• Increasing complexity of (industrial) energy systems and energy procurement due to volatile markets
  • Increased requirements to achieve sustainable and cost-efficient production
  • Interdisciplinary knowledge necessary to achieve energy & cost efficiency
  • Increasing amount of data to handle due to rise of IoT devices
  • Limited information and ressources in energy teams lead to sub-optimal systems & operation strategies
  • Structure, dimension and operation of energy systems need to be optimized for cost efficiency
  • Energy markets, production and weather forecast integrations lead to unprecedented cost advantages
  • Efficient tools are needed to support energy team on the complex and interdisciplinary optimization tasks
  • Artificial intelligence is able to handle huge amount of data and to identify optimization measures
  • Digital twins can be used to generate synthetic data in simulations for different environment scenarios

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