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arXiv · 2010.04209

Towards the Detection of Building Occupancy with Synthetic Environmental Data

Manuel Weber·Christoph Doblander·Peter Mandl
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Abstract

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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Manuel Weber, Christoph Doblander, Peter Mandl. 2020-10-08. Towards the Detection of Building Occupancy with Synthetic Environmental Data. https://doi.org/10.1145/3408308.3431124

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