As artificial intelligence (AI) use surges, the data centre energy demand needed to support this growth is creating opportunities across many industries – from job creation to wealth building across commercial real estate construction, technology and venture funding. But with the global data centre sector set to expand at 14% (the compound annual growth rate) through 2030, it will require up to $3 trillion in infrastructure investment.
The data centre boom is also causing a stir in local communities, becoming a hot political issue. Along with the disruption to neighbourhoods from construction, data centres use a tremendous amount of energy and water.
Gartner analysts predict worldwide data centre electricity consumption will double by 2030, and the Environmental and Energy Study Institute reports that some large data centres require up to 5 million gallons of water each day. These demands are only expected to continue to increase as more data centres are built.
But there is an opportunity to conscientiously build data centres for the future. A careful, thoroughly researched and thoughtfully executed data centre plan should enable the acceleration of AI-driven innovation without environmental consequences. Balancing the needs of data center stakeholders and concerned citizens is possible.
Three effective approaches to address the current data centre energy challenges include:
Liquid cooling and heat reuse replaces traditional air blowing to absorb and transfer data centre heat. Instead of venting that heat into the atmosphere, it is repurposed for other needs in the building or distributed to a broader network to lower the energy needs of nearby businesses or residences.
Co-locating data centres on the same or an adjacent renewable energy site such as a wind or solar farm draws less power from the grid. Rather than taking power that would be used by the local community, the data centre has its own source.
Predictive optimization technology uses AI and machine learning to anticipate and address potential data centre issues such as overheating before they have a negative impact on operations.
This reduces energy loss from inefficient airflow and cable degradation. It also captures usage and heating data that shows real-time issues and spots trends over time. This data can support more efficient load balancing, maintenance scheduling and overall equipment optimization.
Introducing new methodologies and technologies into fresh data centre builds or retrofitting existing buildings is only part of the solution, however. Supporting AI means rethinking the fundamental design of the data centre building because there are still gaps that squander energy through inefficient airflow, cable degradation and reliance on time-consuming manual installation and equipment checks.
One reason for this is that traditional computing workloads have changed. Where CPUs (central processing units) were once the default standard for running workloads, they are fading into the background as today’s AI demands higher powered, energy-intensive GPUs (graphics processing units). Accommodating the new GPU hardware often requires installing new equipment, which can take months and must be followed by a manual safety inspection process.
These inspections rely on the human eye and handheld infrared cameras that scan an area to capture temperature readings. As AI generates heat loads that are denser, less stable and can shift, it is difficult to determine precisely where the most heat is being generated. This is due to the lack of visibility into data centre buildings’ thermal energy performance and limits.
As a result, these manual inspections cannot keep up with the need for continuous monitoring of the entire data centre for issues such as overheating. A lack of skilled data centre professionals compounds these challenges. This is one of the biggest and most overlooked risks in data centre management and construction today.
The Uptime Institute reports that half of data centre owners and operators are tracking the metrics needed to assess their sustainability and meet regulatory requirements. Data centres are also now capturing thermal energy performance using thermal-based spatial intelligence.
This approach can spot anomalies such as uneven heat distribution or hot spots that would otherwise go unrecognized by the human eye or would not be detected by an algorithm until the situation becomes critical.
As a sensor without a camera, thermal-based spatial sensing provides data centre operators with real-time insight on energy consumption and heat output, down to the most granular levels. When this data is integrated into a dashboard depicting building performance, it helps ensure energy capacity flows only where it is needed.
These insights can lead to faster responses and more strategic planning based on a more accurate depiction of data centre conditions. This also solves a precision issue that emerges when digital twins, virtual replicas of a facility’s physical infrastructure, are used. The digital twin models are only as accurate as the sensor data input. Without continuous, physical sensing, the twin reflects intent versus reality, which can lead to costly surprises.
Spatial intelligence insight, as part of a comprehensive data centre efficiency strategy, can reduce energy demand. Peer-reviewed research confirms that usage-based building controls can reduce energy consumption by 20-30% by aligning cooling output to actual demand. This would help to balance a data centre's needs with those of its neighbours. It would also help data centres to show any meaningful reductions in resource consumption.
It is the responsibility of those that build and maintain data centres to optimize the infrastructure for energy efficiency. Only then can we have more productive conversations about planning the data centres of the future.
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