Artificial intelligence is more relevant than ever. It is an unavoidable topic that is becoming increasingly important across industries. This article looks at the various ways to use artificial intelligence in the energy sector.
What is artificial intelligence and how does it work?
that European Parliament defines artificial intelligence as the imitation of human abilities through 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 base being analyzed, the AI's response also varies. The core of AI technology therefore lies in data processing. Existing data is analyzed by AI systems to identify patterns and relationships. These findings are 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?
Currently, the best-known application of AI technology is “ChatGPT,” a language model based on artificial intelligence. It is able to have 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 hardly any limits to the application. Following its release in November 2022, over 100 million people used the Consumer application. The basis for successful use of ChatGPT is the formulation of good “prompts”. These are the instructions that you give to artificial intelligence.
However, artificial intelligence technology has countless other uses and is therefore of great use in a wide variety 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. There are opportunities in agriculture to use AI to optimize crop yields and reduce the use of pesticides. In addition, AI is also being used more and more in creative industries, such as art, poetry or music.
A basis is necessary
Before the many benefits 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. First, a secure basis must be created with which to work. This basis is created through data science and data management.
Toth says about this: “The energy world is still in its infancy when it comes to the usability of data. Everyone must become more data-driven. However, there is a gap between the current situation and the efficient analysis, evaluation and processing of data, particularly in conjunction with artificial intelligence. Most houses do not yet have a general collection point for data, let alone a system for analyzing it.”
The importance of data is therefore enormous and it is urgently necessary to use this potential. However, many companies in the energy sector are stuck in a conflict between the need to take the first step, on the one hand, and the desire to immediately take big steps towards a digitalized energy industry. This can lead to frustration when progress isn't being made as quickly as you'd hope for.
How do energy companies solve this problem?
In order to be able 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 so far there are only a few energy suppliers and network operators who meet the technical requirements to implement such systems. This applies both to the required hardware 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 towards efficient data management.
For many companies, combining complex data management with their core business is also a challenge. They do not see how data analysis and artificial intelligence fit directly with their energy solutions and therefore do not recognize the need to change their systems and processes to use artificial intelligence.
Szilard Toth sees another important aspect when it comes to benchmarking: “Many companies build their own solutions, but work in closed systems and don't know what's happening in other companies. A platform that collects data from various companies anonymously 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 have made further progress in this area. They have systems to use data lakes and analyze data.
In general, companies in the energy sector must digitize and accelerate processes in a wide range of work areas in order to become more efficient. In many areas, artificial intelligence can help digitize the energy industry. A step-by-step approach is the key to creating a solid basis, 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 Competence Center for 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, distribution and consumer.
forecast accuracy
Forecast accuracy is crucial for planning renewable energy generation plants. Artificial intelligence can help, for example, to improve forecasts of solar radiation and wind speed and thus improve the quality of forecasts.
Sales
In sales, artificial intelligence in the energy sector can help speed up inefficient processes and thus reduce costs. In the core business of energy supply companies, costs must be reduced while at the same time expanding the portfolio into new areas such as energy solutions. Here, it is important to try out, measure and optimize new approaches.
customer service
Another approach is to use artificial intelligence to help employees or even customers identify problems and solve them themselves with the help of AI. Here, for example, AI chatbots trained for specific use cases can answer initial questions and provide assistance in a dialogue. 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 in energy trading. Analyzing the energy market through 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. Furthermore, the so-called Algorithmic trading automated energy placements on the market, i.e. independent trade through artificial intelligence.
More options
In addition, there are other future fields of energy supply in which AI can be used, such as when determining suitable areas for expanding wind energy. A wide variety of complex factors, such as the recognition of endangered bird species, can be included. Consumers can also benefit from the use of artificial intelligence. In addition to existing smart home solutions, the devices in a smartly connected home could collect data about user behavior and use this as a basis to save as much energy as possible. In addition, factors such as prices on the electricity market can also be included in order to keep costs as low as possible for consumers.
