The issue on how to enhance data centers’ cooling systems has been on the table for long now. Today, optimizing cooling has grown to represent a strategic area for data centers, considering its impact on protecting hardware, energy consumption and overall data center economic viability.
In this context, the rise of AI-driven smart data center solutions emerges as a key solution. In essence, what the AI data center does is allowing for thermal management strategies that adapt to real-time conditions. This translates into a series of benefits, including the opportunity for cutting down costs and data centers’ environmental impact while ensuring equipment is at desirable temperature ranges at all times.
This shift is particularly crucial at a time when increasing computational demands are expected in the coming years, which will require doubling down on efficiency in order to build resilience while remaining economically sound.
Data centers’ cooling systems represent a complex ecosystem whose energy consumption can vary from about 7% to 30%, according to figures by the IEA. By applying AI-based automations, leading data centers such as Google’s have achieved an outstanding 40% reduction in energy consumption. An achievement that hasn’t gone unnoticed, with the smart data center will likely soon become the norm: the Uptime Institute Global Data Center Survey 2024 cites how “nine out of 10 (91%) vendor respondents believe that it is likely that AI will be widely used in the data center in the next five years”.
But how exactly are AI-driven solutions applied to data centers’ cooling systems?
In this article, we analyze the potential for AI to adjust data center cooling to real-time conditions and the ripple beneficial effects that it entails.
The two key ways AI is transforming data centers’ cooling systems
Real-time monitoring for dynamic cooling and predictive analytics
Sensors, digital twins and machine learning models are the first step in the AI ecosystem when it comes to data centers’ cooling systems.
These three act in the following progression: firstly, sensors provide monitoring capabilities. In charge of collecting real-time environmental and equipment information, they may monitor temperature data but also airflow data and power load data.
Building from this, digital twins use this wide spectrum of data to simulate potential cooling strategies and predict their outcomes without interrupting core operations. Finally, machine learning models are capable of seeing ‘the bigger picture’, analyzing complex data patterns to infer thermal trends and system requirements, and adapting cooling strategies as a consequence.
By combining these three technologies, a highly-adaptive smart data center emerges, where the cooling system responds to real workload and environmental conditions.
In other words, AI is helping build extremely accurate and dynamic data centers’ cooling systems that match real-time conditions and demand, reducing energy consumption when possible while guaranteeing adequate temperature ranges.
At the same time, AI-based technologies are also instrumental in predicting potential undesirable scenarios, allowing operators to apply preventive maintenance to extend equipment’s life. As such, advanced sensors can detect damage to cooling equipment at an early stage, issuing a warning that activates maintenance for the affected component before the actual issue takes place. As such, AI represents an opportunity to enhance equipment longevity and resilience.
Intelligent cooling algorithms
The implications of AI for the smart data center go beyond the real-time, dynamic scenarios we just described. Intelligent cooling algorithms are capable of creating a system that is self-sufficient and self-governed, reducing human errors while expanding the capacities of operators.
At the core of this capacity are tools such as Reinforcement Learning, where the cooling system learns to make decisions by interacting with data in an iterative manner and with the aim of maximizing a long-term reward. In the case of RL applied to data centers’ cooling systems, it can learn to maximize energy use as a reward while also maintaining the desired temperature levels. In order to do so, it interacts with data on workloads, environmental conditions and cooling system parameters.
Additionally, control loops are introduced in AI-based systems to guarantee all variables remain at desired levels by applying continuous adjustments.
By combining RL and control loops, it’s possible to optimize fundamental aspects of cooling systems such as fan speeds, liquid flow and chiller activity, all with a high degree of accuracy.
For instance, in Luo et al. (2022), Reinforced Learning is suggested as a method to improve chiller operation as part of Google’s AI data centers: even in complex scenarios where multiple chillers are involved, the paper shows Reinforced Learning goes beyond conventional control systems by minimizing energy consumption based on dynamic data. In fact, the paper mentions how this approach resulted in energy savings of between 9 and 13%.
Emerging liquid cooling technologies powered by AI
Liquid cooling solutions are taking a central role in taking data centers’ cooling systems to the next level in terms of efficiency. At a time when increased data center redundancy and energy efficiency are fundamental, liquid cooling provides a solution for data centers that are incorporating high-performance computing and more intense workloads.
Today, the top liquid cooling techniques include:
- Liquid immersion cooling: relies on submerging IT equipment in dielectric fluids, which absorb the heat and thus reduce temperatures.
- Direct-to-chip cooling: it is based on circulating dielectric fluids directly to the components that typically produce the most heat, such as processing chips or motherboard components.
