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Development of Row-based Energy Evaluation Methodology for Legacy Data Centers and M&V Baseline Models based on PUE
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기존 데이터센터의 구역단위 에너지 효율평가 방법 및 PUE 기반 M&V 베이스라인 모델 개발

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Type
Academic journal
Author
Jinkyun Cho (국립한밭대학교) Won-Pyo Hong (국립한밭대학교)
Journal
The Society Of Air-Conditioning And Refrigerating Engineers Of Korea Korean Journal of Air-Conditioning and Refrigeration Engineering Vol.32 No.2 KCI Excellent Accredited Journal
Published
2020.2
Pages
88 - 99 (12page)
DOI
10.6110/KJACR.2020.32.2.088

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Development of Row-based Energy Evaluation Methodology for Legacy Data Centers and M&V Baseline Models based on PUE
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Data centers usually operate 24/7 year round and are very energy intensive. A data center sometimes uses up to 50 times more energy than a typical office building. High energy consumption can be attributed primarily to the IT demands and cooling equipment, power distribution. M&V (Measurement and Verification) is practical for all building types. An accurate determination of the energy baseline provides valuable feedback on the ECMs (energy conservation measures). As an alternative to the existing baseline method for normal buildings, we developed an energy baseline model for the M&V based on prediction models with the PUE index. This paper specifies a methodology of developing the PUE-based baseline for energy savings from the installation of mechanical and electrical systems in the data centers. In applying the PUE-based energy baseline, we derived the non-IT reference model and the baseline PUE for the IT server room area of large data centers in operation. This paper presents the standardized M&V approaches which target the minimizing of variations in quality across the analyses, and energy baseline requirements for data center projects and identifies energy prediction rules.
#Data center(데이터센터) #M& #V(측정 및 검증) #Row-based energy evaluation(구역단위 에너지평가) #PUE(전력사용효율화) #Energy baseline(베이스라인)

Contents

Abstract
1. 연구배경 및 목적
2. 데이터센터 M&V 기술현황
3. 데이터센터 에너지 베이스라인 모델 개발
4. Case Study; 구역단위 PUE 에너지 효율평가
5. 결론
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UCI(KEPA) : I410-ECN-0101-2020-553-000345857