Synthetic data is a game-changer for the simulation of residential energy demand. Join us at the 6th Synthetic Data Meetup to learn more about important research our guest speaker, Max Kleinebrahm is working on at the Karlsruhe Institute of Technology, modeling residential energy systems using synthetic data!
Register for the virtual meetup and save the date: Thursday, May 6, 12:00 pm (EDT)
What is the 6th Synthetic Data Meetup going to be about?
Models simulating household energy demand based on occupant behavior have received increasing attention over the last years due to the need to better understand fundamental characteristics that shape residential energy.
In this Synthetic Data Meetup, hosted by Dr. Paul Tiwald, Head of Data Science at MOSTLY AI, we will present deep learning methods ready to capture complex long-term relationships in occupant behavior that can provide high-quality synthetic behavioral data.
The generated synthetic dataset combines various advantages of individual empirically collected data sets and thus enables a better understanding of residential energy demand without collecting new data with great effort.
Our guest speaker is Max Kleinebrahm, Research Assistant at the Chair of Energy Economics at the Karlsruhe Institute of Technology in Germany. Max’s research interests are renewable energies, decentralized energy systems, energy self-sufficient residential buildings, and time series analysis of energy consumption and occupant behavior. In his PhD, he is investigating the dissemination of self-sufficient residential buildings in the future European energy system.
Join the next Synthetic Data Meetup to learn how household energy demand can be simulated with high-quality synthetic behavioral data!