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Detection of Cooling Operational Statuses in Data Center Energy Management using Clustering Algorithms
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Building Engineering, Energy Systems and Sustainability Science, Energy Systems and Building Technology. Linköpings universitet.ORCID iD: 0000-0002-0604-3672
Linköpings universitet.
Linköpings universitet.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Building Engineering, Energy Systems and Sustainability Science, Energy Systems and Building Technology. Linköpings universitet.ORCID iD: 0000-0002-4823-9905
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2024 (English)In: 2024 23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), IEEE , 2024Conference paper, Published paper (Refereed)
Abstract [en]

In our digitalized world, Data Centers (DCs) serve as crucial infrastructure. Within the DC sector, data processing operations, including processes such as process cooling, hold special significance when investigated from an energy efficiency perspective, as they account for a substantial portion of total energy end-use. Therefore, it is important to prioritize data processing operations in energy management. The objective of this research is to explore the application of AI-powered clustering techniques to identify cooling operational statuses. Additionally, this research offers valuable perspectives on using AI for visualizing and identifying cooling patterns that deviate, which can provide valuable insights into DC energy management. The study object consists of a DC room equipped with Liquid Cooling Packages (LCPs). The findings show that the cooling power density on average is 9.1 kW/m 2 . Through analysis of the elbow curve, the optimal number of clusters is identified to be three. One of the identified clusters, i.e., Cluster 3, is characterized by large time periods with no supplied cooling from the LCPs. When comparing Clusters 1 and 2, Cluster 1 has a higher temperature difference between the chilled water supply and return, but a lower LCP return temperature compared to Cluster 2. Moreover, the quantified cooling characteristics contribute to the understanding of the LCPs’ operational statuses and cooling performance, which is useful for implementing targeted improvements, e.g., adjusting PID parameters, in the cooling infrastructure.

Place, publisher, year, edition, pages
IEEE , 2024.
Keywords [en]
Data Center, Cooling operational statuses, Energy management, Clustering algorithms, AI
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:hig:diva-45860DOI: 10.1109/itherm55375.2024.10709422Scopus ID: 2-s2.0-85207839276ISBN: 979-8-3503-6433-0 (electronic)OAI: oai:DiVA.org:hig-45860DiVA, id: diva2:1906378
Conference
2024 23rd IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), Aurora, Colorado, USA, 28-31 May 2024
Available from: 2024-10-17 Created: 2024-10-17 Last updated: 2024-11-11Bibliographically approved

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Milić, VlatkoThollander, PatrikMoshfegh, Bahram

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