Keynote – Olivier Blum
Summary
Blum highlighted energy as the most critical bottleneck for AI: data centers will need 200 GW of new capacity by 2030, and AI‑driven workloads will double that demand. Schneider Electric’s strategy is to pair AI with “Energy Intelligence” – integrating AI for demand forecasting, grid stability, and dynamic load balancing.
Key initiatives:
- AI‑enabled micro‑grids using edge compute to optimise renewable integration.
- Digital twins of power plants for predictive maintenance, reducing downtime by 20 %.
- Multilingual AI models to manage energy consumption in regional languages, enhancing adoption in the Global South.
Blum argued that AI will both increase energy demand and provide tools to reduce it, making Schneider’s energy‑efficiency solutions indispensable.
Key Takeaways
- AI‑energy paradox: AI raises power demand, but AI can also optimise energy use.
- Micro‑grid AI: Edge compute for real‑time renewable integration and load balancing.
- Digital twins: Predictive maintenance reduces plant downtime by ~20 %.
- Multilingual energy AI: Enables broader adoption in low‑resource regions.
- Strategic synergy: Schneider views AI and energy as co‑evolving technologies.