Choose an AI data centre location by proving that it can support the workload’s power, cooling, network, water, resilience and growth requirements on the required schedule. Start with utility-confirmed capacity and the workload itself—not a map pin near a substation or a major city. Treat power availability, workload fit and legal or environmental feasibility as gates: a strong score elsewhere cannot compensate for a site that fails one of them.
Start with the workload, not a generic “AI data centre” brief
Before comparing sites, write down what the facility must run and when it must be ready. AI training, inference, batch processing and archive workloads can have different needs for latency, data movement, density and expansion. An assumed “AI” profile is not a sufficient design basis.
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- IT load and growth: Specify the initial load, expected ramp and later expansion. Include the anticipated rack-density trajectory, not just a facility-wide megawatt target.
- Thermal and electrical design: Establish the equipment’s power distribution and thermal requirements, cooling approach and design assumptions with the engineering team.
- Service requirements: Set availability targets and identify what redundancy the application actually needs.
- Data and network needs: Identify where users and data are, required latency, data-transfer volumes and any residency constraints.
- Schedule: Set the target date for usable capacity, accounting for utility interconnection and critical equipment as well as building work.
| Workload pattern | Location question to resolve | What to verify |
|---|---|---|
| Interactive inference or other user-facing services | Does the site meet the service’s latency and data-residency requirements? | Measure route latency and confirm the actual user distribution, carrier paths and applicable data constraints. |
| Model training and other large jobs | Can the work be placed regionally without making data movement impractical? | Confirm bandwidth, transfer time and cost with the application and network teams. |
| Batch processing, archiving and back-office functions | Can the workload use a regional location without harming its service requirements? | Check data access, transfer and recovery needs rather than assuming proximity to a city is essential. |
A New South Wales Net Zero Commission inquiry submission specifically argues that regional sites merit consideration for workloads such as AI model training, batch processing, data archiving and back-office functions when they are not latency-sensitive. That is a policy submission, not a universal engineering rule or a binding approval criterion. There is no universal latency threshold established for these workload categories; assess each service against its own requirements.
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Ask the utility about the capacity it can actually deliver to the proposed site and when. Physical proximity to a transmission line or substation does not establish that the required capacity is available, or that it will arrive on schedule. ASHRAE’s AI Data Center Energy Performance Framework identifies reliable, high-capacity power as a dominant constraint in many regions and recommends early utility coordination.
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Request evidence for each candidate, including:
- Utility-confirmed capacity and the assumptions behind it.
- Interconnection studies, milestones and estimated dates, plus known grid constraints.
- Substation capacity, planned upgrades and the utility’s expansion plans.
- Lead-time assumptions for critical equipment such as transformers and switchgear.
- Options for independent grid feeds and the proposed backup-power design.
ASHRAE’s detailed site-planning guidance warns that interconnection delays may exceed construction timelines. Treat utility milestones and equipment delivery assumptions as schedule risks to validate—not as incidental details to resolve after selecting a parcel. Ask the utility and project team to identify dependencies, uncertainty and contingency plans for the required delivery date.
Assess cooling and water as one decision
Cooling affects both the facility’s energy use and its demand on local water resources. Compare cooling approaches against the workload’s density, the local climate and actual water availability; do not select a system on an efficiency metric alone.
For each candidate, check basin water stress, seasonal availability, source reliability, wastewater infrastructure and whether reclaimed water is available. Establish how direct water use and water associated with electricity generation will be accounted for. The ASEAN guide recommends water-stress classification during siting and permitting, transparent water accounting and consideration of low-water, closed-loop or heat-reuse approaches where suitable.
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The same guide cautions that evaporative cooling can lower energy use while raising water use and pressure on municipal supplies. It also notes that non-potable water cannot sensibly be mandated where reclaimed-water networks do not exist. Confirm local infrastructure and permits rather than treating a preferred water source as guaranteed.
For thermal design, ASHRAE points planners to TC 9.9 Thermal Guidelines for Data Processing Environments, which provides recommended and allowable environmental envelopes. Engineers should use the applicable current edition and the actual equipment requirements; the envelopes are inputs to cooling and climate planning, not a substitute for site-specific engineering.
Compare connectivity, resilience, land and approvals
Once a candidate can meet workload, power and cooling requirements, compare the infrastructure and constraints that determine whether it can operate and grow reliably. Use evidence specific to each parcel and jurisdiction.
