IEA Energy and AI Observatory (data tool)
International Energy Agency
An online platform from the International Energy Agency giving regularly updated data and analysis on the energy demand of AI and on AI applications in the energy sector.
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Overview
The IEA Energy and AI Observatory is an online data tool from the International Energy Agency (IEA). Its subject is tracking the relationship between AI and energy from two directions. This record rests on the observatory's public landing page.
The first direction is the implications of AI for energy demand, that is, energy for AI. The platform provides regularly updated data on this. The second is the applications of AI in the energy sector for efficiency, innovation, resilience and competitiveness, that is, AI for energy. For data center designers, these two themes provide context for understanding both demand-side growth and how that growth interacts with the energy system.
The observatory is developed and maintained by the IEA and draws on data contributions from partners in the energy and technology sectors. It also complements the IEA's Special Report on Energy and AI; the related PDF reports are available separately under CC BY 4.0. This structure makes the platform a living data source and not a one-off publication.
The limit of the record is that the page gives no specific data center energy figures; numbers change inside the tool and are not reproduced here. The source should therefore be used as an address for reaching current national and regional demand figures, and when figures are fixed in design documents the date the data was retrieved and the report version should be stated.
Key points
- Provides regularly updated data on AI implications for energy demand (energy for AI).
- Also covers AI applications for efficiency, innovation, resilience and competitiveness (AI for energy).
- Developed and maintained by the IEA with partner data contributions.
- Complements the IEA Special Report on Energy and AI.
- An interactive web tool; data terms follow IEA terms of use.
- The related PDF reports are CC BY 4.0 and downloaded separately.
Using it in design
- Link to the observatory from the tool's energy-context panel and take current national and regional data center demand figures from it rather than hard-coding values.
- Record the date the data was retrieved when using a figure.
- Read the special report and the observatory data together.
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Once a month: new library sources, changes to Turkish regulations and upcoming data centre events.
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Who should read it
Planners, designers and analysts who want to track data center energy demand in national and regional context.
Publication details
- Authors
- International Energy Agency
- Publisher
- International Energy Agency
- Year
- 2025
- Document language
- English
- Source type
- Government / public
- Access
- Free to read
- Licence
- Copyright retained; citation only — the publisher’s terms apply to the original document
Original licence note
Interactive web tool; data terms per IEA terms of use; cited only (the related PDF reports are CC BY 4.0 and downloaded separately).
Citation
International Energy Agency (2025). IEA Energy and AI Observatory (data tool). International Energy Agency. https://www.iea.org/data-and-statistics/data-tools/energy-and-ai-observatory
Events on this subject
- iMasons Climate Accord Summit
- Data Center World Middle East
- ELECO 2026 Elektrik-Elektronik ve Biyomedikal Mühendisliği Konferansı
All content and rights belong to the respective authors and publishers. SoSo CAD provides only the citation, its own summary and a link to the source, and is not responsible for the sources' content.
Related sources
All sources on this topic
International Energy Agency 2025
Energy and AI (World Energy Outlook Special Report)
Use the IEA component split (servers about 60%, storage about 5%, network up to 5%, cooling 7-30%) to sanity-check modelled energy breakdowns.
- The 2035 range across cases is 700-1,700 TWh; the High Efficiency Case is 20% below the Base Case.
European Commission 2026
Report from the Commission to the European Parliament and the Council on the energy efficiency of data centres in the EU - COM(2026) 500 final
Set the PUE target for new designs against the bands in the report (below 1.2 excellent, above 1.5 needs improvement) and possible future minimum-standard thresholds, noting that the 500-1000 kW class averaged 1.64.
- EU data centres consumed 68 TWh of electricity in 2024, expected to reach 114 TWh by 2030 (3.2% of EU electricity).
European Commission 2026
Annexes 1 to 3 to Commission Delegated Regulation C(2026) 3472 - EU data centre energy (PUE) and water (WUE) efficiency classes, data centre label, and amendments to Delegated Regulation (EU) 2024/1364
Translate PUE and WUE targets into A-G classes: class A needs a design PUE of 1.15 or below and WUE of 0.1 or below; the class D boundary is 1.5 and 0.6. Write the class target into the design specification.
- WUE classes: A <= 0.1; B 0.1-0.2; C 0.2-0.4; D 0.4-0.6; E 0.6-0.8; F 0.8-1.0; G > 1.0.
European Commission 2026
Commission Delegated Regulation C(2026) 3472 of 21.9.2026 supplementing Directive (EU) 2023/1791 and amending Delegated Regulation (EU) 2024/1364 as regards the establishment of a common Union rating scheme for data centres (main act)
As labels will be issued every year from 15 August 2027, set PUE and WUE class targets (Annex I) at design stage for new and existing sites and build the metering infrastructure to the reporting requirements.
- Labels are generated automatically by the European database every year from 15 August 2027 and are publicly available electronically in all official Union languages.
Open Compute Project Foundation 2026
Introducing Power Compute Effectiveness (PCE), a Provisioned Capacity Allocation Metric for the New Data Center Era (October 2026)
Compute PCE automatically from the design model: utility/transformer capacity as P_facility, peak cooling electrical input at design ambient, UPS/transformer/busway losses and auxiliary panel loads, all on the declared redundancy basis.
- Motor horsepower converts at HP x 0.746; kVA ratings convert to kW at the declared power factor.
arXiv preprint (cs.AI), Harvard T.H. Chan School of Public Health 2026
Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers
In fleet or facility energy estimation, use capacity × 8,760 × facility-load coefficient (0.48-0.70, central 0.58) and report the range (67.7-98.6 TWh) explicitly.
- State-level intensities (central scenario): Virginia 535, Oregon 440, Iowa 645, Nebraska 740, Wyoming 1035 gCO2/kWh; Virginia had 142 HDCs, 21.0 TWh, 11.5 Mt.