WP1: AI-Driven High-Fidelity IoE Digital Twin

Objective

Construct an advanced AI-driven Internet-of-Energy digital twin that can represent, analyse, and optimise a complex energy system in real time.

Lead: Aarhus University (AU) · Timing: M7-M42 · Main ESR involvement: ESR1-ESR3

Key Tasks

• Develop ultra-reliable low-latency data transmission and multi-source data fusion mechanisms for digital twin synchronisation.
• Create multiscale models that combine first-principles device models with graph neural network orchestration at the system level.
• Introduce AI-based uncertainty analysis with multi-exit DNNs, fuzzy logic, and a unified knowledge graph for system-level uncertainty insight.

Programme Links

This work package contributes primarily to RO-1 and is part of the proposal-stage programme structure captured in the SAILING Part B document.