Applications of AI for network operators
AI can make grid operation more flexible by enabling complex status determinations for power grids. In a practical test, a self-learning agent has shown that he can handle fluctuating feeds and loads, maintenance work and attacks. AI can help make decentralized energy generation from renewable energy sources more efficient by coordinating the energy system in real time and reacting automatically.
The resilience of the power grid can also be improved through artificial intelligence. AI systems can identify faults and provide possible repair advice to minimize downtime. However, Szilard Toth notes: “The grid sector also faces challenges such as grid compatibility and congestion. Here, too, it is crucial to collect and analyze data to drive regulatory improvements.”
By the appointed Federal Network Agency regulations at the end of November 2023 To integrate controllable consumption units, there is another possible use for AI to monitor the network load and corresponding “dimming” of the controllable systems in the event of an impending overload.
Risks and dangers of artificial intelligence
Although AI offers many benefits, there are also risks and dangers associated with it. It is important to know them and take appropriate steps to minimize them. One main criticism of artificial intelligence is data security and data protection. As described in the previous part, collecting, analyzing and storing data form the basis for making artificial intelligence usable. However, you must also be able to ensure that this data is safe from cyber attacks and that data protection is maintained at all times. For network processes that are fully automated by AI, for example, it must be ensured that hackers have no opportunity to manipulate them in order to create disruptions such as blackouts. However, an appropriately trained and therefore “intelligent” AI could also identify such attack attempts at an early stage.
Users as a threat
In addition, there are also risks that do not come from artificial intelligence itself, but from its users. For example, there is a risk of interpreting the actions carried out by artificial intelligence as independent thinking, even though these are purely statistical calculations. AI systems make decisions strictly based on probabilities and data, without human intuition or “gut feeling.”
Overdramatization
Dr. Miriam Meckel also sees this risk of overdramatization. She is considered one of Germany's most important experts in the field of artificial intelligence. In interview on the topic “Why we don't have to be afraid of AI” as part of the STRIVE up your life podcasts by Katharina Wolff, she emphasizes this: In her opinion, it is completely unjustified to be afraid of the “annihilation of humanity” or comparable dystopian scenarios triggered by artificial intelligence. She explains the origin of these concerns by the fact that most people do not understand how artificial intelligence works, resulting in uncertainty and uncertainty. However, this is not necessary: over time, people will get used to the fact that artificial intelligence will be part of everyday life and will be present in many different areas of life. Just as many people don't understand the technology behind everyday objects such as cars, but still aren't 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 consciously deceive people and, in bad cases, strengthen radical opinions. Meckel sees a serious problem here. It should therefore be a high priority to transparently label AI-generated content. Meckel demands 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 workers
Many employees fear the loss of jobs as a result of the increased use of AI tools in all industries. 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 Interview with Katharina Wolff. While 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 administration, application and control of the targeted use of artificial intelligence.
Conclusion: The diverse future of artificial intelligence in the energy sector
The progressive development and integration of artificial intelligence into the energy sector opens up a fascinating range of applications and potential benefits. From optimising energy generation to increasing efficiency in sales and making network operation more flexible, it is clear that AI is a transformative force for the entire energy supply chain.
Data as a basis
However, the basic requirement for this change is the creation of a robust database. As Szilard Toth, epilot's CTO, points out, 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 use the full range of AI solutions. However, the challenges that arise are not negligible. Companies are faced with the task of digitizing processes and making data usable for the application of AI.
The way to efficiently integrate 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 also need to be aware of. Data security and data protection are key aspects that must be ensured in order to minimize potential threats from cyber attacks. The risk of overdramatization and incorrect interpretation of AI decisions is also a point that must be considered.
The goal is clear
The future of the digitized energy industry with AI is characterized by innovations ranging from forecast accuracy to sales and consumer integration. It is crucial to find a balanced approach that harnesses the benefits of AI while taking into account ethical and societal challenges. The path to an intelligent energy future requires clever 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.
