用AI预测液冷泄露，提前2-4小时预警，减少数据中心能耗损失。
Smart IoT-Based Leak Forecasting and Detection for Energy-Efficient Liquid Cooling in AI Data Centers
- LSTM预测泄漏时间，随机森林实时检测异常
- 检测准确率96.5%，预报准确率87%（30分钟窗口）
- 适合关注液冷安全与节能的运维人员
以GPU为核心的AI数据中心采用液冷应对极端热负荷，但冷却液泄漏会导致重大能量损失和非计划停机。本文提出一种基于物联网的智能监控系统，结合LSTM神经网络进行概率性泄漏预测，以及随机森林分类器实现即时检测。在符合ASHRAE 2021标准的合成数据上测试，系统在90%置信度下实现96.5%的检测准确率和87%的预报准确率，时间窗口为±30分钟。分析表明，湿度、压力和流量提供强预测信号，而温度因服务器热惯性响应滞后。系统采用MQTT流传输、InfluxDB存储和Streamlit可视化，可提前2-4小时预报泄漏，并在1分钟内识别突发事件。对于典型47机架设施，该方案每年可避免约1,500 kWh能源浪费。尽管验证仍基于合成数据，结果证实了其在可持续数据中心运营中的部署可行性。
原文摘要 · Abstract (English)
AI data centers which are GPU centric, have adopted liquid cooling to handle extreme heat loads, but coolant leaks result in substantial energy loss through unplanned shutdowns and extended repair periods. We present a proof-of-concept smart IoT monitoring system combining LSTM neural networks for probabilistic leak forecasting with Random Forest classifiers for instant detection. Testing on synthetic data aligned with ASHRAE 2021 standards, our approach achieves 96.5% detection accuracy and 87% forecasting accuracy at 90% probability within plus or minus 30-minute windows. Analysis demonstrates that humidity, pressure, and flow rate deliver strong predictive signals, while temperature exhibits minimal immediate response due to thermal inertia in server hardware. The system employs MQTT streaming, InfluxDB storage, and Streamlit dashboards, forecasting leaks 2-4 hours ahead while identifying sudden events within 1 minute. For a typical 47-rack facility, this approach could prevent roughly 1,500 kWh annual energy waste through proactive maintenance rather than reactive emergency procedures. While validation remains synthetic-only, results establish feasibility for future operational deployment in sustainable data center operations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。