Fraunhofer Institute for Systems and Innovation Research ISI
Fraunhofer Institute for Systems and Innovation Research ISI

How does AI contribute to an efficient energy transition?

Research questions

  • How can AI be used to make the energy transition efficient and sustainable?
  • How do AI systems have to be designed so that electricity consumers use them?
  • What impacts does the use of AI have on energy consumption?
     

Projects

Implications of European data strategy and regulation for the energy industry 

As part of its data strategy, the EU has created a far-reaching legal basis for digital value creation through data in Europe – including in the energy industry. While the Data Act greatly expands data availability in the energy system, the Data Governance Act regulates trustworthy transaction options for this data.

Under the regulations that will apply in the future, operators of wind turbines, for example, will be entitled to all data generated during the use of the turbines. This includes, for example, extensive sensor data that is essential for operational monitoring and predictive maintenance. Until now, the turbine manufacturer has had exclusive access to this data as the data owner and has also sold the rights of use to the operators.

Together with the Fraunhofer Cluster of Excellence for Integrated Energy Systems (CINES), a white paper was developed that analyzes the effects and potential of the European data strategy, provides an understanding of the new regulations, and outlines specific options for action for players in the energy industry. 

EnArgus 3.0 – AI-supported information system for energy research funding 

The aim of the EnArgus 3.0 joint project was to adapt and improve the central information system for energy research funding in terms of both concept and content in line with new scientific findings. Using the latest AI methods, current energy research content was made accessible and available to public authorities and the general public in a simple and transparent manner.

The project focused in particular on how the dynamic development of energy research topics can be taken into account in an information system and to what extent the ontologies used for this purpose can be supported by AI methods. 

DiMA Grids – Digital business models with self-determined users for smart distribution networks

This joint project further developed existing approaches to digitizing the energy transition. It investigated how distribution grids can be controlled more flexibly and automatically and how end customers can actively control their consumption via smart meters. The project also identified digital business models based on platform economies and proposed implementation strategies. 

New Trends in energy demand modeling

Digitalisation has the potential to fundamentally change the way things are currently performed. While most digitalised processes are getting more and more efficient, not all of them are designed in a way that reduces energy consumption. New societal trends empowered by digitalisation such as shared economy, autonomous driving and low-carbon circular economy could lead to an increase in energy demand if they are not countered by measures with a strong focus on saving energy. The goal of the EU-funded NEWTRENDS project is to identify and quantify how new societal trends may affect energy demand. To achieve its goal, it will combine qualitative and quantitative cross-sectoral modelling and explore how energy demand models can be improved to represent new societal trends.

Artificial Intelligence for the integrated energy transition

The analysis report, written as part of the BMWi-funded project “EnerAI – using artificial intelligence to optimize the energy system”, aims to make the alluring term of AI more objective and place this in the context of the integrated energy transition. The analysis shows that AI can be used in a wide variety of applications in all areas of the energy sector and can make a major contribution in future to a more secure, climate-friendly and cost-efficient energy supply.  

Literature