EC1 - Deep-Tech Driven Energy Transformation

Transforming energy systems with AI, data, and next-generation digital technologies.

Credits

6 ECTS

Semester

2 Semester

Delivery

Online

Duration

13 weeks

Language

English

About This Course

This is a second-semester course in the MBA in Digital Deep Tech Driven Circular Economy
This course explores how deep technologies are transforming energy systems into sustainable, data-driven, and resilient infrastructures.
You will learn how AI, IoT, and digital twins enable smarter energy production, distribution, and consumption, while addressing key challenges such as climate impact, energy efficiency, and system resilience.
The course also examines the human and societal dimensions of energy transformation, including energy justice, inclusivity, and responsible digital innovation.

What You Will Learn


Deep-Tech Energy Systems

  • Smart grids and renewable energy architectures
  • Energy flexibility and distributed systems
  • Digital twins for energy optimisation


Digital Technologies for Energy

  • AI, IoT, and edge computing in energy systems
  • Data-driven forecasting and predictive maintenance
  • Integration of digital infrastructures


Sustainable & Inclusive Energy Transition

  • Energy justice and equitable access

  • Human-centred energy systems design

  • Environmental and governance challenges

Your 13-Week Journey

Here’s how your learning unfolds

Week 1 – AI in Energy Systems: forecasting, optimisation, and predictive maintenance

Using AI to improve energy forecasting, grid efficiency, and predictive maintenance in smart energy systems.

Week 2 – Smart grids for low carbon emissions

Understanding smart grid architecture and how digitalisation supports low-carbon energy systems.

Week 3 – Net Zero Energy Buildings

Exploring technologies and strategies for achieving net-zero energy performance in buildings.

Week 4 – Renewable Energy Communities

Examining decentralised energy models and digital platforms enabling local energy sharing.

Week 5 – Energy Flexibility

Analysing demand–response and digital solutions for optimising energy consumption and grid performance.

Week 6 – Human factors in sustainable industrial ecosystems

Understanding human–AI collaboration, safety, and design in automated energy and industrial systems.

Week 7 – Smart materials and embedded sensing for lifecycle management

Using smart materials and sensors to monitor performance and enable predictive maintenance.

Week 8 – IoT and edge computing for sustainable industrial ecosystems

Applying IoT and edge computing for real-time monitoring, analytics, and operational efficiency.

Week 9 – Energy justice and the social dimensions of energy transition

Exploring fairness, accessibility, and the societal impact of digital energy systems.

Week 10 – Technology, circularity and the future of urban infrastructure

Using AI and digital tools to optimise urban systems and support circular infrastructure.

Week 11 – Energy grids as networks of interconnected digital twins

Understanding digital twin ecosystems for energy sharing, optimisation, and system coordination.

Week 12 – Use of digital twins and AI for modelling cyberphysical systems in the energy domain

Applying AI and digital twins to model, optimise, and manage complex energy systems.

Week 13 – AI-Powered digital twins for sustainable energy systems

Leveraging AI-driven digital twins to enhance performance, efficiency, and sustainability.


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Skills You Will Gain

Data-Driven Energy Design

  • Designing AI-powered energy solutions
  • Building IoT and data-driven systems
  • Applying predictive maintenance strategies

Circular & Sustainable Energy Strategy

  • Evaluating renewable energy systems
  • Integrating circular economy principles
  • Optimising resource efficiency

Strategic & Ethical Innovation

  • Leading energy transformation projects
  • Balancing innovation and regulation
  • Managing sustainability and social impact

Continue Your Journey