Non-intrusive indoor environmental data in two double-occupied offices

The dataset is derived from a longitudinal study conducted in two double-occupied offices at the Munich University of Applied Sciences. Focusing on occupancy detection and window state prediction, the study leverages an IoT sensory device placed desk-level near occupants. This dataset includes various indoor environmental metrics: Air temperature, CO2 concentration, indoor air quality, illuminance, relative humidity, and sound pressure level. Ground truth occupancy data was recorded manually by occupants, while window status was relayed via an EnOcean-protocol-enabled contact. Detailed sensor information is presented in the linked article.