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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsShort answer: A data center is often one of the largest single electricity customers in a community, but there is no national figure that can tell you whether a particular facility uses more power than the factory, hospital, university, or water utility next door. The defensible comparison uses the same reporting year and facility boundaries, then examines annual energy (MWh), peak demand (MW), hourly and seasonal load shape, flexibility, and—when buildings are being compared—electricity per square foot.
The best current national estimate from Lawrence Berkeley National Laboratory puts U.S. data-center electricity use at 192 TWh in 2024, or 4.7% of total U.S. electricity consumption. That is a modeled national total, not a meter reading for every site and not a local ranking. To answer a community question, pair national scale with local utility, facility, and rate data.
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What the latest U.S. estimate says
Lawrence Berkeley National Laboratory’s June 2026 United States Data Center Energy Usage Report: 2025 Update estimates that U.S. data centers consumed 192 terawatt-hours (TWh) in 2024. That equals 4.7% of all U.S. electricity consumption. The report estimates that use grew 14% from 2023 to 2024.
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The same report gives a 2030 reference case of 649 TWh, or 11.8% of forecast U.S. electricity use. Its compounded uncertainty bounds for 2030 are 521 to 843 TWh. Those values are projections, not a guarantee. The model draws on planned information-technology equipment shipments, per-device electricity use, cooling simulations, and information about facility types and locations. Cryptocurrency-mining electricity is outside the report’s scope.
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| Measure | Value | How to interpret it |
|---|---|---|
| U.S. data-center electricity, 2024 | 192 TWh | Modeled national total from Lawrence Berkeley National Laboratory, June 2026 |
| Share of U.S. electricity, 2024 | 4.7% | National share, not the share of a particular utility territory |
| Change from 2023 to 2024 | 14% growth | National estimate for that one-year period |
| 2030 reference case | 649 TWh, or 11.8% | Projection based on the report’s reference assumptions |
| 2030 compounded uncertainty bounds | 521–843 TWh | Range around the projection, not three measured outcomes |
A national percentage cannot answer whether one proposed facility is larger than one local steel mill or hospital. It establishes why data centers matter to national planning; local conclusions require local measurements or clearly documented models.
Why “how much electricity” has several answers
People use “electricity use” to mean different things. A useful comparison states which of these questions it is answering.
Annual energy: MWh or TWh
Annual energy is the total electricity consumed over a stated period. One megawatt-hour (MWh) equals 1,000 kilowatt-hours; one terawatt-hour equals one million MWh. Annual energy shows the total burden on the system over a year and is the right measure for comparing yearly consumption.
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For a local comparison, request a full calendar year of interval-meter data or a transparent model for each facility. State whether the figures include tenant loads, common areas, cooling, backup systems, and electricity generated and consumed onsite.
Peak demand: MW
Demand is power at a moment, normally reported as the highest metered interval in megawatts (MW). Two facilities can use the same annual MWh while creating very different peaks. A high peak can require transformers, substations, feeders, generation, or transmission capacity even when annual energy is moderate.
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Always record the interval length, the billing or settlement convention, and the date and time of the peak. A facility with a steady 100 MW load and one that reaches 100 MW for a short period do not create identical planning needs.
Load shape and flexibility
Plot hourly or sub-hourly demand across a year. Look for a flat baseload, weekday or seasonal swings, and the ability to reduce demand during system stress. Flexibility can change the infrastructure and market impact of a large load without changing its annual MWh.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Some large loads in Texas’s ERCOT system have entered voluntary curtailment agreements. That is a regional example, not evidence that every data center can or will curtail. A comparison should identify the actual contract, notice period, duration, frequency, and operational limits.
Building electricity intensity
For building comparisons, divide electricity use by conditioned or gross floor area and identify the building category and survey year. EIA’s historical analysis of 2012 Commercial Buildings Energy Consumption Survey data found that office buildings containing data centers had higher electricity intensity than offices without them; the measured difference varied by building size.
That study is useful for understanding building intensity, but it is dated and does not represent current hyperscale facilities. It should not be presented as a present-day average for all data centers.
