AI RESEARCH
A Scalable Digital Twin Framework for Energy Optimization in Data Centers
arXiv CS.LG
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ArXi:2605.05581v1 Announce Type: cross 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 operational decision-making.