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Data-center electricity forecasts are rising, but they are not interchangeable—and none should be read as a meter reading of electricity already being used. The International Energy Agency’s global outlooks and Lawrence Berkeley National Laboratory’s U.S. estimate differ in geography, forecast vintage, scenarios, and methods. Utilities, developers, forecast authors, and regulators should disclose those assumptions and distinguish committed projects from proposals when presenting expected demand.
How large are the estimates—and what do they measure?
The figures below are projections of annual electricity consumption, expressed in terawatt-hours (TWh), not peak power demand in gigawatts. Their different geographies and report vintages matter: a global estimate and a U.S. estimate do not describe the same system, and an updated outlook is not a correction to an earlier one unless its authors say so.
| Source and case | Geography and reference point | Estimate |
|---|---|---|
| IEA, Energy and AI (2025), Base Case | Global; 2024 and 2030 | 415 TWh in 2024—about 1.5% of global electricity consumption—and around 945 TWh in 2030. |
| IEA, updated outlook in Key Questions on Energy and AI (accessed 2026) | Global; 2025 and 2030 | 485 TWh in 2025 and around 950 TWh in 2030. |
| Lawrence Berkeley National Laboratory, 2025 Update, Reference Case | United States; 2030 | 649 TWh, or 11.8% of U.S. electricity in the central estimate; the compounded uncertainty range is 521–843 TWh, corresponding to 9.5–15.3%. |
The IEA’s 2025 and updated outlooks use different reference years and report vintages. Their close 2030 central estimates do not make the underlying inputs identical. Likewise, LBNL’s U.S. Reference Case should not be compared as if it were a global total. The IEA’s updated outlook says its central projection remains close to the trajectory in its 2025 report; that does not remove uncertainty in either the underlying demand or the timing of projects.
Why can forecasts diverge?
A forecast is the result of choices about what to count and how to model it. LBNL describes a bottom-up approach using equipment shipment data, device electricity models, cooling simulations, and facility information. Its sensitivity cases test alternative data and industry feedback. Those details let readers examine how assumptions influence a result rather than treating a headline number as self-explanatory.
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The IEA uses a global model and multiple demand scenarios. When estimates differ, readers should check whether they share the same:
- Geography, baseline year, publication date, data cutoff, and forecast horizon.
- Scenario definition and treatment of efficiency gains, server utilization, and idle power.
- Facility coverage and method for estimating equipment, cooling, and other site electricity use.
- Unit: annual electricity consumption is not peak demand or a project’s requested grid capacity.
- Project status rules—whether the forecast counts announced, queued, contracted, financed, under-construction, or operating facilities.
- Percentage denominator. A share of U.S. electricity and a share of global electricity cannot be read as the same measure.
These are not technical footnotes. A forecast that includes announced projects answers a different question from one that counts only sites with firm commitments and a credible connection schedule. Without a published definition of project status, readers cannot tell how much projected load is likely, conditional, or merely possible.
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Why does local impact matter more than the global share alone?
A global share can make the issue look small while obscuring pressure on a particular grid. The IEA notes that data-center capacity and demand are geographically concentrated. As a result, the effect on local generation, transmission, and distribution can be materially different from the global average. A credible local forecast should identify where the load would connect, when it is expected to ramp up, and what infrastructure is assumed to be available.
Timelines also deserve scrutiny. The IEA estimates that around 20% of planned data-center projects could be at risk of delay if grid risks are not addressed. Its 2025 analysis says transmission lines can take four to eight years to build in advanced economies and that waits for critical grid components have lengthened. These findings underscore the gap between a proposed load and electricity that can actually be delivered on a stated schedule; they do not establish that any particular project is speculative.
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Who should be accountable for the assumptions?
No single institution controls every input. The people who produce, rely on, or approve a forecast each have a distinct responsibility:
- Forecast authors should publish the data cutoff, model boundary, scenario definitions, key assumptions, uncertainty range, and revisions from prior editions.
- Utilities and grid planners should explain how project status affects their load forecasts, what connection milestones they require, and how they account for delays or cancellations.
- Developers and large customers should provide enough consistent information for planners to distinguish a site under serious development from an early-stage proposal, while protecting genuinely sensitive details where necessary.
- Regulators and lawmakers should set clear reporting rules, check whether submissions are comparable, and ensure that forecast users can see how uncertainty informs infrastructure and cost decisions.
These are accountability standards, not claims that every jurisdiction already requires every disclosure. Existing policy examples are narrower. The IEA’s 2024 Electricity report describes EU data-center operator reporting on energy use and emissions beginning in 2024, and summarizes U.S. Energy Act of 2020 provisions for studies, metrics, good practices, and public reporting of historical data-center energy and water use. The Associated Press reported that Texas lawmakers enacted a measure requiring developers to disclose whether they had requested power elsewhere in Texas and to demonstrate substantial financial commitment to a site. The IEA and AP accounts are useful policy summaries, but exact duties depend on the current legal text and implementation; they should not be treated as a complete statement of law.
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What should a transparent forecast disclose?
A useful public forecast should make its assumptions auditable without pretending that uncertainty can be eliminated. At minimum, the accompanying explanation should answer these questions:
- What is counted? State the facility types, geographic boundary, energy measure, and whether figures represent annual consumption, peak load, or requested capacity.
- Which projects are included? Define status categories and explain what evidence qualifies a project for each category. Separate operating load from planned load and show how less-certain projects affect the total.
- What must happen for the load to arrive? Identify expected connection dates, ramp-up assumptions, and dependencies such as transmission upgrades or equipment delivery.
- How was demand estimated? Describe the data sources and modeling choices for equipment, utilization, idle power, cooling, and efficiency, along with significant exclusions.
- How uncertain is the result? Publish a range or scenarios, explain the factors that move the estimate, and avoid presenting a single central case as a guaranteed outcome.
- What changed since the last forecast? Date the data cutoff and explain material changes in project pipeline, methodology, or assumptions so readers can distinguish new evidence from a new modeling choice.
Publishing those details would not make every projection agree. It would make disagreement more useful: a policymaker, ratepayer, or grid planner could see whether it comes from different project lists, different assumptions about electricity use, or different judgments about delivery timing. That is the difference between a number that demands trust and an estimate that can be checked.
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