Artificial intelligence in the energy sector

Artificial intelligence is more relevant than ever. It is an unavoidable topic that is gaining in importance across all sectors. This article looks at the various ways in which artificial intelligence can be used in the energy sector.

What is artificial intelligence and how does it work?

The European Parliament defines artificial intelligence as the imitation of human capabilities by machines and technology. This includes skills such as logical thinking, learning, planning, but also creativity. To make this possible, technological systems perceive their environment and learn to process the perceived data. Depending on the data basis analyzed, the reaction of the AI also varies. The core of AI technology therefore lies in data processing. Existing data is analyzed by AI systems to identify patterns and correlations. This knowledge is then used to make decisions or solve problems independently. AI systems continuously learn from the data they receive and adapt their behavior accordingly.

How can artificial intelligence be used?

The currently best-known application of AI technology is “ChatGPT”, a language model based on artificial intelligence. It is capable of having human-like conversations. By now, almost everyone has at least heard of ChatGPT or even used it themselves. Whether for simple questions, instructions, summarizing or writing texts. There are virtually no limits to the application. After its release in November 2022, over 100 million people used the consumer application within the first two months. The basis for successful use of ChatGPT is the formulation of good “prompts”. These are the instructions you give the artificial intelligence.

However, artificial intelligence technology has countless other potential applications and is therefore of great benefit in a wide range of industries. It is used in medicine to make diagnoses and optimize treatment plans. In the transport sector, AI can be used to monitor traffic and control autonomous vehicles. In agriculture, there are opportunities to use AI to optimize crop yields and reduce the use of pesticides. AI is also being used more and more in creative industries such as art, poetry and music.

Artificial intelligence in the energy sector

A basis is necessary

Before the many advantages of AI can also be used in the energy sector, other fundamental challenges must first be overcome. In conversation with Szilard Toth, epilot’s Chief Technical Officer, one important point in particular became very clear. The first step is to create a secure basis on which to work. This basis is created through data science and data management.

Toth says: “The energy world is still in its infancy when it comes to the usability of data. Everyone needs to become more data-driven. However, there is a gap between the current status and the efficient analysis, evaluation and processing of data, especially in combination with artificial intelligence. Most companies do not yet have a general collection point for data, let alone a system to analyze it.”

The importance of data is therefore enormous and it is imperative that this potential is utilized. However, many companies in the energy sector are caught between the need to take the first step on the one hand and the desire to immediately take major steps towards a digitalized energy industry on the other. This can lead to frustration if progress is not made as quickly as hoped.

How do energy companies solve this problem?

In order to successfully use artificial intelligence in the energy sector for your own area of application, you first need a database that can be fed into the AI. Therefore, the first step is to collect, store and analyze data. Companies should set clear goals based on their current situation and then optimize them step by step.

One problem is that there are currently only a few energy suppliers and grid operators that meet the technical requirements to implement such systems. This applies both to the hardware required and to the technical knowledge of the employees. They are stuck in lengthy, analog processes, which is why it is difficult to make major changes in the direction of efficient data management.

For many companies, coupling complex data management with their core business is also a challenge. They don’t see how data analytics and AI fit directly with their energy solutions and therefore don’t recognize the need to change their systems and operations to use AI.

Szilard Toth sees benchmarking as another important aspect: “Many companies build their own solutions, but work in closed systems and don’t know what is happening in other companies. A platform that collects anonymized data from different companies could generate valuable, cross-industry insights and enable machine learning.” One example of this is the epilot 360 platform with its data lake. However, there are also pioneers in the energy sector: large companies such as EnBW are more advanced in this area. They have systems in place to use data lakes and analyze data.

In general, companies in the energy sector need to digitize and accelerate processes in a wide range of areas in order to become more efficient. Artificial intelligence can help digitize the energy industry in many areas. A step-by-step approach is the key to creating a solid foundation, making data properly usable and then building AI solutions on this basis.

The future for energy suppliers

As part of the three-year research program of the Competence Center Cognitive Energy Systems (K-ES), various use cases for artificial intelligence in the energy sector were tested. Solutions were developed for the entire value chain, from raw material extraction to production, distribution, sales and the consumer.

Forecast accuracy

Forecast accuracy is crucial for the planning of renewable energy generation plants. Artificial intelligence can, for example, help to improve forecasts of solar irradiation and wind speed and thus increase the quality of forecasts.

Sales

In sales, artificial intelligence in the energy sector can help to speed up inefficient processes and thus reduce costs. Costs must be reduced in the core business of energy supply companies, while at the same time the portfolio is being expanded into new areas such as energy solutions. Here it is important to try out, measure and optimize new approaches.

Customer service

Another approach is the use of artificial intelligence to support employees or customers in recognizing problems and solving them themselves with the help of AI. For example, AI chatbots trained for specific use cases can answer initial questions and provide assistance in a dialog. By better structuring and storing data, artificial intelligence could improve inbound and outbound traffic and automate processes, which could lead to increased efficiency.

Energy trading

Artificial intelligence can also be helpful in various areas of energy trading. Analyzing the energy market using AI helps to simplify its monitoring. In this way, irregularities in the market or the exploitation of market power can be identified and prevented at an early stage. Algorithmic trading also enables automated energy placements on the market, i.e. continuous trading using artificial intelligence.

