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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteForecast AI data center power from the equipment and workloads you expect to deploy, then add the facility systems needed to support them. Define the site, planning horizon, and whether you need peak power, a time-varying load profile, annual energy, or all three. Build a range of scenarios: accelerator adoption, utilization, efficiency, cooling, and commissioning dates can materially change the result.
What should a data center power forecast tell you?
Start with the decision the forecast must support. An interconnection request, equipment design, energy procurement plan, and operating schedule do not necessarily need the same output. Set the facility or fleet boundary, location and utility territory, forecast horizon, and the decision-maker’s required level of detail.
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- Peak or contracted power: the demand the facility may need to draw at a particular time, often expressed in MW. This matters for electrical infrastructure and grid connection.
- Load over time: demand at intervals that match the planning task, such as an hourly or seasonal profile. This helps reveal when peaks occur and how demand changes as workloads run.
- Annual energy: electricity consumed over a year, expressed in MWh or TWh. Annual energy does not, by itself, tell you the size or timing of the peak.
A sound forecast may need all three. Avoid treating a yearly energy estimate as if it were a facility’s required MW capacity.
How do you build a facility-level estimate?
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Define the site, boundary, and horizon
Identify the location and utility territory, the buildings or sites included, the forecast start and end dates, and whether the estimate covers installed capacity, expected operation, or both. Record whether the output is peak demand, a load profile, annual energy, or a combination.
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Inventory IT equipment and deployment timing
List the server and accelerator types, quantities, and expected commissioning dates. Separate AI-focused accelerated servers from conventional servers rather than applying one growth rate to every class. Estimate utilization and workload mix over time; installed equipment alone does not say how much power it will draw in operation.
Shipment outlooks can inform the timing and scale of an equipment forecast, but they are not a substitute for a site’s actual deployment plan. The International Energy Agency (IEA) says its modelling uses near-term industry projections for server shipments while considering demand and supply constraints.
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Estimate IT demand across the planning period
For each equipment class and deployment period, estimate operating demand using the equipment quantity and an appropriate power assumption, adjusted for expected utilization and workload mix. Keep the assumptions visible: accelerator uptake, operating schedule, and utilization can change the forecast independently of the server count.
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Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.Build the estimate by period rather than relying only on a single end-state number. Equipment arriving in phases produces a different demand ramp from a fleet installed at the outset.
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Add facility infrastructure and cooling
The IT estimate is not the whole-facility load. Include cooling and power-delivery infrastructure, using the facility design and operating assumptions appropriate to the site. A useful conceptual relationship is whole-facility demand = IT demand + non-IT facility demand. If you use a facility-efficiency factor such as PUE to translate IT demand into total demand, state the value, source, and conditions behind it; do not assume one efficiency value applies to every facility or operating condition.
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LBNL’s bottom-up national method combines computing-equipment shipment estimates with thermodynamic modelling of cooling. That is a useful reminder that equipment growth and facility overhead need to be modelled together, not conflated.
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Translate the estimate into peak, profile, and annual energy
Use the expected operating schedule and deployment ramp to create a time-varying load profile. Derive the peak from the periods relevant to the design or grid decision, and calculate annual energy from demand across the year. Keep the time resolution and assumptions consistent with the use case: an annual total alone cannot show whether demand is concentrated in particular hours or seasons.
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Model the site’s grid context
Apply the forecast to the actual location and utility territory. Local constraints, interconnection timing, and the concentration of data centers in particular places affect planning; a national electricity share cannot establish the capacity available to an individual site. LBNL’s resources distinguish national energy modelling, regional data center power data, and customizable facility or grid load profiles.
How should you handle uncertainty in AI demand?
Publish scenarios rather than presenting one precise number as certain. The IEA’s 2025 Energy and AI outlook explicitly notes substantial uncertainty in data center consumption and uses sensitivity cases. Its Lift-Off, High Efficiency, and Headwinds cases illustrate how adoption, efficiency, and deployment constraints can pull forecasts in different directions.
| Scenario | Assumptions to vary | What it helps test |
|---|---|---|
| Base | Expected accelerator adoption, utilization, equipment efficiency, cooling design, and commissioning schedule | The central planning case under current deployment expectations |
| High growth or accelerated adoption | Faster AI workload growth, more accelerator deployments, higher utilization, or earlier commissioning | Whether electrical capacity and grid plans can accommodate a faster ramp |
| Efficiency or deployment downside | More efficient hardware or software, slower deployment, supply bottlenecks, lower utilization, or delayed commissioning | How much planned capacity could be deferred or avoided under a slower or more efficient trajectory |
Do not change only the server count between cases. Make clear which assumptions move in each scenario, including hardware and software efficiency, utilization, cooling, supply constraints, and deployment dates. Keep the resulting peak and annual-energy outputs paired with their assumptions so a reader can tell why the scenarios differ.
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What published forecasts can—and cannot—tell you
National and global outlooks provide context for the scale and uncertainty of data center electricity demand. They are not substitutes for a facility forecast: their populations, geographies, time horizons, and metrics differ.
| Source and scope | Published figure | How to interpret it |
|---|---|---|
| LBNL, United States, 2025 update | Data centers reach 11.8% of total U.S. electricity use by 2030 in the central estimate; LBNL scenarios range from 9.5% to 15.3%. | A national share of electricity use under forecast scenarios, not a site-specific MW requirement. |
| IEA, global, 2025 outlook | 415 TWh of data center electricity consumption in 2024; around 945 TWh in 2030 in the Base Case. | Global annual energy totals. The 2030 value is a scenario result, not a guaranteed outcome or a forecast for one country or facility. |
| IEA, global Base Case, 2025 | Annual electricity consumption growth of 30% for accelerated servers, compared with 9% for conventional servers. | Growth rates for separate server classes in the IEA Base Case, not one universal rate for all facility demand. |
| LBNL estimate reported by the U.S. Department of Energy, 2024 | 176 TWh of U.S. data center electricity use in 2023, with a projection of 325–580 TWh in 2028. | An older U.S. national projection useful as historical context; LBNL’s 2025 update is newer. |
Do not combine these figures into one trend line without reconciling their geography, dates, definitions, and scenario assumptions. In particular, a projected share of national electricity use and an annual global TWh total describe different things.
Which tools can help with location and load shape?
LBNL’s Center of Expertise for Data Center Energy describes three resources relevant to different forecasting scales:
- Bottom-up national energy model: estimates national consumption using computing-equipment shipments and cooling modelling.
- Regional data center power database: categorizes sites by type and utility power needs, supporting regional analysis.
- Shape Maker: generates customizable electricity load profiles for data center, facility, and grid planning.
These tools address different questions. A national model gives broad energy context, regional power data helps locate demand, and a load-profile tool can support time-based planning. None removes the need to define a particular facility’s equipment, operating assumptions, utility territory, and forecast horizon.
When should you revise the forecast?
Maintain an assumptions register alongside the outputs, and revisit the forecast when an input that drives demand changes. Useful triggers include accelerator shipment or deployment plans, observed utilization, cooling design, commissioning dates, or new grid constraints. Set the review cadence to the project’s decision cycle rather than treating a published national forecast as a fixed operating plan.
For each revision, preserve the previous assumptions and record what changed and how that affected peak demand, load shape, and annual energy. That makes forecast changes explainable and helps distinguish a real change in expected demand from a change in modelling assumptions.
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