The role of artificial intelligence in energy systems development: A comparative analysis of national strategies and future directions

Artificial intelligence is expanding rapidly across energy systems, offering tools for renewable integration, forecasting, grid reliability, and demand management while also increasing electricity and infrastructure demand. Yet evidence of its energy-system benefits remains fragmented across national contexts and is often synthesised without accounting for differences in evidence quality or governance. This study addresses that gap through a systematic review of 146 peer-reviewed studies and authoritative report…

By the Truvace deskOctober 19, 20261 min read1 sourcePeer-reviewedTRV-2026-1905· cite & verify

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In brief

The authors reviewed 146 peer-reviewed studies and authoritative reports from 2010 to 2026 and compared AI use in energy systems in the United States, India, South Korea, and Sweden. They paired an evidence-tiered comparison across 14 indicators with a ten-city deployment index and a four-pillar governance framework.

The work matters because it shows that AI benefits for energy systems depend on more than technical performance or spending. Grid carbon intensity, institutional capacity, cybersecurity, explainability, and equity all shape results, but country-specific quantitative validation is still uneven, so the cross-country evidence is incomplete.

Main points

The review covers 146 studies and reports published from 2010 to 2026, plus earlier methodological sources dating back to 1994.
The four countries were chosen to represent market-led, emerging-economy, state-directed, and mature low-carbon contexts.
Northern Virginia/DC ranks first on the deployment index at 83.00, just ahead of Stockholm at 82.95.
The two top cities reached their scores by different routes: infrastructure scale and grid deployment versus low-carbon electricity integration and institutional maturity.

The gain

Reported studies show better renewable-energy forecasting, and CNN-based and explainable machine-learning methods have been applied to power-system fault detection.

The rundown

The study responds to fragmented evidence on AI's energy-system benefits, which earlier syntheses often combined without accounting for differences in evidence quality or governance. Its method links evidence tiers, sub-national deployment patterns, and measurable governance priorities.

AI tools are described as useful for renewable integration, forecasting, grid reliability, and demand management. The same expansion also raises electricity and infrastructure demand.

Across the four cases, technical performance and investment alone did not ensure sustainable outcomes. The authors present their approach as transparent and replicable guidance for more effective and equitable integration of AI into energy systems.