构建可扩展的数字孪生框架，实时优化数据中心能耗。
A Scalable Digital Twin Framework for Energy Optimization in Data Centers
- 融合物联网与机器学习，实现能耗数据实时采集与预测
- 采用LSTM模型预测用电需求，使能效提升，PUE改善
- 小规模实验验证可行性，适合可持续数据中心管理
本研究提出一种可扩展的数据中心数字孪生框架，用于能源优化。该框架整合基于物联网的数据采集、云计算与机器学习技术，实现能耗的实时监控、预测与智能管理。在受控的小规模数据中心环境中，监测了功耗、温度和计算负载等变量。采用长短期记忆（LSTM）模型预测能源需求，支持运营决策。实验结果表明，该框架显著提升了能源效率，实现了功耗降低与功率使用效能（PUE）改善。尽管在受限环境下评估，但展现出作为可扩展、低成本解决方案的巨大潜力，适用于可持续数据中心管理。
原文摘要 · Abstract (English)
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.
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