to-medium-scale data center model situated in Daejeon, South Korea (ASHRAE Climate Zone 4A). A parametric analysis was performed by varying the maximum allowable OA intake dry-bulb temperature from 10 to 18 °C in 2 °C increments. The simulation results indicated that OA intake was limited during the summer due to high humidity levels, whereas it was more viable during the winter and interseason period. However, when the maximum OA intake dry-bulb temperature exceeded 14 °C, the increase in latent heat load caused humidity increased overall energy consumption. To expand the applicable range for air-side economizer operation, the cooling system capacity was adjusted. The scenario with a 14 °C OA threshold demonstrated the most favorable balance between energy efficiency and compliance with operating environmental criteria. The findings of this study provide a technical basis for establishing OA intake guidelines in dry-bulb temperature-based economizer control. However, further field-based validation is required to verify the effectiveness of these simulation results under real-world operating conditions.