The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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Artificial Neural Network-based Thermal Environment Prediction Model for Energy Saving of Data Center Cooling Systems

데이터센터 냉각 시스템의 에너지 절약을 위한 인공신경망 기반 열환경 예측 모델

  • 임채영 (고등기술연구원 에너지환경IT융합그룹) ;
  • 여채은 (고등기술연구원 에너지환경IT융합그룹) ;
  • 안성율 (고등기술연구원 에너지환경IT융합그룹) ;
  • 이상현 (호남대학교 컴퓨터공학과)
  • Received : 2023.10.02
  • Accepted : 2023.11.10
  • Published : 2023.11.30
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Abstract

Since data centers are places that provide IT services 24 hours a day, 365 days a year, data center power consumption is expected to increase to approximately 10% by 2030, and the introduction of high-density IT equipment will gradually increase. In order to ensure the stable operation of IT equipment, various types of research are required to conserve energy in cooling and improve energy management. This study proposes the following process for energy saving in data centers. We conducted CFD modeling of the data center, proposed an artificial intelligence-based thermal environment prediction model, compared actual measured data, the predicted model, and the CFD results, and finally evaluated the data center's thermal management performance. It can be seen that the predicted values of RCI, RTI, and PUE are also similar according to the normalization used in the normalization method. Therefore, it is judged that the algorithm proposed in this study can be applied and provided as a thermal environment prediction model applied to data centers.

데이터센터는 24시간 365일 IT 서비스를 제공하는 곳이기 때문에, 2030년에는 데이터센터의 전력 소비량은 약 10%로 증가될 것으로 예측되고, 고밀도 IT장비들의 도입이 점차 증가하면서, IT장비가 안정적으로 운영될 수 있도록 냉방 에너지 절감 및 이를 위한 에너지 관리가 갖춰져야 하기에 다양한 연구가 요구되고 있는 상황이다. 본 연구는 데이터센터의 에너지 절약을 위해 다음과 같은 과정을 제안한다. 데이터센터를 CFD 모델링하고, 인공지능기반 열환경 예측 모델을 제안하였으며, 실측 데이터와 예측 모델 그리고 CFD 결과를 비교하여 최종적으로 데이터 센터의 열관리 성능을 평한 결과 전처리 방식은 정규화 방식으로 사용되었고, 정규화에 따른 RCI, RTI 및 PUE의 예측값 또한 유사한 것을 확인할 수 있다. 따라서 본 연구에서 제안하는 알고리즘으로 데이터센터에 적용될 열환경 예측 모델로 적용 및 제공할 수 있을 것으로 판단된다.

Keywords

Acknowledgement

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT)(No.RS-2023-00233745, Development and performance evaluation of data center cooling system using liquefied gas cooling heat)

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