The choice between these two will largely depend on each data centers’ particular context, requirements, opportunities and limitations. This is where the true value of ad hoc design for data centers’ cooling systems becomes evident. As a rule, liquid immersion can be particularly advantageous for data centers that aim at minimizing air-based equipment such as fans. Additionally, they can be paired up with waste heat recovery strategies for further environmental benefits. On the other hand, direct-to-chip cooling can be attractive for data centers looking for top efficiencies that don't necessarily need to rule out air-based auxiliary systems.
In both cases, the use of AI technologies allows for advanced and precise control in key areas such as regulating coolant temperature and coolant flow.
For instance, successful cooling strategies have been developed based on predictive coolant temperature analysis to reduce energy use and enhance system reliability. This approach has been analyzed by Ma et al. (2025), where “experimental results demonstrate the model’s superior accuracy compared to traditional prediction models”, with their model “effectively reducing the operational energy consumption of the liquid cooling system.”
The result is a balanced and precise liquid cooling model where data is employed as a lever to apply real-time optimizations and where energy savings and efficiency are priorities.
Benefits of AI-driven cooling systems for smart data centers
Energy efficiency and cost reductions
Google’s claim of having reduced their data center energy consumption by 40% thanks to DeepMind ensured attention towards AI-driven optimizations in cooling systems.
As seen across the article, outstanding figures like these rely on AI’s capability to continuously analyze and leverage real-time data, ensuring only the required energy is used while guaranteeing equipment safety at all times.
Because only the necessary cooling is activated, this opens the door to important energy efficiency and cost savings. Additionally, options such as free cooling in data centers can be activated strategically when actual conditions allow it.
Improved reliability and hardware longevity
AI technologies ensure hardware operates at desired temperature ranges at all times, reducing thermal stress on components. This enables an extension in equipment’s operational lifespan, which then cascades into a series of benefits, including less downtime due to overheating.
By ensuring consistent thermal management, data centers extend their reliability. On top of this, cost reductions are also achieved by AI’s predictive capabilities, which prevent catastrophic equipment failure (and its ensuing financial losses) while also increasing hardware’s lifespan in the medium and short term.
Scalability for future AI workloads
Data centers’ capacities to accommodate AI workloads in the not-so-distant future have been an important subject of discussion in the industry in recent times.
Critical projections have guided such discussions, with the issue of data center energy efficiency being a key concern.
In 2024, the IEA described data centers as accounting for “about 1.5% of global electricity consumption”, a figure that “has grown at 12% per year over the last five years.” Along the same lines, another report by the IEA paints a scenario where “from 2024 to 2030, data centre electricity consumption grows by around 15% per year, more than four times faster than the growth of total electricity consumption from all other sectors” and reaching “just under 3% of total global electricity consumption in 2030.”
This growth, which is “mainly driven by AI adoption” across the globe, puts data centers at a crossroads: they must scale up to handle increased computational demands while also limiting their energy consumption and environmental impact.
The AI-based smart data center solutions emerge as a key solution in this scenario. As seen above in this article, cooling systems that incorporate AI technologies are projected to enable outstanding improvements in energy efficiencies, necessary to make the data centers of the future technically, environmentally and economically viable.
Through applying adaptive and dynamic liquid cooling solutions, data centers’ cooling systems can advance to incorporate the heat generated by high-performance AI hardware, and they can do so efficiently.
In fact, smart cooling systems have been reporting the capacity to “increase the nominal tonnage of a typical plant by as much as 20%”. An important step to boost data centers’ cooling capacities to meet future workloads while also ensuring energy efficiency.
At a time when data centers’ cooling systems take centerstage as a strategic choice for economic and environmental reasons, at ARANER we put our thermal engineering expertise to work to become key allies for data center operators.
Today and in the near future, achieving data center redundancy for evolving computing-intensive workloads will be equally as important as designing green data centers that ensure compliance and remain economically viable.
In this scenario, design for data center cooling systems must incorporate cutting-edge technologies while also adjusting to each project’s needs, limitations and opportunities. This is precisely where ARANER comes in.
From modular cooling plants to Thermal Energy Storage (TES) systems and waste heat recovery, we design state-of-the-art data centers’ cooling systems that blend technology with sophisticated thermal engineering. The result? Fully-integrated AI-based data centers’ cooling systems that put energy efficiency and redundancy at the center.
Want to learn more about how we can help you build the smart data center your project needs? Discover more about our data center cooling solutions and get in touch with us to speak to our team.
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