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Module 4 - AI in Energy Management

Abstract graphic featuring colorful circuit lines and nodesAbstract graphic featuring colorful circuit lines and nodes

Making energy systems smarter and more sustainable

Modern energy systems are becoming more complex. Renewable energy production changes with the weather, electricity demand can rise and fall quickly, and power grids must continuously balance supply and demand.

Artificial Intelligence can help manage this complexity by analysing large amounts of data, improving forecasts and supporting faster, more informed decisions.

Why take this module?

The transition towards renewable and decentralised energy requires new skills as well as new technologies. Understanding how AI supports forecasting, smart grids, storage and energy efficiency can help learners and professionals engage confidently with the energy systems of the future.

The module also looks beyond the benefits of AI and asks an important question: how can we make sure that AI itself is used efficiently and sustainably?

What will you explore?

  • The challenges facing modern energy systems
  • Renewable energy and demand forecasting
  • Smart grids and real-time energy management
  • Predictive maintenance
  • AI-supported battery and energy storage management
  • Vehicle-to-Grid technology
  • Real AI tools and platforms used within the energy sector
  • Practical energy forecasting and optimisation activities
  • Green AI and the energy footprint of AI itself
  • The importance of human oversight, cybersecurity and responsible implementation

You will see how forecasting, storage, smart grids and AI can work together to create more reliable, efficient and sustainable energy systems.

Ready to explore the future of energy?
Download the module below and complete the quiz when you are finished.

Access the Module

Quiz

1. Why does decentralisation increase energy system complexity?(Required)
2. A grid operator faces sudden drops in wind production. What is the BEST use of AI?(Required)
3. Why is demand forecasting critical for energy management?(Required)
4. Which combination of data would MOST improve renewable energy forecasting?(Required)
5. In a smart grid, what is the main advantage of real-time AI decision-making?(Required)
6. A city has high evening electricity demand and solar production during the day. What is the BEST AI-supported strategy?(Required)
7. Why is predictive maintenance more efficient than traditional maintenance?(Required)
8. How does AI contribute to integrating more renewable energy into the grid?(Required)
9. What is a key trade-off highlighted in “Green AI”?(Required)
10. In a Vehicle-to-Grid (V2G) system, what is the main benefit of AI?(Required)
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