ExoLabs wins EU funding
- July 28, 2020
- Posted by: EARSC
- Category: Members News
ExoLabs is one of 15 winners of the EU Parsec Accelerator. For its snow monitoring and forecasting system the start-up receives financial and operational support. For example, it can help hydropower companies worldwide to generate energy more profitably.
ExoLabs, a spin-off from the University of Zurich (UZH), is among the winners of the European Parsec Accelerator. The EU’s Parsec programme accelerates 15 new products and services which, in the opinion of the expert jury, are needed on the global market and are used in the food, energy or environmental sectors. Each of the selected projects will receive 100’000 Euro as equity-free financing. They will also be offered support in the form of coaching, business contacts and market introduction.
ExoLabs applied together with the Norwegian company Think Outside and UBIMET from Vienna with the project “Snow Information for Hydropower”. The Zurich-based start-up uses remote sensing as a tool to monitor environmental changes such as changes in snow water storage. The project, now selected by the EU, focuses on hydroelectric power. The consortium will provide operators of hydroelectric power plants with accurate predictions of snow melt and water flow. This helps them to generate energy as profitably as possible. Based on global satellite observations, scalable cloud computing and machine learning techniques, the team can serve hydroelectric power plants worldwide.
In Switzerland, Exolabs recently won the national final of Climate Launchpad. The world’s largest ecological competition for green business ideas aims to unleash the cleantech potential to combat climate change. Innosuisse is also supporting a project involving ExoLabs, the EcoVision Lab of the Swiss Federal Institute of Technology Zurich (ETH) and other partners such as Outdooractive, MountaiNow and the Davos Institute for Snow and Avalanche Research (SLF). The aim of this project is to monitor the snow depth in the mountains with deep learning techniques.