  Information about room-level occupancy is crucial to many building-related
tasks, such as building automation or energy performance simulation. Current
occupancy detection literature focuses on data-driven methods, but is mostly
based on small case studies with few rooms. The necessity to collect
room-specific data for each room of interest impedes applicability of machine
learning, especially data-intensive deep learning approaches, in practice. To
derive accurate predictions from less data, we suggest knowledge transfer from
synthetic data. In this paper, we conduct an experiment with data from a CO$_2$
sensor in an office room, and additional synthetic data obtained from a
simulation. Our contribution includes (a) a simulation method for CO$_2$
dynamics under randomized occupant behavior, (b) a proof of concept for
knowledge transfer from simulated CO$_2$ data, and (c) an outline of future
research implications. From our results, we can conclude that the transfer
approach can effectively reduce the required amount of data for model training.
