Track / Overview
To reach the goals of the Swiss federal energy strategy 2050, the share of renewable energy has to be substantially increased as well as the efficiency in the energy system has to be improved. Strategies to reach these goals include the reduction of the energy demand by building (fabric) renovation, the optimisation of industrial processes, the improvement of the energy distribution, and the improved integration of renewable energy sources into the energy system.
The development of solutions to these problems requires novel approaches combining technical, social, and environmental sciences. A key driver for novel insight is the application of machine learning (ML) and artificial intelligence (AI) methods to analyse and model the massive data from components, markets, and the environment. Applications of data science range from curve fitting, pattern recognition tasks, to segmentation, optimisation and control.
A key aim of this track is to connect data experts providing novel methods for the analysis and modelling of the data sets with energy experts applying ML and AI to solve their challenging problems on the demand and the production sides to increase the share of renewable energy sources, to achieve net-zero energy buildings and overall an energy system free of carbon emissions during its operation.
Track / Speakers
Thomas ChenStudent, Academy for Mathematics, Science, and Engineering & Head of Outreach, Climate Data Hub
Track / Co-organizers
AI & Pharma
Simone Lionetti, Camille Marini, Enkelejda Miho, Jonas Richiardi, Kurt Stockinger, Patrick Schwab, Kostas Sechidis, Lisa Herzog, André Jaun, Khaled El Emam, Cécile Louwers, Jason Plawinski, Limor Shmerling Magazanik, Agata Mosinska, Marius Garmhausen09:00-17:00 August 23Online