The Energy Transition in Germany Requires an AI-Supported Dynamic Control of the Power Supply Network

This position work is based on the best practices of Smart Grid, chosen case studies for energy efficiency, and an in-depth analysis of energy conversion problems in Germany. In addition, real examples based on the authors’ own experiences with energy-efficient Smart Home were conducted. The focus has been shifted to the requirements for modern power grids due to the volatility of renewable energies. Furthermore, the discussion of dynamic network control problems that can be solved by infrastructure approaches such as Smart Grid, NGN with 5G and Beyond, as well as Smart Home, and, certainly, by Artificial Intelligence (AI) methods that will play an increasingly important role. In particular, we proposed a proof-of-concept test-bed for AI-supported dynamic control of the smart grid. We have successfully developed a solar power generation forecasting model based on the Long Short-Term Memory (LSTM) algorithm and implemented an automatic underfloor heating control system that uses these forecasts to optimize energy usage in a private household. We achieved a high accuracy of 91% in forecasting solar power generation and ensured efficient energy management based on conditions. We mean that this is a so-called work-in-progress. The obtained results can be also distributed into larger economic objects: power supply networks for communities, enterprises, geographic areas, and industries.