Extreme weather conditions and climatic changes highlight the urge for the decarbonisation of the energy sector. A potential strategy is the integration of (variable) renewable energy sources. However, aspects like the mismatch between availability of these sources and the demand on different time scales from hours to months generate severe challenges for the energy system as well as their safe operation.

This track brings into focus challenges of the energy transition and their solutions in real-world projects. Examples include the decarbonisation of heating and cooling in Switzerland within the next three decades. The potential of AI to help increasing the share of renewable energy in the electricity system, provide options for flexibility of demand without losing user comfort, and support the coupling of different energy carriers are also complex topics. In those, research and innovation driven by data science faces challenges at every stage in the value chain from data assimilation to the robust implementation of artificial intelligence systems.

We aim at bringing together perspectives and experiences of real-world applications of AI for the energy turnaround as well as experiences on crowdsourcing data science challenges, such as those pursued in hackathons. Furthermore, data engineering aspects such as standardization of data from the energy systems components, the role of actionable data (e.g. machine readable data), and approaches that preserve privacy will also be covered. Moving further down the value chain of AI solutions, we look for presentations of novel models or products that enable a collective reduction of energy consumption to support reduction of carbon footprint.