MSCA Doctoral Networks Proposal · HORIZON-MSCA-DN-2024-01-01

SAILING: Secure AI and Digital Twin Empowered Smart Internet-of-Energy

A proposal-stage doctoral network that combines secure AI, digital twins, smart energy management, and resilient fault analytics to build a reliable, resilient, and energy-efficient Internet-of-Energy ecosystem.

Planned duration: 48 months · Consortium: 11 organisations from 7 countries · Programme: 8 work packages Total Project Funding: Euros ca.30M

Explore the proposal → View the consortium

Project Visuals

SAILING Research Visuals

These visuals present the project's identity, Smart IoE vision, and integrated research structure in a cleaner website-ready style.

SAILING project identity illustration — Project Identity

A visual summary of SAILING's core theme: secure AI and digital twins supporting renewable, resilient, and intelligent energy systems.

Smart Internet-of-Energy vision illustration — Figure 1: Smart IoE Vision

The research vision links physical energy assets with digital twin models, secure AI analytics, and real-time optimisation across the IoE stack.

Work package structure illustration — Figure 2: Work Package Structure

The programme combines five scientific research streams with integration, training, management, and dissemination into one coordinated doctoral network.

Consortium Snapshot

SAILING proposes an interdisciplinary and inter-sectoral doctoral network centred on digital twins, secure AI, and smart energy systems.

Research core: Five scientific work packages covering digital twins, secure AI, energy management, fault prediction, and system integration.
Training model: 12 ESR projects, joint supervision, secondments, annual workshops, summer schools, and plenary meetings.
Consortium profile: 8 academic institutes and 3 industrial companies contributing complementary expertise.
Associated partner role: NTNU, UPC, and KTU support doctoral awarding, secondments, and specialised training.
Impact ambition: Reliable, resilient, and energy-efficient IoE systems that support Europe's clean-energy transition.

Research Objectives

Five connected research and innovation objectives extracted from the proposal.

RO-1: IoE Digital Twin 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)
RO-2: Secure AI for IoE Develop secure and reliable AI solutions that protect IoE infrastructures against data tampering, adversarial attacks, and privacy leakage. Lead: Vienna University of Technology (TUW)
RO-3: Energy Management Develop smart and scalable strategies to balance supply and demand in distributed IoE systems and improve operational efficiency. Lead: Solitek R&D (SOLI)
RO-4: Fault Detection Develop fast fault diagnosis and proactive failure prediction algorithms that improve IoE resilience and reliability. Lead: Catalonia Institute for Energy Research (IREC)
RO-5: Optimisation System Integrate the scientific results from WP1-WP4 into an intelligent optimisation platform for real-time monitoring, management, and diagnosis of real power grids. Lead: Estabanell Energia, S.A.U. (ANELL)

Research Programme

Work Packages

Eight proposed work packages structure the SAILING doctoral network from core research to training and impact.

WP1

AI-Driven High-Fidelity IoE Digital Twin

Reliable data communication, multiscale modelling, and uncertainty-aware digital twinning for IoE.

WP2

Blockchain-Empowered Secure and Reliable AI for IoE

Blockchain-secured data sharing, adversarial robustness, and privacy-preserving AI for IoE.

WP3

Smart and Scalable IoE Energy Management

Prediction, hierarchical distribution, and adaptive storage management for distributed smart energy systems.

WP4

Agile Fault Detection and Accurate Failure Prediction

Semi-supervised learning, echo state networks, and spatial-temporal prediction for resilient IoE operation.

WP5

Intelligent Energy Optimisation System

System integration, multi-objective optimisation, and real-world demonstration on operational power-grid infrastructure.

WP6

Project Management

Governance, reporting, recruitment oversight, risk management, and consortium coordination.

WP7

Training and Knowledge Transfer

PCDPs, secondments, workshops, summer schools, and network-wide training for 12 ESRs.

WP8

Dissemination, Exploitation, and Communication

Website, outreach, open resources, stakeholder engagement, and exploitation planning.

Challenge

Four Research Problems

The proposal is organised around four core technical problems in the Internet-of-Energy domain.

Real-Time Digital Representation

Existing grid simulations do not capture the dynamicity and uncertainty of real IoE systems with enough fidelity for real-time decision support.

Secure and Reliable AI

AI-driven IoE management remains vulnerable to malicious data tampering, adversarial attacks, and privacy risks on resource-constrained infrastructures.

Real-Time Energy Management

Supply-demand fluctuations, renewable intermittency, and distributed assets make scalable, adaptive energy management difficult to achieve.

Fault Diagnosis and Failure Prediction

Massive heterogeneous IoE data makes fast fault diagnosis and proactive cascading-failure prediction difficult with current methods.

Consortium

Participating Organisations

Beneficiaries and associated partners proposed in SAILING.

University of Exeter (UNEXE) — United Kingdom

Catalonia Institute for Energy Research (IREC) — Spain

Institute of Theoretical and Applied Informatics, Polish Academy of Sciences (IITIS-PAN) — Poland

Aarhus University (AU) — Denmark

Vienna University of Technology (TUW) — Austria

Solitek R&D (SOLI) — Lithuania

OSC AS (OSC) — Norway

Estabanell Energia, S.A.U. (ANELL) — Spain

Norwegian University of Science and Technology (NTNU) — Norway

Technical University of Catalonia (UPC) — Spain

Kaunas University of Technology (KTU) — Lithuania

Contact

Proposal Contacts

Key institutions and scientist-in-charge information drawn from the proposal document.

Coordination and partner contacts

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Coordinating beneficiary

University of Exeter (UNEXE)
Scientist in charge: Prof. Geyong Min

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Associated partner highlight

NTNU — Prof. Houxiang Zhang
Doctoral awarding, secondments, digital twin training, and open science support.

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Programme

Marie Sklodowska-Curie Actions Doctoral Networks
Call: HORIZON-MSCA-DN-2024-01-01