PARSEC Accelerator Open Call 2 concluded last Friday by selecting the 15 teams that will receive access to €100.000 in equity-free funding and the PARSEC second stage business support services. Exolabs is among the winners. The start-up recently also convinced support organisations in Switzerland.
The PARSEC Demo Days consisted of two intensive pitching days on the 2nd and 3rd of July 2020. In total, 60 consortia consisting of start-ups and SMEs had their businesses evaluated by a jury of experts during the PARSEC Demo days. This marked the end of the PARSEC Open Call 2 which aimed to select the 15 best Earth Observation based innovations for the food, energy or environment sectors. The PARSEC Accelerator is funded by the “Horizon 2020” Research and Innovation programme of the European Union.
Among the selected innovations is the project Snow information for Hydropower by ExoLabs, Think Outside and UBIMET. “We are all very much looking forward to provide the most accurate snow monitoring and forecasting system. Thanks PARSEC for the recognition!» comments the team in a LinkedIn post.
In Switzerland Exolabs has recently also won the national finals of Climate Launchpad. In addition a project with the EcoVision Lab at the Swiss Federal Institute of Technology (ETH), the start-up and further partners including the WSL Institute for Snow and Avalanche Research SLF, Outdooractive, and MountaiNow is supported by Innosuisse. The project aims to measure snow cover in the mountains in real time.
Exolabs, a spin-off from the University of Zurich, uses remote sensing as a tool to understand and monitor the influence of human activities on the environment. State-of-the-art data analysis techniques enable the simulation, evaluation, and ultimately prediction of environmental changes and their interactions with other natural processes and human activities.
One application field is hydropower. Hydropower operators need accurate water inflow predictions to generate energy most profitably. In snowmelt dominated catchments (1/4 of the world’s hydropower), uncertain snowmelt quantities deteriorate inflow predictions resulting in overall losses of 7-10%, annually.
Exolabs’ solution to this problem includes data from multiple satellite constellations, ground station networks and numerical weather forecasts to derive real-time snow quantities in a 20 m spatial resolution for entire mountain ranges. Using scalable cloud processing and innovative machine learning algorithms, the start-up is capable to serve hydropower stations globally.
(Press release / SK)
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