A self-assessment framework for evaluating efficiency of data centers

Increasing global data center energy consumption, with the rapid digitalization of society, has driven the need for comprehensive assessment reports periodically. Current challenges include the complexity of collecting data and calculating Key Performance Indicators (KPI) within the corresponding reporting period. To address this, we introduce the Self-Assessment Tool (SAT) as a unified, modular, and extensible framework for the evaluation of thermal and energy performance of data centers based on IT and cooling data sourced from data monitoring systems. A historical dataset can also be imported to the SAT to assess the energy efficiency of a data center based on the data collected from external sensors. The KPI calculation module of SAT calculates a comprehensive set of standardized thermal (RCI, RHI, RTI, RI, and LI) and energy (PUE and COP) metrics based on both real-time and historical datasets, with a transparent and reproducible KPI computation workflow that enables independent implementation. As demonstrated and validated on two pilot data centers located in Denmark and Switzerland, the SAT automatically generates assessment reports including time-series visualizations, rack-level thermal maps and energy-efficiency classifications according to the KPIs. Finally, the proposed framework can be deployed either as a standalone or web-based service for the performance evaluation of data centers following the present assessment workflow.

Authors

Institutions

Publication Details

Journal
Energy Informatics
Published
2026-02-26
DOI
https://doi.org/10.1186/s42162-026-00652-7
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A self-assessment framework for evaluating efficiency of data centers

Sascha Stoller, Cagatay Yilmaz, Jesper Ellerbæk Nielsen, Reto Fricker et al.
Energy Informatics
Cloud Computing and Resource Management
article

A self-assessment framework for evaluating efficiency of data centers

Sascha Stoller, Cagatay Yilmaz, Jesper Ellerbæk Nielsen, Reto Fricker, Thomas Juul, Simon Pommerencke Melgaard, Basak Bayraktar, Binod Prasad Koirala, Axel Kärnebro, Mustafa Kuzay, Ender Demirel, Gabriele Humbert, Josef Vilestad
article en

Abstract

Increasing global data center energy consumption, with the rapid digitalization of society, has driven the need for comprehensive assessment reports periodically. Current challenges include the complexity of collecting data and calculating Key Performance Indicators (KPI) within the corresponding reporting period. To address this, we introduce the Self-Assessment Tool (SAT) as a unified, modular, and extensible framework for the evaluation of thermal and energy performance of data centers based on IT and cooling data sourced from data monitoring systems. A historical dataset can also be imported to the SAT to assess the energy efficiency of a data center based on the data collected from external sensors. The KPI calculation module of SAT calculates a comprehensive set of standardized thermal (RCI, RHI, RTI, RI, and LI) and energy (PUE and COP) metrics based on both real-time and historical datasets, with a transparent and reproducible KPI computation workflow that enables independent implementation. As demonstrated and validated on two pilot data centers located in Denmark and Switzerland, the SAT automatically generates assessment reports including time-series visualizations, rack-level thermal maps and energy-efficiency classifications according to the KPIs. Finally, the proposed framework can be deployed either as a standalone or web-based service for the performance evaluation of data centers following the present assessment workflow.

Energy Informatics
RISE Research Institutes of Sweden (SE), Eskisehir Technical University (TR), Swiss Federal Laboratories for Materials Science and Technology (CH), Aalborg University (DK)
Affordable and clean energy
Openalex Percentile: Top 60%
Cloud Computing and Resource Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.