Further options

There are also other future fields of energy supply in which AI can be used, such as determining suitable areas for the expansion of wind energy. Various complex factors such as the identification of endangered bird species can be taken into account. Consumers can also benefit from the use of artificial intelligence. In addition to the existing smart home solutions, the devices in a smart networked home could collect data about the user’s behavior and work as energy-efficiently as possible on this basis. In addition, factors such as prices on the electricity market can also be taken into account in order to minimize costs for the consumer.

Possible uses of AI for network operators

AI can make grid operation more flexible by enabling complex status determinations for electricity grids. In a practical test, a self-learning agent showed that it can deal with fluctuating feed-ins and loads, maintenance work and attacks. AI can help to make decentralized energy generation from renewable energies more efficient by coordinating the energy system in real time and reacting automatically.

The resilience of the electricity grid can also be improved by artificial intelligence. AI systems can detect malfunctions and provide possible repair instructions to minimize downtimes. However, Szilard Toth notes: “There are also challenges in the grid sector, such as grid compatibility and overloading. Here, too, it is crucial to collect and analyze data in order to drive regulatory improvements.”

The regulations set by the Federal Network Agency at the end of November 2023 for the integration of controllable consumption units will provide a further opportunity to use AI to monitor the grid load and correspondingly “dim” the controllable systems in the event of an imminent overload.

Risks and dangers of artificial intelligence

Although AI offers many advantages, there are also risks and dangers associated with it. It is important to be aware of these and to take appropriate measures to minimize them. One of the main criticisms of artificial intelligence is data security and data protection. As described in the previous section, the collection, analysis and storage of data form the basis for harnessing artificial intelligence. However, it must also be possible to ensure that this data is safe from cyber attacks and that data protection is complied with at all times. In the case of network processes fully automated by AI, for example, it must be ensured that hackers have no way of manipulating them to cause disruptions such as blackouts. However, an appropriately trained and therefore “intelligent” AI could also recognize such attempted attacks at an early stage.

Users as a danger

There are also risks that are not posed by artificial intelligence itself, but by its users. For example, there is a danger of interpreting the actions carried out by artificial intelligence as independent thinking, even though they are purely statistical calculations. AI systems make decisions strictly according to probabilities and data, without human intuition or “gut feeling”.

Overdramatization

Dr. Miriam Meckel also sees this risk of over-dramatization. She is considered one of Germany’s most important experts in the field of artificial intelligence. She emphasizes this in an interview on the topic of “Why we don’t need to be afraid of AI” as part of Katharina Wolff’s podcast “STRIVE up your Life”. In her opinion, it is completely unjustified to be afraid of the “extinction of humanity” or similar dystopian scenarios triggered by artificial intelligence. She explains the origin of these concerns with the fact that most people do not understand how artificial intelligence works, resulting in uncertainty and insecurity. However, this is not necessary: over time, people will get used to the fact that artificial intelligence will be part of everyday life and present in many different areas of life. Just as many people do not understand the technology behind everyday objects such as cars, but are nevertheless not afraid of it.

Abuse

Another risk lies in the potential misuse of AI. For example, the technology could be used for targeted disinformation campaigns and “fake news” to deliberately deceive people and, in serious cases, reinforce radical opinions. Meckel sees a serious problem here. Accordingly, a high priority should be placed on transparently labeling AI-generated content. Meckel calls for regulations to be able to distinguish generated data from real data, as otherwise there would be a high risk of misleading and spreading false information.

Risks for employees

Many employees fear that the increased use of AI tools in all sectors will lead to job losses. The energy sector is no exception. Many fear that artificial intelligence will work so precisely and efficiently that human workers will simply be replaced. However, this fear is not necessary either, says Miriam Meckel in an interview with Katharina Wolff. Although it is true that some jobs will be lost, history shows that innovations of this size create more jobs than they destroy. The use of AI in companies would create many new areas of work, such as the management, application and control of the targeted use of artificial intelligence.

Conclusion: The versatile future of artificial intelligence in the energy sector

The ongoing development and integration of artificial intelligence in the energy sector opens up a fascinating range of possible applications and potential benefits. From optimizing energy generation and increasing efficiency in sales to making grid operation more flexible, it is clear that AI is a transformative force for the entire energy supply value chain.

Data as a basis

However, the basic prerequisite for this change is the creation of a robust database. As Szilard Toth, CTO of epilot, emphasizes, the energy world is still in its infancy when it comes to the usability of data. A step-by-step approach, starting with collecting, storing and analyzing data, is essential to be able to use the full range of AI solutions. However, the challenges that arise should not be ignored. Companies are faced with the task of digitizing processes and making data usable for the application of AI.

The path to efficiently integrating artificial intelligence in the energy sector requires not only technological adjustments, but also a rethink in terms of data management and analysis. There are also some risks that you need to be aware of. Data security and data protection are key aspects that must be guaranteed in order to minimize potential threats from cyber attacks. The danger of over-dramatizing and misinterpreting AI decisions is another point that needs to be taken into account.

The goal is clear

The future of the digitalized energy industry with AI is characterized by innovations ranging from forecasting accuracy to sales and consumer integration. It is crucial to find a balanced approach that leverages the benefits of AI while taking ethical and societal challenges into account. The path to an intelligent energy future requires smart decisions, transparent regulations and, above all, the willingness to responsibly shape the transformative power of artificial intelligence.

The first step in creating the necessary data basis is a central software landscape. You can find out what this could look like in the following article: 11 tips for a future-proof software landscape for energy suppliers.