This study proposes a scalable Digital Twin framework for energy optimization in data centers.The framework integrates IoT-based data acquisition, cloud computing, and machine learning techniques to enable real-time monitoring, forecasting, and intelligent energy management. A controlled small-scale data center environment was developed to monitor variables such as power consumption, temperature, and computational workload. Long Short-Term Memory (LSTM) models were employed to predict energy demand and support operational decision-making. Experimental results demonstrated improvements in energy efficiency, including reductions in power consumption and enhancements in Power Usage Effectiveness (PUE). Despite being evaluated in a constrained environment, the proposed framework demonstrates strong potential as a scalable and cost-effective solution for sustainable data center management.
@article{arxiv.2605.05581,
title = {A Scalable Digital Twin Framework for Energy Optimization in Data Centers},
author = {Raphael Hendrigo de Souza Gonçalves and Wendel Marcos dos Santos},
journal= {arXiv preprint arXiv:2605.05581},
year = {2026}
}