AI-based location identification for decentralized electrolysers (lecture language: German)
Jimmie Langham demonstrates how AI-supported geodata analysis combined with expert knowledge systematically identifies optimal locations—e.g., for co-location with wind farms and behind-the-meter concepts.
The selection of locations for electrolysers is complex – especially in the context of sector coupling and decentralised energy infrastructure. This presentation introduces an innovative, AI-based methodology that efficiently evaluates large amounts of geodata to identify suitable locations, e.g. in combination with wind turbines.
The entire location assessment system is explained, from comprehensive analysis and quality assurance to on-site testing. Using concrete project examples, Jimmie Langham shows how intelligent data processing, visualization, and manual testing can lead to the optimal “behind-the-meter” location—in a time-saving, scalable, and practical way.
Speakers (1)
