Applied Machine Learning in Hydropower

One of Europe’s largest generators of renewable energy and leading company in hydropower set out to explore the potential of digitalization for its hydropower plants. Our team of consultants developed a machine learning platform, the surrounding backend infrastructure as well as visualization tools to explore and analyse sensor data for both specific use cases as well as across the entire fleet.


We explore the data in close collaboration with the maintenance team on site and apply state-of-the-art machine learning algorithms to monitor various kinds of failure modes from slowly developing failures over time to repeating failures in the auxiliary equipment. This kind of condition monintoring supports the daily maintenance by improving the predictability of wear and asset failures, thus reducing operating costs and the need for costly and time-consuming manual inspections.


Vinzenz Gregor Eck

Vinzenz is a curious, pragmatic, creative problem solver. He holds a PhD in biomechanical engineering, focusing on stochastic simulations of the blood flow in the human arterial system. This interdisciplinary work included working within engineering, medicine, biology, software development and statistics.