Estimate an AI data center in separate pieces: first define whether capacity means IT load or utility service, then calculate equipment electricity, add facility overhead using a stated PUE or a detailed engineering model, and price construction against a dated benchmark with its exclusions made explicit. Keep active IT equipment, utility work, land and electricity bills separate from shell-and-core construction. Without a site, design, equipment schedule, tariff and procurement assumptions, the result is a planning range—not a project quote.
What must be defined before estimating?
A data-center estimate is only comparable to another estimate when the two use the same boundary and basis. A stated “10 MW data center,” for example, could mean 10 MW of IT equipment load or 10 MW of utility service capacity; those are not interchangeable. Record the assumptions below before calculating.
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- Location and delivery year: country, market, expected construction or operation date, and currency. Labor, equipment availability and utility conditions vary by location and time.
- Capacity basis: target IT load in MW, utility service capacity, or both. State whether a capacity figure is peak design capacity or expected average demand.
- Project type: single-tenant, colocation or enterprise facility, and whether the estimate covers a new build or an expansion.
- Computing design: AI workload, accelerator and server types and quantities, storage and networking, expected utilization, and idle power.
- Facility design: air or liquid cooling, redundancy and reliability requirements, and any relevant climate or site constraints.
- Cost boundary: list whether shell and core, mechanical/electrical systems, site work, utility interconnection, land, professional fees and active IT equipment are included.
A megawatt figure alone cannot determine either energy use or capital cost: equipment mix and operating profile affect electricity, while location, cooling, redundancy, schedule and scope affect construction price.
How do you estimate electricity use?
Build an equipment and operating schedule
List servers and accelerators, storage and networking equipment, with quantities and expected power draw while operating. Model both utilization and idle draw across the year; multiplying nameplate or peak capacity by every hour can overstate consumption when equipment is not continuously at full load. For a first-pass estimate, derive average IT power from the time-weighted operating profile, then multiply by operating hours.
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Annual IT energy (kWh) = average IT load (kW) × operating hours. For a full year, 8,760 hours is the usual non-leap-year assumption. Convert kWh to MWh by dividing by 1,000, or to GWh by dividing by 1,000,000.
Convert IT energy to facility energy
Power Usage Effectiveness (PUE) is total facility energy divided by IT-equipment energy. Use it as a multiplier, not as a percentage saving:
PUE = total facility energy ÷ IT-equipment energy
Estimated facility energy = IT energy × assumed PUE.
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PUE accounts for facility overhead such as cooling and power distribution relative to IT energy. It does not measure the computing performance or energy efficiency of the AI workload itself, and it does not capture every water-use or supply-temperature trade-off. For a conceptual estimate, state the PUE assumption and why it is appropriate. For a design estimate, model power conversion and cooling for the actual climate, facility configuration and operating conditions.
Read published PUE figures as different kinds of evidence
| Figure | What it represents | How to use it |
|---|---|---|
| 1.145 for 2024; modeled 1.136 for 2030 | Berkeley Lab’s 2026 report estimate of average national PUE for facilities serving AI equipment. | A modeled U.S. national estimate and projection, not a guaranteed value for a new facility. |
| 1.56 in 2024 | Uptime Institute’s 2024 survey industry average, based on responses from 526 operators about their largest data center. | A survey result for a different population and method; not directly interchangeable with Berkeley Lab’s AI-facility estimate. |
Neither number is a design promise. Uptime notes that climate and business objectives vary, and PUE does not account for IT-system energy performance or water use and supply-temperature trade-offs. Do not choose a project PUE simply by selecting the lowest published figure.
Illustrative calculation
Suppose, purely to demonstrate the arithmetic, a facility averages 10 MW of IT load throughout a non-leap year and the estimator assumes PUE 1.30. IT energy is 10,000 kW × 8,760 hours = 87,600,000 kWh, or 87.6 GWh. Estimated facility energy is 87.6 GWh × 1.30 = 113.88 GWh. The 10 MW average-load and 1.30 PUE values here are illustrative assumptions, not a benchmark or forecast. If 10 MW instead denotes peak IT capacity, first estimate the time-weighted average load from the operating profile; do not treat peak capacity as continuous average demand.
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What do national electricity forecasts tell you?
