AI-Driven Digital Twin for Energy Optimization in Green Data Centers
DOI:
https://doi.org/10.56127/ijst.v4i2.2249Keywords:
Artificial Intelligence (AI); Digital Twin; Green Data Center; Energy Optimization; Power Usage Effectiveness (PUE); Carbon Usage Effectiveness (CUE); Sustainable ComputingAbstract
This study proposes the development of an AI-driven digital twin for data centers aimed at improving energy efficiency, reducing carbon footprint, and enhancing operational performance. The digital twin—a virtual replica of the physical data center—will be equipped with real-time AI algorithms to predict thermal loads, analyze cooling requirements, and automatically adjust operations to minimize energy consumption. This paper explores the integration of AI with digital twin architectures, tests its performance in simulated scenarios, and evaluates potential energy savings as well as contributions to Green IT practices.
References
Abdessadak, A. (2025). Digital twins combined with artificial intelligence pave the way to smart energy systems. Energy Reports, 12(4), 1423–1436.
Aghazadeh Ardebili, A., et al. (2024). Digital Twins of smart energy systems: a systematic literature review on enablers, design, management and computational challenges. Energy Informatics, 7, 94.
Sarkar, S., et al. (2024). Sustainability of Data Center Digital Twins with Reinforcement Learning. AAAI Conference (peer-reviewed).
Sarkar, S., et al. (2024). Sustainability of Data Center Digital Twins with Reinforcement Learning — ArXiv preprint (DCRL-Green).
Li, Y., Wen, Y., Guan, K., & Tao, D. (2017). Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning. ArXiv preprint.
Boscariol, M. (2025). Review on Green Data Centres: incorporating eco friendly equipment and enhancing operational efficiency. (In press)
TechTarget. (2025, March 6). What is a green data center? — Definitions and best practices.
EE Times. (2024, June 24). Harnessing Digital Twin Technology for Energy Efficient Data Centers.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Isni Oktria

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.













