Target Group: Energy Sector


Due to the large number of spatially distributed generators, consumers and prosumers, the uncertainties, but also the optimisation possibilities for an economic and secure energy supply increase enormously.

It is no longer possible to manually carry out comprehensive control and reliable monitoring of system properties. The multitude of data streams from the individual energy grids, plants and markets that have been built up in recent years can only be meaningfully monitored, evaluated and integrated into innovative applications using intelligent data-driven methods. AI will play a decisive role here.

The AI service for the energy sector is aimed at:   🔗

  • Operators of energy networks (electricity, heat, gas).
  • Energy suppliers and electricity traders
  • Planners, developers and operators of renewable energy plants
  • Planners, developers and operators of decentralised, multimodal energy supply systems
  • Developers and operators of energy management systems
  • IT service providers and control system manufacturers

Areas of application in the energy industry:   🔗

  • Challenges in the feed-in of renewable energies (e.g. wind or solar energy).
  • Coupling of sectors (electricity, heat, transport)
  • Intelligent management of controllable consumers
  • Automated grid operation

Examples   🔗

of AI-driven applications in the energy sector that have been developed in the KISSKI consortium:

AI-InnovA    🔗

AI-InnovA: Stochastic optimisation of wind-flex systems for innovation call for tenders using reinforcement learning

Responsible consortium partner: Fraunhofer IEE

VemoSat    🔗

VemoSat: Satellite-based vegetation monitoring for energy infrastructures

Responsible consortium partner: Fraunhofer IEE

Power-to-X Atlas    🔗

Power-to-X Atlas: Map visualisation of Power-to-X potentials

Responsible consortium partner: Fraunhofer IEE