Daniel Bayer works as a research assistant at the Chair of Computer Science (Modelling and Simulation). He first studied mathematics with a Bachelor of Science degree, then computer science at the Friedrich-Alexander-Universität Erlangen-Nürnberg.
His work focuses on the research project of DigiSWM. Here, one of the central goals is to combine extensive energy data with AI methods to simulate and evaluate new energy services.
In particular, his research interests focus on the following topics:
- data-driven or agent-based simulations, especially in the energy & building sector
- digital twins of local energy systems
- data analysis of time series
- multi-agent reinforcement learning
- sustainable computing
A digital twin of a local energy system based on real smart meter data in Energy Informatics (2023). 6(1)
Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems in 2022 IEEE Conference on Technologies for Sustainability (SusTech) (2022). 187–194.