Towards the Detection of Building Occupancy with Synthetic Environmental Data
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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.
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