Smart IoT-Based Leak Forecasting and Detection for Energy-Efficient Liquid Cooling in AI Data Centers

TL;DR

This paper introduces a smart IoT system utilizing machine learning for early leak forecasting and detection in liquid-cooled AI data centers, aiming to enhance energy efficiency and prevent costly shutdowns.

Contribution

It presents a novel integrated IoT monitoring approach combining LSTM and Random Forest models for leak prediction and detection in data centers.

Findings

Achieves 96.5% detection accuracy and 87% forecasting accuracy.

Forecasts leaks 2-4 hours in advance and detects sudden events within 1 minute.

Potentially prevents 1,500 kWh of annual energy waste.

Abstract

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.