A Scalable Digital Twin Framework for Energy Optimization in Data Centers
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 Effective...
| Authors | Raphael Hendrigo de Souza Gonçalves, Wendel Marcos dos Santos |
|---|---|
| Primary category | cs.DC |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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