Towards the Detection of Building Occupancy with Synthetic Environmental Data

• URL: http://arxiv.org/abs/2010.04209v1
• Date: Thu, 8 Oct 2020 18:41:48 GMT
• Title: Towards the Detection of Building Occupancy with Synthetic Environmental Data
• Authors: Manuel Weber, Christoph Doblander and Peter Mandl
• Abstract summary: Current occupancy detection literature focuses on data-driven methods, but is mostly based on small case studies with few rooms. To derive accurate predictions from less data, we suggest knowledge transfer from synthetic data.
• Score: 0.7375976854181688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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