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2025 Vol.19, Issue 5

Research Article

A Study on the Derivation of Optimal Fluid Temperature by Outdoor Wet-bulb using Energy Predicted Model in Data Center

데이터센터 에너지 예측 모델을 활용한 외기습구온도별 최적 유체 온도 도출에 대한 연구

Su-Min Go1
,
Young-Hak Song2*
고 수민1
,
송 영학2*
1Graduate Student, Department of Architectural Engineering, Graduate School, Gyeongsang National University, Jinju, Korea
2Professor, Department of Architectural Engineering, ERI, Gyeongsang National University, Jinju, Korea
1경상국립대학교 건축공학과 대학원 석사과정
2경상국립대학교 건축공학과 교수
*Corresponding Author
30 October 2025. pp. 191-200
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Abstract

As societal demands for carbon neutrality continue to rise, reducing energy consumption in the building sector has become essential. Among various building types, data centers consume a significant amount of energy, prompting numerous studies to explore the application of Water-side Economizer (WSE) systems that enable Free Cooling operation for enhanced energy efficiency. This study aims to optimize the performance of WSE systems by proposing a control strategy that minimizes cooling energy consumption in response to varying outdoor conditions. As a preliminary step, a cooling energy prediction model for a data center was developed to derive optimal fluid temperature setpoints that minimize energy usage according to outdoor wet-bulb temperature. The predictive model was constructed using Long Short-Term Memory (LSTM) and Light Gradient Boosting Machine (LightGBM), and demonstrated high accuracy and reliability with R-squared value of 0.995, CV (RMSE) of 4.31%, and an MAE of approximately 1.13 kW. Based on this model, optimal fluid temperature combinations were determined for each outdoor wet-bulb condition throughout the year. These results are expected to serve as a foundational guideline for developing future control algorithms and establishing energy-efficient operation strategies.

Keywords
Artificial Intelligence (AI)
Predicted model
Outdoor humidity temperature
Chilled water
Condenser water
키워드
냉방 에너지
예측 모델
유체 온도
외기습구온도
외기냉수냉방
References
1

Adelekan, D.S., Ohunakin, O.S., Paul, B.S. (2022). Artificial Intelligence Models for Refrigeration, Air Conditioning and Heat Pump Systems. Energy Reports, 8, 8451-8466.

10.1016/j.egyr.2022.06.062
2

Afram, A., Janabi-Sharifi, F., Fung, A.S., Raahemifar, K. (2017). Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system. Energy and Buildings, 141, 96-113.

10.1016/j.enbuild.2017.02.012
3

Go, S.M., Park, H.E., Song, Y.H. (2024). Development of IDC Energy Consumption Predicted Model by Control Conditions using AI. Journal of Korean Institute of Architectural Sustainable Environment and Building System, 18(5), 441-450.

4

Lecun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

10.1038/nature14539
5

Lee, D., Ooka, R., Matsuda, Y., Ikeda, S., Choi, W. (2022). Experimental Analysis of Artificial Intelligence-based Model Predictive Control for Thermal Energy Storage under Different Cooling Load Conditions. Sustainable Cities and Society, 211(15), 103700.

10.1016/j.scs.2022.103700
6

Lee, J.H., Cho, Y.H. (2023). Development of Control Algorithm for Economizer System using Load Prediction Model. Journal of the Korean Solar Energy Society, 43(3), 1-10.

10.7836/kses.2023.43.3.001
7

Lee, S.J., Dao, V.Q. (2024). Energy-Efficient Operation Simulation of Factory HVAC System based on Machine Learning. Journal of Korea Society of Industrial Information Systems, 29(2), 47-54.

10.9723/JKSIIS.2024.29.2.047
8

Merabet, G.H., Essaaidi, M., Haddou, M.B., Kabbaj, M.I. (2021). Intelligent Building Control Systems for Thermal Comfort and Energy-efficiency: A Systermatic Review of Artificial Intelligence-assisted Techniques. Renewable and Sustainable Energy Reviews, 144, 110969.

10.1016/j.rser.2021.110969
9

Sun, K., Luo, N., Luo, X., Hong, T. (2021). Prototype energy models for data centers. Energy and Buildings, 231, 110603.

10.1016/j.enbuild.2020.110603
10

Yayla, A., Swierczewska, K.S., Kaya, M., Karaca, B., Arayici, Y., Ayözenn, Y.E., Tokdemir, O.B. (2022). Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings. Sustainability, 14(23), 16107.

10.3390/su142316107
11

Yeon, S.H., Lee, K.H. (2024). Developing a DNN-Based Control Algorithm for Optimizing AHU Discharge Air and Cooling Water Temperatures in an Office Building. Korean Journal of Air-Conditioning and Refrigeration Engineering, 36(11), 535-545.

10.6110/KJACR.2024.36.11.535
12

Zhou, S.L., Shah, A.A., Leung, P.K., Zhu, X. (2023). A Comprehensive Review of the Applications of Machine Learning for HVAC. Decarbon, 2, 100023.

10.1016/j.decarb.2023.100023
13

ASHRAE Guideline 14. (2002). Measurement of Energy, Demand, and Water Savings.

14

ASHRAE. (2019). ASHRAE Handbook Heating, Ventilating, and Air-conditioning Applications.

Information
  • Publisher :Korean Institute of Architectural Sustainable Environment and Building Systems
  • Publisher(Ko) :한국건축친환경설비학회
  • Journal Title :Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
  • Journal Title(Ko) :한국건축친환경설비학회논문집
  • Volume : 19
  • No :5
  • Pages :191-200
  • Received Date : 2025-07-29
  • Revised Date : 2025-09-08
  • Accepted Date : 2025-09-09
  • DOI :https://doi.org/10.22696/jkiaebs.20250017
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