2026-10-15 リンショーピング大学
＜関連情報＞
- https://liu.se/en/news-item/study-reveals-where-a-data-centre-s-energy-goes
- https://www.nature.com/articles/s41598-026-68222-1
水冷式データセンター室における階層的分類法の追求 Pursuing a hierarchical taxonomy in a water-cooled data center room
Vlatko Milić, Patrik Thollander, Maria Andersson, Jim Enkel & Bahram Moshfegh
Scientific Reports Published:24 August 2026
DOI:https://doi.org/10.1038/s41598-026-68222-1
Abstract
Despite the growing number of scientific investigations on developing taxonomies in various industries and categorizing energy end-use (EEU) processes, research on such frameworks in the data center (DC) sector remains limited, restricting systematic analysis of energy use, benchmarking, and identification of energy efficiency measures. This study seeks to achieve two key objectives: (1) to create a hierarchical taxonomy for categorizing EEU process, (2) and to identify the most relevant energy performance indicators (EnPIs). The methodology combines hierarchical classification of energy flows with quantitative analysis based on time-resolved operational data, allowing for detailed analysis of dynamic system behaviour and allocation of energy use across processes, applied to a DC room with a detached in-rack water cooling system in Sweden operated by Ericsson. The results of this paper present a novel hierarchical taxonomy for a water-cooled DC room with six defined EEU processes, of which four are related to data processing processes and two to support processes. Notably, over 99.9% of total EEU is connected to data processing processes which is a significantly higher figure compared to other production processes in other sectors. Moreover, the established EnPIs can facilitate benchmarking between DCs with similar infrastructures and for monitoring the energy use of various EEU processes. The proposed methodology is transferable to other DC environments with comparable system boundaries and data availability, and its reproducibility is supported by the structured and data-driven methodology, which enables replication of the analytical approach given comparable data availability.
