The percentage of fluctuating energy sources in our power supply is growing at a steady pace. Precise chronological forecasts of power feed-in are thus playing an increasingly important role. Wind, photovoltaics and hydroelectric power feed-in forecasts are already essential parameters in the management of transfer and distribution grids.



Anton Kaifel
+49 711 78 70-238

// Regional AI Lab for Renewable Energies

Artificial intelligence (AI) is being used more and more frequently in the energy turnaround sector. Self-learning methods help to better predict wind and solar power feed-in or to optimize production processes for photovoltaic modules, batteries and fuel cells. However, small and medium-sized enterprises in particular still use AI technologies too rarely. For many years, the Centre for Solar Energy and Hydrogen Research Baden-Württemberg (ZSW) has been developing innovative processes and methods of AI or machine learning (ML) within the scope of research and development projects in the field of renewable energies.  

In April 2020, the ZSW launched the "Regional AI Lab for Renewable Energies" project, which is funded by the state of Baden-Württemberg. The aim of the AI Lab is primarily to support small and medium-sized companies in BW in order to offer new products and services using artificial intelligence and thus gain a competitive advantage. If you are interested in consulting on how you can make more out of your data with machine learning, please contact us. The consulting and first application of machine learning methods with your data are free of charge within the AI Lab. We are looking forward to your questions and your contact.

// The latest algorithms for machine learning methods

ZSW researches application-oriented technologies that raise the accuracy of forecasts of the complex interaction between weather model forecasts, satellite data and meteorological measurements and the historic and future wind, solar and hydroelectric power yields of a specific site or region.

The latest algorithms for machine learning methods play an important role in this context. They are capable of significantly improving physically calculated wind, solar irradiation and output forecasts by employing long-term measurement series that help them learn the interaction patterns between the numerous parameters that cannot be covered by physical models and apply the results to the respective current weather situation. Systematic deviations that generally occur in physical models are automatically corrected.