Artificial Intelligence is often described in broad terms. It can sometimes be difficult to picture what it actually does when applied to a real industry.
Electricity grids provide a useful example.
A recent International Energy Agency report identifies several practical functions for AI within electricity networks. These include forecasting, detecting problems, diagnosing issues, simulating possible situations and optimising how systems operate.
Consider forecasting.
Electricity demand changes throughout the day. Renewable electricity generation can also change depending on weather conditions. Better forecasting helps grid operators understand what may happen next and prepare accordingly.
Detection is another important use.
Electricity networks contain large amounts of equipment. Data from monitoring systems can help identify unusual behaviour that might indicate a developing fault or maintenance need.
AI can also support optimisation. This means analysing different options and helping operators understand how existing infrastructure can be used more efficiently while maintaining safety and reliability.
The International Energy Agency stresses that digitalisation and AI can help operators make better use of existing electricity networks, which is increasingly important as electricity demand grows.
These applications also show why vocational skills need to evolve.
Energy workers may increasingly encounter sensors, digital monitoring systems and data alongside traditional electrical equipment. They do not all need to become data scientists, but understanding what digital systems are telling them will become more valuable.
The SUSTaiN AI Training Programme introduces learners to AI in Energy Management and provides a starting point for exploring these practical applications.
Educators can also draw on the SUSTaiN Digital Toolkit to introduce learners to AI based tools and encourage discussion about where such technologies are useful and where professional judgement remains essential.
A useful classroom question might be simple: if a system predicts that a piece of equipment could fail, what should happen next?
Questions like this help learners see that AI supports decisions. People are still responsible for interpreting information, considering risk and deciding what action should be taken.
That combination of digital capability and human expertise will be increasingly important in the energy workforce.
Source: International Energy Agency, Modernising Grids in the Age of Electricity.