It is a little-known fact that artificial intelligence is already laying the groundwork for a successful energy transition. According to current projects at the Fraunhofer IEE, self-learning systems are not only taking over grid control, but also optimising energy trading. In an international competition, an AI agent was even able to prove that it can confidently handle the fluctuating feed-in of renewable energies and even fend off attacks on the grid.
So what kind of professionals are really needed to drive the energy transition with the help of AI? Below, you will learn which qualifications are in particular demand and in which areas AI will prove indispensable in the coming years. Incidentally, systematically leveraging AI for the energy industry itself is also a way to counteract the shortage of skilled workers.
The role of artificial intelligence in the energy transition
Today, AI is a key driver of the energy transition and is used in almost every area of renewable energy. From managing wind farms to optimising the operation of photovoltaic systems, AI is taking over more and more tasks that used to require manual intervention. Particularly exciting is the fact that AI systems are capable of learning independently and continuously improving. We have already mentioned AI agents. In one of their projects, the Fraunhofer IEE (Fraunhofer Institute for Energy Economics and Energy System Technology) shows how AI reacts to faults in the power grid in real time and rectifies them efficiently.
After all, one of the most important areas of application for AI in the energy industry is the precise forecasting of electricity generation. Renewable energies such as wind and solar energy are, by their very nature, volatile. However, we need to know exactly how much electricity will be available and when. AI models analyse weather data and combine it with real-time information to create forecasts. These enable grid operators and energy suppliers to better plan the flow of energy. This is not only about efficiency, but also, and above all, about grid stability – an improperly planned power grid can lead to overloads and outages.
What's more, AI-based systems are used in energy management. They ensure that energy is optimally distributed between different plants and that consumption can be flexibly adapted to production. AI is therefore a key tool for making the energy transition more efficient, cost-effective and stable. The possibilities seem endless – but all of this will only work if the right professionals are available.
Key areas of AI application in the energy industry
The energy industry is particularly challenged to integrate renewable energies into existing systems. This is where the true value of AI lies: it offers solutions that primarily promote stability in the energy sector. But which areas benefit most from this technology?
One of the key areas of application is predicting and optimising energy production. As we have already seen, renewable energies such as wind and solar energy are highly dependent on the weather and thus fluctuate – highly precise forecasts for production and consumption are critical. AI models analyse enormous amounts of data – from weather forecasts to historical production patterns – and provide predictions that enable grid operators and energy suppliers to adjust their strategies. This helps to keep the energy supply stable even when production fluctuates.
Another area is operational optimisation. This is about maximising the output of facilities such as wind farms or solar power plants. AI can analyse how efficiently a facility is operating and suggest adjustments to improve operations. This results in more energy being generated. At the same time, the facilities last longer and require less maintenance.
AI is also being used increasingly in automated grid control. Here, too, the aim is to efficiently control networks that are fed by a variety of decentralised energy sources. Self-learning systems help to identify critical grid situations and take action before overloads or outages occur.
Finally, AI also offers great potential for evaluating and identifying opportunities in terms of sustainability. For example, research is increasingly being conducted into how AI can help to reduce the carbon footprint of energy networks and production processes by optimising energy consumption and minimising unnecessary losses.
In summary, AI optimises operations and energy production and also ensures that the entire energy system remains stable and efficient. So which professionals are needed to implement and further develop these technologies?
Which AI professionals are in particular demand?
With the increasing importance of AI in the energy industry, the demand for specialised professionals is naturally growing. Companies are specifically looking for experts who have the technical knowledge in AI development as well as an understanding of the requirements of the energy transition. But what qualifications and skills are particularly in demand in this area?
The demand is centred around data scientists and big data specialists. The renewable energy sector also generates huge amounts of data that need to be processed for AI-based prediction of electricity generation and consumption. This requires the development and optimisation of machine learning (ML) algorithms that analyse weather data or patterns in energy production, for example. The work of these professionals lays the foundation for many AI-based applications in the energy industry – from production forecasting to operational optimisation.
Another important area is AI-powered automation. This role calls for engineers specialising in machine learning and cognitive systems. These professionals develop self-learning systems that can make autonomous decisions to improve grid control and make energy distribution more efficient. Particularly important in this regard is knowledge of neural networks, as demonstrated in the Fraunhofer IEE automated grid control project.
There is also an increasing demand for AI developers specialising in energy management systems. These professionals work on the development of systems that monitor and optimise energy consumption in real time. They help to ensure that renewable energies are used more efficiently and that electricity grids remain stable. Interdisciplinary knowledge is often required in this area as well, since both knowledge of energy technology and computer science are in demand.
Last but not least, professionals specialising in sustainability and green AI are on the rise. This is because AI is also part of the problem. These experts address the question of how AI can contribute to reducing the carbon footprint. They develop algorithms that minimise the energy consumption of AI systems themselves and promote sustainable solutions.
AI professionals and AI skills are needed for the successful implementation of the energy transition. Companies need experts who have specific knowledge in the field of energy technology and can apply machine learning and automation here. In the next chapter, we take a look at future developments and how the demand for AI talent might evolve.
Future developments and opportunities for AI in the energy industry
The role of artificial intelligence in the energy industry will continue to grow in the coming years, particularly in the context of the global energy transition. So will the demand for professionals who develop and implement AI applications. But what do future developments look like, and what opportunities will arise for companies that focus on AI-based solutions?
As we have already discussed here, one significant future trend is the increased use of AI to improve grid stability and resilience. As energy supply becomes more decentralised, dominated by unpredictable fluctuations in wind and solar energy, self-learning systems will be critical to managing this complexity. Thanks to AI, we will make accurate forecasts, respond automatically to disruptions and further stabilise grid operating systems. The potential to fully automate power grids is one of the greatest opportunities that AI offers us.
Another field is the development of Green AI. This involves the question of how AI can be used not only to make renewable energies more efficient, but also to reduce the energy consumption of AI applications themselves. Green AI will thus make all sectors of the economy more sustainable. And the pressure on companies to develop sustainable and environmentally friendly solutions is growing.
What is more, the personalisation of energy services using AI will become increasingly important. Intelligent systems will be able to analyse the energy consumption of households and companies in detail and, based on this, provide tailored recommendations for optimising energy consumption. This will be made possible primarily by the increasing integration of smart grids and the Internet of Things (IoT).
The future of AI in the energy industry is packed with possibilities – from automating and optimising energy production to developing sustainable, climate-friendly applications. To fully exploit this potential, the right specialists must be recruited and expertise in this area must be developed. Companies that invest in AI expertise now will play a pioneering role in shaping the energy transition in the years to come-
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