But what is intelligence exactly? Various fields of research have already attempted to define intelligence - and have come to different conclusions. A central aspect of intelligence in AI is that it makes decisions based on information and carries out actions with regard to its goals. Under certain circumstances, this includes collecting this information and reacting flexibly to changes and the environment. In other words, this means that the AI learns from experience and makes new decisions independently .
Even with this definition, the term remains difficult to grasp. In practice, therefore, it is common to speak of strong or weak AI. A strong AI is one in which the application has all aspects associated with human intelligence, such as the ability to draw logical conclusions, the existence of general knowledge, the ability to learn to perceive and understand language, to plan and foresee, to move and manipulate objects, and to recognize emotions.
Another common definition of AI is that Artificial Intelligence is a subdiscipline of computer science that aims at enabling machines to perform tasks intelligently. Although computer science is very important in AI, Artificial Intelligence is applicable to other fields, such as statistics, robotics, linguistics, or philosophy.
There is still a discussion about what is already considered AI and what is still the computing power of machines. The transitions are fluid, not least because of the definitional inaccuracies.
The term Machine Learning (ML) is often used in connection with AI and is of great importance in the energy industry. However, ML and AI are not the same, since Machine Learning includes a part, but not all of the AI. Machine Learning means that machines can learn independently, i.e. draw conclusions for the future from their experiences and solve problems that have not been there before.
Artificial Intelligence (AI) in the Energy Industry
Artificial Intelligence becomes more and more important in the energy industry and is having great potential for the future design of the energy system. Typical areas of application are electricity trading, smart grids, or the sector coupling of electricity, heat and transport. Prerequisites for an increased use of AI in the energy system are the digitalization of the energy sector and a correspondingly large set of data that is evaluable. AI helps make the energy industry more efficient and secure by analyzing and evaluating the data volumes.
Artificial Intelligence in the Power Grid - Smart Grids and Sector Coupling
In particular, AI is present in the field of intelligent networking of electricity consumers and generators across sector boundaries. With the increasing decentralization and digitalization of the power grid, it is becoming more difficult to manage the large number of grid participants and keep the grid in balance. This requires evaluating and analyzing a flood of data. Artificial Intelligence helps process this data as quickly and efficiently as possible.
Smart grids are another area of application. These networks transport not only electricity but also data. Especially with an increasing number of volatile power generation plants such as solar and wind, it is becoming more and more important for power generation to react intelligently to consumption (and vice versa). AI can help evaluate, analyze, and control the data of the various participants (consumers, producers, storage facilities) connected to each other via the grid.
A particular focus of AI in the energy industry is on the integration of electro mobility. An increase in e-cars offers opportunities and challenges. The charging of electric cars must be coordinated, but at the same time, they offer the possibility of storing electricity and stabilizing the grid, for example by adjusting the charging demand to price signals and availability. AI can help with all this by monitoring and coordinating.
In addition, the AI can stabilize the power grid by, for example, detecting anomalies in generation, consumption, or transmission in near real time, and then develop suitable solutions. Initial research projects in this field, such as at the Fraunhofer Institute, are already underway.
Further, AI can help coordinate maintenance work and determine optimal times for the maintenance of networks or individual systems. This helps minimize costs and loss of profit as well as disturbances of the network operation.
Artificial Intelligence in Electricity Trading
Artificial Intelligence in power trading helps improve forecasts. With AI, it is simpler to evaluate systematically the large amount of data in electricity trading, such as weather data or historical data. Better forecasts also increase grid stability and thus supply security. Especially in the field of forecasts, AI can help facilitate and speed up the integration of renewables . Machine Learning and Neural Networks play an important role in improving forecasts in the energy industry.
Developments in forecasting quality in recent years have shown the potential of AI in this area: There is already a reduction in the demand for control reserve, even though the share of volatile power generators in the market has increased.