forecaster stringclasses 10
values | forecast_date stringclasses 5
values | horizon_year int64 2.03k 2.04k | scope stringclasses 2
values | metric stringclasses 3
values | unit stringclasses 3
values | value_low int64 78 1.07k | value_high int64 78 1.07k | revised_value float64 106 220 ⌀ | revised_date stringclasses 2
values | transparency stringclasses 3
values | verified stringclasses 2
values | source stringlengths 29 111 | notes stringlengths 107 231 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
IEA | 2025 | 2,030 | Global | demand | TWh | 946 | 946 | null | null | open | yes | IEA Energy&AI annex World Data r24 | Base Case (2024=416); IEA OWN 2030 scenarios span 669 Headwinds-1264 Lift-Off; quoted 945 is base-case-only |
Gartner | 2025-11 | 2,030 | Global | demand | TWh | 980 | 980 | null | null | opaque | yes | Gartner newsroom press release 2025-11-17 | CONFIRMED 448 TWh(2025)->980(2030) Global; AI servers 93->432 TWh; transparency=opaque means model proprietary/not reproducible (release is free but unauditable) |
McKinsey | 2024 | 2,030 | US | demand | TWh | 606 | 606 | null | null | opaque | yes | McKinsey "How data centers...sate AI's hunger for power" (17 Sep 2024) + "AI's power binge" charts (6 Nov 2024) | CONFIRMED at McKinsey primary: 147(2023/3.7%)->224(2025/5.2%)->606(2030/11.7%); medium scenario full yr-by-yr; ~23% CAGR; figure PRIMARY but model proprietary/not reproducible |
LBNL | 2024 | 2,028 | US | demand | TWh | 325 | 580 | null | null | open | yes | LBNL 2024 US Data Center Energy Usage Report (Shehabi et al; eta-publications.lbl.gov) | 325-580 TWh = 6.7-12% of US elec in 2028 NOT 2030 (common misquote; CSV year corrected 2026-06-06); 2023 actual=176 TWh/4.4%; the US-govt anchor |
EPRI | 2024 | 2,030 | US | demand | TWh | 200 | 400 | null | null | open | yes | EPRI Powering Intelligence (May 2024; restservice.epri.com) | PRIMARY UNIT is % not TWh: scenarios 4.6/5.0/6.8/9.1% of US elec by 2030 (TWh DERIVED/approx); EPRI 2026 ed REVISED UP to 9-17% by 2030 (~+60%) = self-revision datapoint |
BCG | 2024 | 2,030 | US | demand | TWh | 1,050 | 1,050 | null | null | opaque | no | via WRI (NOT the in-hand BCG PDF) | CHECKED in-folder BCG "Infrastructure Strategy 2026" p20: gives only a GROWTH RATE (compute demand high-teens pct/yr to 2030 vs hist 11-12pct) NOT 1050 TWh; the 1050 is from a SEPARATE paywalled BCG study -> stays opaque/unverified |
GoldmanSachs | 2025-02 | 2,030 | Global | growth | pct_vs_2023 | 165 | 165 | 220 | 2026-04 | opaque | yes | Goldman Sachs (Feb 2025 "165% by 2030" -> GS SUSTAIN Nov 2025 "175%" -> Apr 2026 "~220%") | CONFIRMED DOUBLE up-revision 165->175->220 pct vs2023 (all primary GS); ~1350 TWh global equiv per secondary; model proprietary |
SP_Global | 2025 | 2,030 | US | demand | TWh | 728 | 728 | null | null | secondary | yes | S&P Global 451 Research Market Monitor (Sep 2025; spglobal.com news) | base 366 TWh(2025)->728(2030) US; HYPERSCALE/LEASED/CRYPTO ONLY (excl enterprise=scope caveat); also 61.8->134.4 GW; "nearly triple" is GW-from-2024 (~2x in TWh) - unit nuance |
BloombergNEF | 2025-04 | 2,035 | US | capacity | GW | 78 | 78 | 106 | 2025-12 | secondary | yes | BNEF "AI and the Power Grid" (1 Dec 2025; via Utility Dive/Bloomberg) | REVISED UP 78(Apr25)->106(Dec25) GW =+36% in 7mo; 2035 horizon + GW (not TWh); BNEF calls 106 CONSERVATIVE vs GS/BCG/McKinsey; TX 12GW early-stage only 1.8GW location-confirmed (speculative) |
McKinsey | 2024 | 2,030 | Global | capacity | GW | 220 | 220 | null | null | opaque | yes | McKinsey "Scaling bigger faster cheaper data centers" (Exhibit 2) | GLOBAL ~220 GW by 2030 (capacity); SAME forecaster reports US in TWh (606) but GLOBAL in GW = intra-forecaster unit-switch; NOT directly comparable to IEA/Gartner global-TWh (no clean conversion) |
Deloitte | 2025 | 2,030 | Global | demand | TWh | 1,065 | 1,065 | null | null | secondary | yes | Deloitte 2025 TMT Predictions | 536 TWh(2025/~2pct)->1065(2030/~4pct); scenario range ~1000-1300; built on EIA IEO-2023 base + named sources (validated) = methodology sketched (more transparent than peer consultancies) |
AI Energy-Demand Forecast Scorecard
A reproducible audit of how the field forecasts data-centre electricity demand: how the published forecasts disperse, how they get revised, and whether they are transparent enough to reproduce. Primary-sourced, published with the data and a script that regenerates every figure.
AI disclosure: the research is the author's; this text was drafted with AI assistance and reviewed by the author. The model, and the conflict it creates, are named in the Conflict of interest section of the linked paper.
- Author: NM AI Research (independent analyst)
- ORCID: 0009-0003-4213-7769
- DOI: https://doi.org/10.5281/zenodo.20572928
- Interactive tool: https://nmairesearch.github.io/forecast-scorecard/
- Source and code: https://github.com/NMAIResearch/forecast-scorecard
Files
forecast_scorecard_data.csv(11 rows): one row per published forecast. Columns:forecaster,forecast_date,horizon_year,scope,metric,unit,value_low,value_high,revised_value,revised_date,transparency,verified,source,notes.build.py: standard-library reproducer that reads the data and writes the front-end.LICENSE: Creative Commons Attribution 4.0 International.
Method
Dispersion is measured only within comparable slices, because units and scopes are not interchangeable. Each forecast is traced through a transparency funnel from verified to confirmable to reproducible.
Citation
NM AI Research. AI Energy-Demand Forecast Scorecard. Zenodo. https://doi.org/10.5281/zenodo.20572928 . Licensed CC BY 4.0.
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