National forecasts provide context for the scale and uncertainty of data-center demand, not a substitute for a project equipment schedule. Berkeley Lab’s 2026-published U.S. data-center energy update gives a 2030 reference case of 649 TWh. Its sensitivity cases are 578, 664, 590 and 782 TWh, reflecting alternative assumptions about equipment installations, specialized-chip shipments, AI-chip lifetime, and AI-server idle power and utilization. Its compounded uncertainty range is 521–843 TWh in 2030.
These are model-derived estimates, not a count of every meter or a projection for one AI facility. The range illustrates how equipment shipments, lifetimes and operating assumptions can materially change a national total. A 2024 DOE/Berkeley Lab report estimated 176 TWh—about 4.4% of U.S. electricity—in 2023 and projected 325–580 TWh for 2028; that earlier work is useful historical context, while the 2026 update is the newer forecast.
How do you estimate capital cost without confusing scope?
Separate the cost lines
Use a benchmark matched to the project’s market, size, type, delivery date and cooling design. Before adding subtotals, mark each line as included, excluded or unknown. At minimum, separate:
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- Shell, core and architectural work.
- Mechanical and electrical fit-out and equipment.
- Contractor preliminaries, margin and contingency.
- Client-direct costs and site works, including abnormal groundworks where relevant.
- Utility works and interconnection.
- Land and professional services.
- Active IT and technology fit-out, including servers and accelerators.
A shell-and-core benchmark is not an all-in AI data-center budget. If an item is excluded from a benchmark, do not silently treat it as included in the resulting total. Unknown costs should remain visible as unknown rather than being hidden inside a per-MW figure.
Use construction benchmarks as scope-limited reference points
| Benchmark | Published figure or basis | Key scope qualification |
|---|---|---|
| JLL Research, 2026 outlook | Global average shell-and-core construction cost of $10.7 million per MW in 2025; 2026 forecast of $11.3 million per MW. | Land and active IT are excluded. JLL’s estimate that AI technology fit-out may cost as much as $25 million per MW is a separate possible tenant technology cost, not an automatic addition for every project. |
| Turner & Townsend, 2025 methodology | Modeled baseline: air-cooled, build-to-suit hyperscale facility with 30–50 MW of IT load. The methodology draws on more than 300 live or recent projects in more than 20 countries. | Cost headings include shell/core, architectural fit-out, mechanical/electrical fit-out and equipment, contractor preliminaries, margin and contingency. It excludes client-direct costs, land, utility works, abnormal groundworks, site works, active IT equipment and professional services. |
JLL’s figures illustrate how to apply a per-MW benchmark, not what a particular facility will cost. For example, multiplying its 2025 global average of $10.7 million per MW by a hypothetical 10 MW yields $107 million for the benchmarked shell-and-core scope; using its 2026 forecast of $11.3 million per MW yields $113 million. Those arithmetic examples do not include the excluded items and do not establish that a 10 MW project matches the benchmark’s capacity or design assumptions. JLL’s geographic examples vary substantially, so use the relevant market figure and confirm capacity and cooling assumptions before applying it.
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How do you estimate the electricity bill?
After estimating facility energy, multiply it by a site-appropriate electricity price. State whether the price is an all-in average or an energy-only rate. Depending on the tariff and contract, a bill may also include demand charges, taxes, procurement or contract charges, and other components.
Indicative energy expense = facility energy (kWh) × assumed energy price per kWh. Keep any demand charges and other tariff components on separate lines where possible. The published sources cited here do not establish a tariff for a particular site, so they cannot support a project-specific bill by themselves. Build low, base and high scenarios from explicit utilization and price assumptions instead of presenting one certain operating-cost figure.
What inputs turn a first-pass estimate into a project estimate?
A more reliable estimate requires site and design information that national averages cannot supply. Before relying on a capital total or electricity bill, assemble:
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- Whether each MW figure means IT load or utility capacity, and whether it is peak or average.
- Equipment quantities, power profiles, utilization, idle draw and replacement assumptions.
- Cooling design, climate inputs, redundancy and reliability targets.
- A cost breakdown identifying inclusions, exclusions and unknowns, including utility and interconnection scope.
- Applicable tariff, contract and procurement assumptions, including whether quoted prices include demand charges and taxes.
Berkeley Lab describes a bottom-up energy and water modeling approach that uses planned IT-equipment shipments, per-device annual electricity assumptions, cooling simulations, and facility type and location information. Its MOSTCOOL simulation toolkit is relevant to detailed power and thermal design, but a conceptual estimate still needs project-specific inputs.
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