Christoph Goebel

Prof. Christoph Goebel is a Professor of Energy Management Technologies at the TUM School of Engineering and Design, Technical University of Munich. His research focuses on optimizing energy systems through advanced algorithms, machine learning, and data-driven approaches. Key areas include renewable energy integration, smart grid technologies, electric vehicle charging optimization, and the application of graph neural networks in power systems.

His work bridges theoretical advancements with practical implementation, such as developing platforms like e-SparX for collaborative machine learning in energy research and frameworks like EnergyOS for modular energy management systems. Recent studies explore thermal energy storage models, cost-effective CO2 reduction strategies via heat pumps, and the economic incentives of wind energy data sharing.

Goebel's publications (2016–2025) consistently emphasize predictive control, optimization under market constraints, and the integration of ICT innovations into energy infrastructure. Notable contributions include transfer learning models for building thermal dynamics and benchmarking frameworks for energy hardware-software systems.