Towards the Detection of Building Occupancy with Synthetic Environmental Data

TL;DR

This paper explores using synthetic CO₂ data generated from simulations to improve building occupancy detection, demonstrating that knowledge transfer can reduce the need for extensive real-world data.

Contribution

It introduces a simulation method for CO₂ dynamics, demonstrates knowledge transfer from synthetic to real data, and discusses implications for building occupancy detection.

Findings

Transfer from synthetic to real data reduces training data requirements.

Synthetic data can effectively model CO₂ dynamics under varied occupant behaviors.

Knowledge transfer improves occupancy detection accuracy with less real data.

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.