All Issue

2025 Vol.19, Issue 6 Preview Page

Research Article

Development of Cooling Energy Reduction Algorithm for Optimal Control of Chilled and Condenser Water in WSE

Water-side economizer 시스템의 냉수 및 냉각수 최적 제어를 위한 냉방 에너지 절감 알고리즘 개발

Su-Min Go1
,
Su-Ji Kim1
,
Young-Hak Song2*
고 수민1
,
김 수지1
,
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 December 2025. pp. 416-427
PDF XML Citation
Abstract

This study aims to develop and quantitatively verify the effectiveness of an optimal control algorithm for a Water-side Economizer (WSE) system to reduce cooling energy consumption in data centers. Optimal fluid combinations that minimize energy usage for each outdoor wet-bulb temperature were derived using a combined LSTM and LightGBM model. An algorithm for the optimal operation of chilled water and condenser 1, 2 water, including the selection of the most appropriate optimal fluid combination, was developed and applied to the WSE system. Comparative analysis between the model with the optimal control algorithm and the Base model revealed that the optimized model enhanced operational stability by precisely and regularly controlling the temperatures of the chilled water and cooling water. Furthermore, the Free cooling operation period increased by approximately 11.8% compared to the conventional operation, while the Chiller cooling operation period decreased by 23.4%, resulting in reduced overall system energy consumption and lowering the peak load from 180 kW to 160 kW. These results confirm that the optimal operation of chilled water and cooling water contributes to cooling energy savings and improved system efficiency in data centers.

Keywords
Water-side economizer
optimal operation
cooling energy
artificial intelligence
chilled water
condenser water
키워드
외기냉방
최적 운전
냉방 에너지
인공지능
냉수
냉각수
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

Cho, J.K. (2021). An Analysis of the Data Center Energy Consumption Structure for Efficient Energy Utilization – In Case of S Telecom Data Center in Korea. Journal of the Architectural Institute of Korea, 37(8), 153-164.

10.5659/JAIK.2021.37.8.153
3

Ding, Y., Zhang, Q., Yuan, T. (2017). Research on Short-term and Ultra-short-term Cooling Load Prediction Models for Office Buildings. Energy and Buildings, 154, 254-267.

10.1016/j.enbuild.2017.08.077
4

Go, S.M., Song, Y.H. (2025). A Study on the Derivation of Optimal Fluid Temperatire by Outdoor Wet-bulb using Energy Predicted Model in Data Center. Journal of Korean Institute of Architectural Sustainable Environment and Building System, 18(5), 441-450.

5

Song, M.H. (2020). Data Center Trends and Forecasts in Korea. Journal of Korean Association of Air Conditioning Refrigerating and Sanitay Engineers, 37(10), 28-38.

6

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
7

Taylor, S.T. (2014). How to Design & Control Waterside Economizers. ASHRAE Journal, 56(6), 30-36.

10.1162/DESI_a_00247
8

Tradat, M., Manaserh, Y., Sammakia, B., Hoang, C.H., Alissa, H.A. (2021). An Experimental and Numerical Investigation of Novel Solution for Energy Management Enhancement in Data Centers using Underfloor Plenum Porous Obstructions. Applied Energy, 289, 116663.

10.1016/j.apenergy.2021.116663
9

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, 16107.

10.3390/su142316107
10

Presidential Commission on Carbon Neutrality and Green Growth (CNC). (2021). 2050 Carbon Neutrality Scenario.

11

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

12

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

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 :6
  • Pages :416-427
  • Received Date : 2025-11-17
  • Revised Date : 2025-12-03
  • Accepted Date : 2025-12-04
  • DOI :https://doi.org/10.22696/jkiaebs.20250036
Journal Informaiton Journal of Korean Institute of Architectural Sustainable Environment and Building Systems Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
Manuscript Submission
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close