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Forecast AI data center power needs from the bottom up: define the site, utility territory, time horizon and decision; estimate the IT load from the planned equipment and workload; add cooling and other facility loads; then model peak demand, load shape and annual energy across multiple scenarios. A national electricity forecast can provide context, but it cannot tell you how many megawatts a particular facility will need.
Start by defining what the forecast must answer
Before estimating demand, specify the boundary and the output. A forecast for one facility is different from a utility’s regional forecast or a national outlook, and a number in megawatts is not interchangeable with a number in megawatt-hours or terawatt-hours.
- Boundary: Name the facility or fleet, its geography and utility territory, and which loads are included.
- Horizon: State the base year and the planning years. A commissioning decision and a long-range grid plan may need different horizons.
- Decision: Identify whether the estimate supports an interconnection request, power procurement, equipment design, or an operating plan.
- Metric: Decide whether you need peak or contracted power, time-varying demand, annual energy, or all three.
Power describes the rate of electricity use at a point in time; energy describes electricity consumed over a period. Annual energy totals alone do not reveal the highest load a site or grid connection must serve.
Build the estimate from equipment and workload
Inventory the IT load
List the planned server and accelerator types, quantities, deployment dates, expected utilization and workload mix. Separate AI-focused accelerated servers from conventional servers rather than applying one growth rate to every server class. Hardware shipments, demand and supply constraints can affect when equipment is installed and how much capacity is available; the International Energy Agency (IEA) says its modeling relies on near-term industry projections for server shipments while considering those constraints.
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Translate the inventory and workload assumptions into an IT demand profile over time. Keep the assumptions visible: a forecast that assumes a full accelerator fleet on day one means something different from one that phases equipment in over several years.
Add the loads required to run the IT
Estimate whole-facility demand by adding the IT load to cooling, power delivery and other facility infrastructure. Cooling requirements depend on the facility and its design, so the non-IT portion should not be treated as a universal fixed add-on. Lawrence Berkeley National Laboratory (LBNL) describes a bottom-up national modeling approach that combines computing-equipment shipments with thermodynamic modeling of cooling; IEA component estimates likewise show that facility type and efficiency affect infrastructure overhead.
For a site-specific estimate, use the facility’s planned design and operating assumptions. The national modeling approaches help explain what to include, but do not supply the missing site design or workload schedule.
Forecast scenarios, not a single AI growth rate
AI adoption, efficiency, supply constraints and deployment timing are uncertain. Build at least three cases and make the assumptions that distinguish them explicit.
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| Scenario | What to vary | Planning use |
|---|---|---|
| Base | Expected AI uptake, equipment deployment, utilization and efficiency under the central assumptions. | Working estimate for the stated decision and horizon. |
| High-growth | Faster AI adoption or deployment, higher utilization, or less favorable efficiency assumptions. | Tests whether power procurement, equipment and interconnection plans can handle stronger demand. |
| Efficiency or deployment downside | Improved hardware or software efficiency, slower deployment, or tighter supply constraints. | Tests the consequences of lower or later demand rather than assuming planned capacity appears on schedule. |
These are useful scenario categories, not predictions that any one outcome will occur. IEA’s 2025 Energy and AI outlook uses Lift-Off, High Efficiency and Headwinds cases to represent competing assumptions. It also emphasizes substantial uncertainty in data center consumption today and in the future.
Model peak demand and load shape as well as annual energy
For facility design and operations, estimate when the load occurs, not just how much energy is consumed across a year. For utility and regional planning, retain the site locations and their time-varying profiles: demand can be concentrated in particular places, and the timing of load matters to grid planning. A fleet-wide annual total can hide a local peak or a cluster of new demand.
LBNL’s Shape Maker generates customizable electricity load profiles for data center, facility and grid planning. LBNL also describes a regional power database that categorizes data center sites by type and utility power needs. These resources address different questions: load profiles help represent timing, while regional data help characterize where power needs are located.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use published outlooks as context, not as a facility forecast
The published figures below describe different geographies, years and metrics. They are scenario results and outlooks, not guaranteed outcomes or substitutes for a site-level load model.
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| Source and date | Geography and metric | Published figure | How to interpret it |
|---|---|---|---|
| LBNL, 2025 update | United States; share of total electricity use in 2030 | 11.8%, with scenarios ranging from 9.5% to 15.3% | National electricity context, not a facility’s peak MW or annual energy requirement. |
| IEA, 2025 | Global data center electricity consumption in 2024 | 415 TWh | Annual energy, not instantaneous power. |
| IEA, 2025 Base Case | Global data center electricity consumption in 2030 | Around 945 TWh | Annual energy in the Base Case, not a guaranteed outcome or a facility-level estimate. |
| IEA, 2025 Base Case | Annual growth in electricity consumption, by server class | 30% for accelerated servers; 9% for conventional servers | Different modeled growth rates reinforce why AI-focused and conventional server demand should not be combined under one assumed rate. |
| LBNL, 2024, as reported by the U.S. Department of Energy, 2024 | United States; data center electricity use in 2023 and projection for 2028 | 176 TWh in 2023; projected 325–580 TWh in 2028 | Historical projection for context. LBNL’s 2025 update is newer; do not treat this older range as its current forecast. |
The U.S. share estimate and the global IEA energy totals should not be compared as though they were the same measure: one is a national share of electricity use, while the others are global annual consumption figures. Their geography, date, definitions and scenario assumptions differ.
Compare forecasts on the assumptions that drive them
When deciding whether another forecast is useful for your plan, check its scope and methods rather than comparing headline numbers alone.
- Geography and population: Is it a site, utility region, U.S. national estimate or global outlook? Which facilities are counted?
- Time and metric: What is the base year and horizon? Does the number describe peak power, capacity, annual energy or a share of electricity?
- Equipment and workloads: Are accelerators separated from conventional servers? Does the estimate account for utilization, shipment volumes and deployment timing?
- Efficiency and infrastructure: What assumptions are made about hardware, software, cooling and facility type?
- Resolution and uncertainty: Does the forecast include scenarios, regional detail and time-varying profiles, or only a national annual total?
Update the model when its inputs change
Publish the assumptions behind each scenario and revisit them when accelerator shipments, utilization, cooling design, facility commissioning dates or grid constraints change. The update cadence should fit the decision and how quickly its inputs change; the cited methods do not establish one fixed schedule for every operator.
LBNL’s Center of Expertise for Data Center Energy describes three research resources relevant to this work: a bottom-up national energy model, a regional data center power database and Shape Maker for customizable load profiles. They help with national estimation, geographic characterization and load-shape planning respectively; they do not by themselves provide the facility-specific workload, design, utility territory or commissioning assumptions needed for an individual forecast.
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