https://doi.org/10.6110/KJACR.2025.37.3.107

Jin-A Jeong ; Sheng-Hoon Park ; Ji-Yong Yu ; Eui-Jong Kim

This study aimed to enhance the accuracy of chiller system performance predictions by comparing a model based on fixed performance curves with an optimized model tailored to actual operating conditions. Key performance indicators, including Capacity, Coefficient of Performance (COP), and Fraction of Full Load (FFLP), were adjusted to align with operational data. Results indicated that the existing model, reliant on fixed experimental conditions, failed to adequately reflect dynamic operational changes, leading to discrepancies between predicted and actual power consumption. In contrast, the optimized model effectively incorporated real-time conditions, resulting in improved prediction accuracy and significantly reduced prediction errors. This underscores the necessity of refining performance curves for accurate chiller system predictions to enhance energy efficiency and cost savings. Future research should...

https://doi.org/10.6110/KJACR.2025.37.3.119

Jin Man Kim ; Seon-Chang Kim

This study aimed to develop a heat transfer coefficient correlation for R1336mzz(Z) in the supercritical region using deep learning. The unique thermal behavior of supercritical fluids, especially around the pseudo-critical point where property variations are significant, poses challenges for traditional heat transfer coefficient correlations. To address this, an intermediate model was first generated based on experimental data to reduce overfitting risks, followed by deep learning model training. The deep learning approach improved the prediction accuracy for heat transfer coefficients over conventional correlations, effectively capturing rapid changes near the pseudo-critical temperature. The resulting model had a good agreement with experimental data across various conditions, showing a higher coefficient of determination than existing methods, indicating its potential for broader applications in supercritical fluid heat...

https://doi.org/10.6110/KJACR.2025.37.3.131

Huijing Yan ; Jumi Baek ; Sowoo Park ; Ji Soo Shim ; Doosam Song

To achieve 2050 carbon neutrality in the building sector, the government is pushing for mandatory zero-energy buildings for new buildings and green remodeling to significantly improve energy efficiencies of existing buildings. In this study, we developed a green remodeling decision-making tool in preliminary planning stage that could calculate costs and perform energy performance analysis in the initial stage of creating an Excel-based user's input error based on ISO 13790, an internationally recognized building energy performance evaluation logic, to support green remodeling decision-making. Energy performance analysis through simple input was conducted and real-time linkage with ISO 13790 was used through application of technical elements for each target building to propose a plan to show the optimal energy saving effect for the target building. Final results are shown through a readable picture only for...

https://doi.org/10.6110/KJACR.2025.37.3.143

Jae Beom Jeon ; Young Il Kim

The Building Energy Management System (BEMS) continuously monitors and analyzes real-time energy consumption data such as electricity and gas to optimize efficiency and propose energy-saving strategies. However, communication failures, device malfunctions, and system updates can lead to missing or abnormal data, necessitating effective data recovery methods. Addressing these data gaps is crucial for maintaining reliability of energy analysis. Traditionally, simple linear interpolation has been used for data recovery. However, this method is prone to substantial inaccuracies. In addition, it is insufficient for complexities of energy systems. The power consumption of a building is heavily influenced by heating and cooling loads, which are closely related to outdoor conditions. To restore missing power consumption data, we proposed a method that could identify a day with similar changes in outdoor...

https://doi.org/10.6110/KJACR.2025.37.3.151

Joo Yeon Roh ; Se Jin Lee ; Won Seok Lee ; Tae Young Park ; Dong Il Park ; Myoung Souk Yeo

This study aimed to address noise issues and concerns regarding contaminant re-entry in temporary negative pressure isolation rooms equipped with portable negative pressure units. To achieve this, we developed low-noise integrated portable HEPA filter unit and separated unit, in which fan and filter components were divided. We then evaluated their performance in both a healthcare facility and a simulated chamber, comparing with the existing integrated unit currently used in healthcare facilities. Results showed that the low-noise integrated unit significantly reduced noise levels compared to the existing integrated unit. The separated unit exhibited similar noise levels to the low-noise integrated unit at lower airflow rates but exhibited lower noise levels compared to the integrated unit as airflow increased. The separated unit also effectively prevented contaminant re-entry, with lower tracer gas...