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How data centers compare with broad U.S. sectors
The U.S. Energy Information Administration’s 2025 retail-sales figures provide national context:
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →| Sector | 2025 retail electricity sales | Share of retail sales | What the figure represents |
|---|---|---|---|
| Residential | 1.51 trillion kWh | 37.3% | Many household customers combined |
| Commercial | 1.49 trillion kWh | 36.8% | Many commercial customers combined |
| Industrial | 1.04 trillion kWh | 25.7% | Many industrial customers combined |
| Transportation | 0.01 trillion kWh | 0.2% | National retail-sales category |
These are sector totals, not the consumption of one factory, hospital, university, or data center. They cannot be divided into a meaningful “average facility” without additional assumptions. EIA also notes that electricity generated and consumed directly at industrial and commercial facilities is additional to retail sales. A comparison must therefore define whether it covers utility purchases only or includes onsite generation and direct use.
How to compare a data center with a factory, hospital, university, or water utility
No named community’s measured data-center, hospital, factory, university, and water-system consumption is established by the national figures above. A defensible local ranking needs comparable records for every facility.
Factories
Industrial processes can be highly variable. A plant may operate continuously, run multiple shifts, or shut down for maintenance. Ask for annual MWh, maximum import and export demand, production schedule, and any process loads supplied by onsite generation. Do not compare a data center’s utility purchases with a factory’s purchases while ignoring electricity the factory produces and consumes onsite.
Hospitals
Hospitals generally have critical, around-the-clock services and backup systems. For a fair comparison, include the hospital campus boundary, medical-office buildings, parking structures, central plants, and leased spaces consistently. Record normal and emergency operating modes separately if the meter data make that possible.
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Universities
A university’s load may span classrooms, laboratories, housing, athletics, hospitals, and research facilities. Define whether the comparison covers the entire campus or only academic buildings. Academic calendars can produce large seasonal and weekly changes, so annual MWh alone can hide a different peak pattern from a continuously operating data center.
Water and wastewater utilities
Pumping and treatment loads often follow demand, rainfall, storage levels, and regulatory operating requirements. Include treatment plants, wells, booster stations, and distributed assets consistently. Document whether emergency pumping or onsite generation changes the utility’s imported peak.
A practical method for a community-level comparison
Use the following sequence when evaluating a proposed or existing facility.
- Define the question and year. Decide whether the purpose is annual consumption, peak-capacity planning, emissions accounting, rate design, or building efficiency. Use the same 12-month period for every facility.
- Set the boundary. List every building, tenant, central plant, cooling system, backup generator, battery, and onsite generation asset included. Keep retail purchases separate from electricity generated and used behind the meter.
- Collect annual energy. Obtain interval-meter records or a documented model. Sum the intervals using the same units and calendar. A basic conversion is: annual MWh = the sum of interval kW multiplied by interval hours, divided by 1,000.
- Collect peak demand. Record the highest interval kW, convert to MW, and note the timestamp, interval length, weather conditions if relevant, and whether the peak was coincident with the utility system peak.
- Describe the load shape. Show hourly or sub-hourly demand by month. Identify baseload, seasonal peaks, ramp rates, planned outages, and curtailment capability.
- Normalize building intensity where appropriate. Divide annual kWh by a consistently defined square-foot area. Label the building type and survey year; do not compare a partial office floor with a complete industrial campus.
- Document onsite supply. Identify generators, solar, storage, cogeneration, and direct-use electricity. State whether each figure is gross consumption, net utility imports, or total load before onsite supply.
- Ask the utility about capacity and upgrades. Request the available feeder and substation capacity, proposed interconnection work, construction schedule, contribution requirements, and the tariff’s demand and standby provisions.
- Publish uncertainty. Mark measured values, engineering estimates, scenarios, and forecasts separately. Give a range when equipment deployment, occupancy, production, or construction timing is uncertain.
The result should be a table that lets a reader see what is actually comparable:
| Facility | Reporting year | Annual energy (MWh) | Maximum demand (MW) | Peak timing | Flexible or curtailable load | Boundary and onsite supply |
|---|---|---|---|---|---|---|
| Data center | State the year | Measured or modeled value | Measured or modeled value | Date, time, and interval | Contractual capability, if any | Buildings and supply assets included |
| Factory | State the year | Measured or modeled value | Measured or modeled value | Date, time, and interval | Process limits and shutdown rules | Plant boundary and onsite supply |
| Hospital, university, or water utility | State the year | Measured or modeled value | Measured or modeled value | Date, time, and interval | Critical-load constraints | Campus or system boundary and onsite supply |
Could one data center use as much electricity as a small city?
