🤖 AI Summary
This study addresses the challenges of high energy consumption and the lack of real-time, scalable energy efficiency management in data centers by proposing a digital twin–based, scalable energy optimization framework. The framework integrates real-time IoT data acquisition, cloud computing infrastructure, and Long Short-Term Memory (LSTM) neural networks, representing the first integration of digital twin technology with LSTM for modeling and forecasting data center energy consumption. This synergy enables real-time monitoring, accurate prediction, and intelligent control of energy usage. Experimental results demonstrate that the proposed approach significantly reduces power consumption and improves Power Usage Effectiveness (PUE) in small-scale environments, while exhibiting strong scalability, cost-effectiveness, and practical potential for broader deployment.
📝 Abstract
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.