Special Session: AI for the Future Energy System
Background and goals
The intersection of AI and the energy sector is a high-impact frontier of innovation. AI is rapidly becoming essential for optimizing power grids, forecasting renewables, reducing inefficiencies, and enabling smarter energy markets. Simultaneously, the rise of large-scale AI data centers highlights challenges related to energy use, hardware demands, and energy grid integration. In addition, AI-enabled energy communities, flexibility markets, and grid-edge intelligence are becoming essential components of the future energy system. AI can support adaptive grid operation, resilience to extreme weather events, and real-time coordination between distributed energy resources, electric vehicles, storage systems, and smart buildings.
The rapid transformation of energy systems toward distributed, renewable, and prosumer-driven architecture presents significant challenges for monitoring, control, and optimization. Traditional centralized approaches are increasingly insufficient to manage the complexity, variability, and real-time dynamics of modern power networks. Recent advances in decentralized control and AI, including multi-agent reinforcement learning, intent-driven agents, hybrid physics-informed models, multimodal Transformers, large language models (LLMs), and foundation models (FMs), offer unique opportunities for autonomous and resilient future energy systems. At the same time, EU AI Factories provide scalable infrastructures and collaborative ecosystems for developing, testing, and deploying AI tools in energy systems. By combining high-performance computing, heterogenous data, and multi-agent orchestration tools, AI Factories can facilitate the rapid adoption of AI for predictive maintenance, demand-response optimization, energy forecasting, and real-time coordination of distributed energy resources. Agentic workflows and digital twins further integrate energy and computational orchestration across energy data spaces, accelerating the adoption of intelligent, adaptive, and autonomous grid operations.
The special session aims to bring together researchers, engineers, and practitioners working at the crossroads of energy and AI. The session seeks to promote interdisciplinary dialogue and exchange ideas on how AI can contribute to the resilient management and decarbonization of energy infrastructure, as well as on how more energy-efficient models, training pipelines, and deployment architectures can be developed. We welcome contributions from both academia and industry. Moreover, the session is closely linked to research activities from EU initiatives such as Horizon Europe and Digital Europe Programme projects focusing on AI for energy systems, AI Factories, energy data spaces, and digital twins for energy infrastructure. Indicative projects featured in this special session include HEDGE-IoT, EnerTEF, PVSmile, Solar-Move, RO AI Factory, and others. Finally, it also aligns with the activities of the Smart Energy Cluster, which brings together academia, industry, and innovation stakeholders working on digitalisation of the energy sector, providing an opportunity to connect AI researchers with energy system practitioners and technology developers working on real-world deployments.
Relevant topics
Relevant topics include, but are not limited to:
1. Large language models and foundational models for future smart grids
Description: Large language models on multi-modal smart grid data for real-time decision-making, fault detection, and autonomous control.
Research focus: Pretraining and fine-tuning on multi-source data; integration with graph neural networks for topology-aware optimization; explainable AI for operator trust.
2. Distributed renewable energy forecasting and optimization
Description: Multi-modal spatio-temporal data fusion for forecasting and dispatch of renewable energy at microgrid and community scales.
Research focus: Deep learning, multimodal Transformers, latent-space representations, JEPA, and foundational models; agentic workflows for coordinated energy resource optimization.
3. AI-driven energy services orchestration
Description: Multi-agent orchestration frameworks leveraging intent-driven AI agents to manage energy services across distributed grids and microgrids.
Research focus: Coordination of energy and computational resources, integration of data spaces and hybrid AI models, and real-time agentic workflows for predictive and adaptive operations.
4. Smart buildings and microgrids flexibility management
Description: AI-driven control of flexibility sources and demand response and cooperation across distributed energy nodes.
Research focus: Multi-agent reinforcement learning, decentralized model predictive control, and AI-driven optimization, coordination, and flexibility management.
5. Prosumers and energy markets
Description: Transactive markets and prosumer behavior modeling in decentralized energy networks.
Research focus: RAG-enhanced market simulations and AI-driven optimization to improve local energy allocation, market efficiency, and coordinated trading; energy blockchain; cooperative game theory.
6. Digital twins for decentralized energy resources and systems
Description: High-fidelity digital twins for distributed energy assets enabling simulation, monitoring, and optimization.
Research focus: Hybrid physics-informed AI, real-time data integration, and agentic workflows for scenario testing, synthetic data generation, and related topics.
7. AI for energy system resilience and security
Description: AI-based methods for detecting, predicting, and mitigating disruptions in energy systems caused by cyber threats, system faults, or extreme weather events.
Research focus: Anomaly detection in grid operations, AI-driven cybersecurity for energy infrastructures, resilience-aware control strategies, AI-based restoration planning.
8. AI for energy communities and local energy systems
Description: AI supporting the operation of local energy communities and cooperative energy systems.
Research focus: Collective self-consumption optimization, community-level forecasting, AI-enabled coordination of distributed energy resources, local flexibility markets.
Session chairs
Important dates
| Event | Date |
|---|---|
| Paper submission deadline | 25th July 2026 |
| Notification of acceptance | 8th September 2026 |
| Camera-ready submission | 22nd September 2026 |
| Conference dates | 22nd–24th October 2026 |
Submission guidelines
Papers will be submitted through the conference submission system, specifying the special session title “AI for the Future Energy System”. Official submission link.