Detecting Building Occupancy with Synthetic Environmental Data

Abstract

Information about room-level occupancy is crucial to many building-related tasks, such as building automation or energy performance simulation. Carbon dioxide levels and other indoor environmental factors can be used as a proxy to detect occupancy. In this regard, machine learning solutions have been proposed, with solid performance in detecting presence, as well as counting the number of present occupants, if enough training data is available. The challenge is, to collect sufficient room-specific ground truth data for model training. With this poster, we address the use of knowledge transfer from synthetic data to reduce the amount of required real world data. We outline two approaches for the combination of transfer learning with physical simulations, and motivate the generation of additional synthetic data. Our results show that the required real world training data can be reduced by 50%.