Leveraging Artificial Intelligence to Reduce Cooling and Power Footprint

With the proliferation of high-density data centres, AI-infrastructure, and ultra-efficient compute clusters, the supporting systems for power delivery and cooling have become more complex than ever. This has spawned a new class of solutions: AI for energy optimisation (sometimes called “AIOps” for infrastructure). These systems harness machine learning (ML) and AI to fine-tune power and cooling operations in real time, reducing costs, improving reliability, and enhancing sustainability.

The Problem Space: Why Optimisation Matters

Data centres are energy hungry. With rising densities, cooling systems, air-handlers, fans, pumps, chillers and supporting infrastructure collectively consume large portions of site energy. According to recent commentary, “artificial intelligence is growing fast, and so are the number of computers that power it… these facilities are using more energy than ever.” dayton-daily-news Optimisation is key: reducing wasted cooling, dynamically adjusting to workload changes, and improving overall PUE (power-usage effectiveness) and TUE (thermal-usage efficiency).

How AI-Based Systems Work

AI-driven optimisation in power and cooling uses sensors, real-time telemetry, historical data, predictive modelling and control loops to make smart adjustments. For example:

• Predictive workload placement that minimises thermal hotspots and balances cooling load. ijctjournal.org

• HVAC and chiller control algorithms that adjust fan speeds, chilled-water temperatures, pump flows and server inlet conditions in real time. Robotics & Automation News

• Integration of power systems (UPS, generators, batteries) with cooling systems so that when power draw spikes, cooling is adjusted proactively to avoid thermal stress or overshoot.

• Fault-detection and anomaly-detection systems: AI flags abnormal energy consumption, cooling pump inefficiencies or emerging hot-spots before they cause outages. SPIE Digital Library

Case Studies and Industry Adoption

Major operators and hardware providers are already deploying these solutions. For instance, a white-paper by Siemens illustrates how AI is used to optimise data-centre thermal and cooling operations (termed “Whitespace Cooling Optimisation”). Siemens Assets Academic studies show reinforcement-learning models applied to cooling control in data centres can dynamically reduce energy consumption while maintaining service-levels. ijctjournal.org

Benefits to Cooling Power Infrastructure

• Energy reduction: Less wasted cooling, more targeted airflow/liquid-flow control, lower chiller and pump energy.

• Improved reliability: By proactively identifying hotspots or inefficiencies, downtime risk is reduced.

• Scalability: As rack densities increase, manual or static cooling set-ups become untenable. AI brings automation and adaptability.

• Sustainability gains: Lower energy consumption implies lower carbon intensity, better utilisation of green power, improved metrics for enterprise sustainability reporting.

Key Challenges and Considerations

• Data availability and quality: Optimisation only works if the sensor data, telemetry, and historical records are accurate and comprehensive.

• Integration complexity: AI systems must tie into HVAC, liquid-cooling loops, power systems, workload scheduling, and sometimes edge or legacy systems.

• Controls risk: Over-automation without proper safeguards can introduce new failure modes (e.g., aggressive cooling reduction leading to thermal stress).

• Cost vs ROI: Initial investment in sensors, AI platforms, and staff training must be justified by energy savings and operational reliability.

• Workload variability: AI optimisation must account for tidal compute loads, bursty AI training jobs, seasonal cooling demands and external ambient conditions.

The Strategic View

For operators focused on high-density compute (AI training clusters, HPC, large-scale cloud), AI-enabled cooling/power optimisation is quickly becoming a differentiator — both in terms of cost-base and sustainability credentials. As legacy systems become increasingly inadequate, the ability to dynamically orchestrate power and cooling across sources, loads and thermal systems will influence the economics of next-generation infrastructure builds.

Closing Thoughts and Looking Forward

In the coming years we’ll see tighter coupling of AI in infrastructure: workload scheduling will feed into cooling decisions, power systems will be aware of thermal demands, and entire data-centre systems will become “self-optimising”. The cooling-power nexus will no longer be two parallel silos, but one orchestrated system.

Serge Boudreaux – AI Hardware Technologies, Montreal, Quebec
Peter Jonathan Wilcheck – Co-Editor, Miami, Florida

References

1. “Energy efficiency in data centers: How cooling and AI are reducing…” Robotics & Automation News. https://roboticsandautomationnews.com/2025/08/05/energy-efficiency-in-data-centers-technologies-and-solutions/93535/ Robotics & Automation News

2. “AI-Driven Data Center Cooling Optimization | Reinforcement Learning for …”, IJCT Journal. https://ijctjournal.org/ai-data-center-cooling-optimization/ ijctjournal.org

3. “Artificial intelligence–enabled predictive energy saving planning of …”, SPIE Digital Library. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13812/138120L/Energy-optimization-management-strategies-for-data-centers-assisted-by-AI/10.1117/12.3087058.full SPIE Digital Library

4. “AI-Enhanced Cooling Systems: Innovations in Heat Management for …”, IJERT. https://www.ijert.org/research/ai-enhanced-cooling-systems-innovations-in-heat-management-for-hyperscale-data-centers-IJERTV13IS110128.pdf IJERT

5. “The Future of Data Center Cooling: AI Innovations and Advanced HVAC …”, McKinsey Electronics. https://www.mckinsey-electronics.com/post/the-future-of-data-center-cooling-ai-innovations-and-advanced-hvac-motor-technologies Mckinsey Electronics