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- Wide Application: The server rack wall mount maximizes the use of available space, suitable for retail venues, classrooms, offices, and other places where space is limited.
| Decision area | Evidence to collect | Why it affects the choice |
|---|---|---|
| Network | Carrier access, diverse fibre routes, bandwidth, route latency, user locations and data movement requirements. | Proximity matters for latency-sensitive services; some training and batch workloads may have more regional flexibility, subject to transfer needs. |
| Hazards and operational resilience | Flood, seismic, wildfire, heat and humidity exposure; independent feeds; backup and recovery design; network route diversity. | Hazards and interruptions affect design, availability and operating risk. |
| Land and expansion | Buildable area, zoning, access, expansion parcels, and room for substations and mechanical systems. | Phased growth can require more than space for the first building; ASHRAE recommends planning for changing density and cooling needs. |
| Permits and community | Zoning and environmental approvals, water permits, noise and visual impacts, public engagement and a credible approval timeline. | Permitting and community concerns can affect viability, acceptance and time to market. |
| Workforce and local conditions | Workforce availability, jurisdiction-specific incentives and the conditions attached to them. | These can affect delivery and economics, but incentive claims need local verification. |
For resilience, test whether the proposed power, cooling and network arrangements are sufficiently independent for the availability target. A second connection is not meaningful redundancy if it shares a material failure point with the first; have the relevant engineering teams establish the design and dependencies.
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Use metrics to compare candidates, not to declare a winner
ASHRAE identifies Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), Water Usage Impact (WUI), Carbon Usage Effectiveness (CUE), Data Center Resource Effectiveness (DCRE) and Information Technology Work Capacity (ITWC) among commonly tracked metrics. They can help organize comparisons, but they do not replace local feasibility checks or a consistent basis for comparison.
For each candidate, document the workload, capacity, climate, cooling assumptions, water sources and accounting boundaries behind any reported metric. A figure measured under different assumptions may not provide a fair comparison. Include energy price structure, power-carbon profile, renewable-energy options and lifecycle resource impacts alongside the project’s site and delivery costs. Verify incentives in the relevant jurisdiction and record their conditions.
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Use a two-stage decision:
- Apply pass/fail gates. Rule out sites that cannot meet essential deliverable-power and schedule requirements, workload and cooling needs, or legal and environmental requirements.
- Score the feasible candidates. Compare the remaining sites on connectivity, resilience, expansion, resource impacts, lifecycle economics and schedule risk. Record the evidence and assumptions behind each rating rather than disguising missing information with a precise-looking score.
Put energy-demand figures in context
Sector-wide figures show why power and cooling deserve early attention, but they are not forecasts for an individual project. A Pacific Northwest National Laboratory release gives U.S. data centres’ electricity consumption as 4.4% in 2023 and projects that data centres could reach 12% by 2028. The same laboratory’s 2026 release states that cooling uses 20–40% of data-centre energy; this is a stated range, not a value established for every facility.
The European Commission’s 2026 page, citing the IEA’s Energy and AI report, puts global data-centre electricity use at about 1.5%, or 415 TWh, annually, and projects it to exceed 945 TWh by 2030, driven mainly by accelerated computing used for AI. Those global and U.S. figures have different boundaries and geographies. Use them as sector context, not as a substitute for a site-specific load forecast.
What to require before committing to a site
Before making a final selection, have the project team assemble a candidate-specific record that joins the workload requirements to utility, engineering and permitting evidence. The decision should make clear what is confirmed, what remains conditional and which dependencies could move the delivery date.
- Workload assumptions, expected IT load and growth, density, latency and data-transfer requirements.
- Utility confirmation, interconnection milestones, grid constraints, upgrade dependencies and critical-equipment delivery assumptions.
- Cooling design basis, local water availability and stress, water accounting and relevant approvals.
- Network-route diversity, hazard exposure, resilience design, buildable expansion area and permit status.
- Comparable energy, water, carbon and lifecycle-cost assumptions for the candidates that passed the feasibility gates.
This is a portfolio-level selection framework, not a site feasibility opinion. Actual utility offers, queue positions, local costs, hazard ratings, permit pathways and incentives depend on the country, jurisdiction and candidate parcels.
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