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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