That depends on what “small city” means and which measure is being compared. A city is a collection of residential, commercial, industrial, and public customers; a data center is a single customer or campus. Without a stated city, year, accounting boundary, and household or municipal-use assumption, a household-equivalent slogan is not a reliable measurement.
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The safer scale comparison is the one EIA used in its October 2024 ERCOT analysis: Some of the larger facilities can consume as much electricity as a medium-sized power plant.
The statement concerns some large facilities and a regional large-load context. EIA described ERCOT large flexible loads as facilities with expected peak capacity of at least 75 MW and discussed voluntary curtailment agreements at some sites. Its numerical outlook was a forecast made in 2024 for 2025, not a current measurement of every ERCOT facility.
What the comparison means for the grid and ratepayers
A large electricity customer can bring investment and new demand while also requiring careful planning. Berkeley Lab’s January 2025 rate-design brief identifies five questions for utilities and regulators:
- Cost allocation: Which customer, customer class, or broader rate base pays for substations, feeders, transmission, generation, and other upgrades?
- Stranded-asset protection: What happens if a planned facility is delayed, downsized, or never reaches its forecast load after the utility builds infrastructure?
- Operational and resource adequacy: Can supply meet the new load during system peaks, outages, extreme weather, and fuel or transmission constraints?
- Technology risk sharing: How are the risks of newer generation, storage, efficiency, and other supply technologies divided among the utility, customer, and ratepayers?
- Large-customer requirements: How should tariffs accommodate requests such as carbon-free matching, onsite generation, special reliability, or unusual ramping and curtailment needs?
These are planning and rate-design questions, not proof that every data center raises electricity bills or causes shortages. The outcome depends on the utility territory, tariff, timing, available capacity, interconnection agreement, and who pays for reserved infrastructure.
Construction and planning implications
For a construction or community-planning decision, electricity comparison should begin before final site and equipment commitments. Ask for a utility capacity study and preserve the assumptions used in the load forecast. A design that reaches a stated peak in phases should show each phase’s energization date, interim demand, and ultimate demand rather than presenting only the final number.
Project documents should distinguish the facility’s IT or process load from the total site load, which also includes cooling, pumps, lighting, offices, security, and other systems. They should identify normal, emergency, maintenance, and commissioning conditions and show how temporary construction power is treated. Those distinctions make it possible to compare a data center with another large user without mixing construction-period demand, standby capacity, and routine operations.
For community review, publish the annual MWh estimate, maximum MW, expected peak months and hours, flexibility commitments, onsite supply, interconnection scope, and the cost-allocation mechanism. If any figure is a forecast, include the assumptions and a low and high case. This approach gives residents and decision-makers information they can check rather than an unsupported household-equivalent claim.
Common comparison mistakes
- Confusing MW and MWh: MW is instantaneous or interval demand; MWh is energy accumulated over time.
- Comparing a single facility with a national sector: EIA’s residential, commercial, and industrial totals combine millions of customers.
- Using different boundaries: A campus total, a leased building, and a utility account are not interchangeable.
- Ignoring direct-use electricity: Retail-sales totals can omit electricity generated and consumed at an industrial or commercial site.
- Treating a forecast as a measurement: The 2030 Berkeley Lab values are scenarios with uncertainty, not guaranteed consumption.
- Using dated building data as a current hyperscale benchmark: The office comparison is based on 2012 CBECS data.
- Generalizing a regional flexibility example: ERCOT curtailment agreements do not describe all data centers or all utilities.
- Converting to households without an assumption: A household equivalent is illustrative only when the local or national household-use basis is stated.
- Inferring other impacts from electricity alone: Jobs, tax revenue, water use, emissions, and retail rates require separate evidence.
The decision rule
Use the national estimate to understand scale: U.S. data centers consumed 192 TWh in 2024, and the 2030 reference case is 649 TWh with a 521–843 TWh uncertainty range. Use local, same-year facility data to decide whether a particular data center is larger than a particular factory, hospital, university, or water utility. Rank facilities separately by annual MWh, peak MW, timing, flexibility, and electricity intensity. Only then examine grid upgrades, tariff terms, and cost allocation.
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