Forecasting Renewable Energy using Machine Learning
Date: Tuesday, 10 Mai, 17:00-18:00
Speaker: Dr Nicole Ludwig
Location: Join in HG D7.2 or via external page Zoom
Speaker Bio
Nicole Ludwig leads the Early Career Research Group "Machine Learning in Sustainable Energy Systems" at the Cluster for Excellence "Machine Learning: New Perspectives for Science" at the University of Tübingen. She develops new machine learning algorithms that enable and support a sustainable energy system of the future. Her research focuses primarily on probabilistic machine learning to understand and quantify uncertainty in sustainable energy systems. Her work has been awarded several best paper awards from leading conferences in energy informatics. Before coming to Tübingen, Nicole Ludwig studied in Freiburg and Oslo and did her PhD in Karlsruhe and Oxford.
Abstract
Most mitigation strategies to face climate change involve the electrification of our grid and a shift towards clean energy resources, thus no fossil fuels. However, without fossil fuels, we have to rely on more volatile renewable energy sources, and the energy system is facing unprecedented challenges which call for new methods and perspectives. These challenges include, among many others, more uncertainty in energy generation through complex interactions with the weather and climate and active participation of consumers through, for example, PV panels on household rooftops. This talk will introduce challenges when forecasting weather-driven renewable energy time series and introduce solutions focusing on (primarily probabilistic) machine learning.