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

Project description

Smart, secure and sustainable energy management

Renewable energy sources are intermittent and unpredictable, while energy demand can shift rapidly. Managing large-scale distributed energy systems requires resilience and security. The main challenges include building accurate real-time digital models, securing AI-based solutions, optimising energy use, and detecting faults or failures quickly. Supported by the Marie Skłodowska-Curie Actions programme, the SAILING project unites leading academic and industrial partners to create smart energy management and fault-diagnosis systems powered by advanced digital twin technology and secure AI. Key innovations include AI-driven, high-fidelity digital twins, blockchain-secured AI, predictive energy optimisation, and deep learning for rapid fault detection. By training early-stage researchers and fostering collaboration across sectors, SAILING will help deliver more efficient Internet-of-Energy systems.

Objective

The Internet-of-Energy (IoE) is a revolutionary paradigm towards achieving the EU’s zero-carbon goal through enabling intelligent and automated energy management across the entire lifecycle of energy planning, storage, distribution, and maintenance. Operating in highly complex environments characterised by intermittent and volatile renewable energy generation, dynamic energy demands, large-scale distributed energy integration, and stringent resilience and security requirements, IoE faces fundamental challenges in constructing accurate and real-time digital representation, securing Artificial Intelligence (AI) solutions, optimising energy management, and diagnosing device faults and failures.

To address these challenges, SAILING aims to establish a multidisciplinary network of world-leading academic and industrial partners to create a ground-breaking smart system for energy management and fault diagnosis powered by innovative digital twin and secure AI technologies. Specifically, SAILING will pioneer transformative research and innovations, including: 1) AI-driven high-fidelity digital twin for IoE digital representation, 2) Secure and reliable AI for IoE empowered by Blockchain and adversarial machine learning, 3) Intelligent and scalable predictive energy management and optimisation, and 4) Deep learning-based rapid fault detection and accurate failure prediction.

SAILING will train 12 Early-Stage Researchers across sectors and disciplines in the novel concepts and methodologies of smart IoE, equip them with sought-after professional and transferable skills, foster broader perspectives, and cultivate them to become future leaders in IoE. Through cutting-edge research and multidisciplinary cross-sectoral collaborations, SAILING will drive scientific breakthroughs with innovative digital twin and secure AI technologies for realising reliable, resilient, and energy-efficient smart IoE, and revolutionising the energy sector for a more sustainable and cleaner future.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks

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Call for proposal

(opens in new window) HORIZON-MSCA-2024-DN-01

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Coordinator

THE UNIVERSITY OF EXETER
Net EU contribution
€ 1 046 214,36
Address
THE QUEEN'S DRIVE NORTHCOTE HOUSE
EX4 4QJ Exeter
United Kingdom
Region
South West (England) Devon Devon CC
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data

Participants (8)

Partners (3)