Governing Artificial Intelligence for Energy Planning and Transition: Shared AI Governance for Energy Systems (SAGES) - A briefing note

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

This briefing examines how artificial intelligence is entering national energy planning and why its use requires explicit governance, assurance and capacity-building. Its scope covers AI applications in forecasting, scenario exploration, optimisation acceleration, digital twins, generative-AI-enabled workflows, stress testing and decision support for increasingly complex, climate-exposed and cross-sectoral energy systems. It argues that AI can improve planning speed, probabilistic analysis and exploration of deep uncertainty, but should complement—not replace—physics-based models, institutional judgement or public accountability. The key finding is that the principal risks of AI in energy planning are institutional rather than purely technical. Poorly governed AI can create opaque model authority, vendor dependence, loss of public-sector capability, weak traceability, data-governance failures, and erosion of national sovereignty over models, data and decisions. The paper therefore proposes the Shared AI Governance for Energy Systems (SAGES) framework as a non-binding, principles-based assurance architecture for governments, regulators and international partners. SAGES centres on five commitments: AI as an empowering partner rather than a default solution; human and institutional accountability for final decisions; proportional, purposeful and testable trust; respect for national sovereignty and institutional maturity; and collective progress towards just energy futures. It recommends practical implementation through a Secretariat and Working Task Groups covering vendor engagement, demonstration tools, sovereignty and IP safeguards, capacity building and model literacy. The briefing concludes that responsible AI adoption requires traceability from data to decision, stress testing, independent challenge, public-sector capability, and international cooperation that supports national ownership without transferring decision rights away from states.

Links and sources

• PDF from original source

Comments