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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsCompare data center energy estimates only after matching their geography, year, metric, system boundary, method and uncertainty. The latest U.S. estimate located here puts data center electricity use at 192 TWh in 2024; a separate global estimate is about 415 TWh for the same year. Those are not competing answers: they cover different geographies and should be labeled accordingly.
What do the latest estimates say?
Lawrence Berkeley National Laboratory (LBNL) estimates that U.S. data centers used 192 terawatt-hours (TWh) of electricity in 2024, equal to 4.7% of total U.S. electricity consumption. Its 2025 update, published in 2026, covers servers, storage, network equipment and supporting infrastructure, and excludes cryptocurrency mining. The report treats 2024 as its last historical year because its main IT shipment data extend only through the first three quarters of 2025. Read the LBNL report.
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For the world, the International Energy Agency (IEA) estimates data centers used about 415 TWh in 2024, or 1.5% of global electricity consumption. The IEA’s 2025 base case projects about 945 TWh in 2030. This is a global estimate, not a U.S. figure or an alternative measurement of the LBNL total. Read the IEA analysis.
How should you compare two estimates?
Before comparing numbers, write down what each one measures. A useful comparison keeps the following dimensions visible:
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- Geography: Is the estimate for the United States, the world, a region or one facility?
- Year and status: Is the figure a historical estimate, a current-year estimate, a forecast or a scenario? A forecast year is not a measurement of current consumption.
- Metric: Does it report electricity consumption in TWh, power capacity in gigawatts (GW), or total primary energy? These are different quantities and cannot be substituted for one another.
- System boundary: Does the total include only IT equipment, or also cooling and other facility infrastructure? Are cryptocurrency-mining operations included?
- Method: Is the number metered, modeled from equipment and operating assumptions, or assembled another way?
- Scenario and uncertainty: Is the number a reference or base case, a sensitivity result, or a high/low uncertainty bound?
- Publication vintage: When were the underlying data and assumptions collected, and has a later edition revised earlier years?
If any of these differ, the figures may answer different questions. Do not rank them as though one must be wrong simply because the totals diverge.
What do the U.S. and global forecasts mean?
LBNL’s U.S. 2030 reference case is 649 TWh. Its compounded uncertainty bounds are 521–843 TWh. Those bounds express the breadth of modeled uncertainty; the reference case is not a guaranteed outcome, and the midpoint of the bounds is not a prediction that consumption will reach. The estimate belongs to LBNL’s U.S. scope and model. LBNL’s 2025 update describes the forecast and its sensitivity cases.
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The IEA’s global 2030 base case is about 945 TWh. It is useful alongside LBNL’s forecast only when the geography and scenario labels remain attached. The IEA notes, “There is substantial uncertainty both about data centre consumption today and in the future.” Its scenario framing identifies efficiency, AI uptake and bottlenecks among the factors that shape demand. IEA, Energy and AI.
How are national estimates built?
LBNL uses a bottom-up model rather than a single meter reading of every data center. It combines equipment shipments and installed stock with assumptions about equipment power and operation, estimates IT electricity use, then applies a modeled power usage effectiveness (PUE) to account for facility electricity beyond IT equipment. LBNL describes the approach as a “bottom-up” energy-use model. The report explains its modeling approach.
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This approach can estimate a national total where comprehensive facility-level metering is not the method being reported, but the output depends on the inputs. A model-based national estimate is not the same thing as a directly metered national total. A facility operator measuring one site, meanwhile, is answering a narrower question than a national model.
Which assumptions can change the answer?
Equipment shipments and installed stock
A bottom-up forecast depends on how many servers and accelerators are installed, and how quickly new equipment enters service. LBNL tests lower equipment installations and greater use of specialized graphics chips in separate sensitivity cases. Different shipment data or deployment assumptions can therefore change the projected total. See LBNL’s sensitivity analysis.
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Power draw, utilization and idle operation
A server’s rated wattage is not its annual energy use. The LBNL model considers rated and maximum power, utilization and idle power to estimate average server wattage over time. How heavily equipment runs—and how much electricity it draws when less busy—can shift an estimate, especially for AI servers. Treating a nameplate rating as if it were continuous measured consumption skips those operating assumptions.
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Forecasts also depend on how long accelerators stay in service and how much hardware or software improvements reduce energy per task. LBNL tests a shorter assumed AI-chip lifetime as a sensitivity. The IEA identifies efficiency and AI adoption among its uncertain demand drivers. A forecast that assumes rapid efficiency gains may differ substantially from one assuming deployments grow faster than efficiency improves.
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Facility overhead and PUE
IT equipment is only part of a data center’s electricity use. Cooling and other infrastructure add facility overhead, which LBNL models with PUE. Comparing a total that includes these systems with an estimate that counts IT equipment alone produces an apples-to-oranges comparison. Check whether the value is IT-only or whole-facility electricity before interpreting a difference.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why can an older estimate disagree with a newer one?
A revised historical figure does not automatically mean data centers used more or less electricity in the past. In its 2025 update, LBNL revised past estimates in part because reported GPU shipments and assumptions about AI inference-server power changed. That is a change in the estimate’s data or method; it should not be confused with a newly observed change in consumption. When quoting a number, include the report year or edition as well as the consumption year. LBNL’s update documents the revised estimates.
A quick test for a potentially misleading claim
- Find the original source. Check whether the number is attributed to LBNL, the IEA or another source, rather than repeated without context.
- Attach the geography and year. For example, “U.S., 2024 estimate” is materially clearer than “data centers use 192 TWh.”
- Check the unit and boundary. Confirm that the claim means electricity consumption, and whether it includes facility infrastructure or excludes crypto mining.
- Identify the method and scenario. Separate a modeled estimate or forecast from metered consumption; distinguish a reference case from an uncertainty range.
- Keep the report vintage visible. A newer edition may have revised past estimates because its data or assumptions changed.
- Retain the uncertainty range when provided. Quoting only a reference case can hide how much outcomes vary across modeled assumptions.
A number that omits these labels may still be useful as a rough pointer, but it is not enough to establish a like-for-like comparison.
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