Privacy-preserving Occupancy Detection in Smart Buildings: Three Weeks of Data from Infrared Array Sensor

The dataset contains temperature measurements taken with an 8x8 infrared array (Panasonic Grid-EYE) over a period of three weeks during 2018 in Bucharest, Romania, in an educational facility.

The experimental system, composed of a Panasonic Grid-Eye development kit and an associated Raspberry Pi wireless gateway, has been deployed for three weeks in an IT Hub where young students are taking classes of programming and robotics. We found this scenario very appealing since we have previously deployed our equipment in the university laboratory where adults are using the spaces, but this one is from another perspective since the young students have different behavior: they are faster when they enter in the room, they are walking in groups of two often, and they have a much smaller height than adults; this means a larger distance to the sensing grid places on the top of the doorcase. Data is recorded with a frequency of 1 Hertz, in frames of 64 values of temperature in degrees Celsius, correspond- ing to the 64 cells of the sensing grid. 

We hope the dataset is useful to researchers in building energy efficiency and user comfort in order to develop algorithms that infer and predict the occupancy levels in smart buildings for optimal HVAC control schemes.

A two-stage method to first detect and subsequently predict occupancy using random forest algorithms using data is presented and can be referenced below:

Grigore Stamatescu, Claudia Chitu, "Privacy-Preserving Sensing and Two-Stage Building Occupancy Prediction Using Random Forest Learning", Journal of Sensors, vol. 2021, Article ID 8000595, 12 pages, 2021. https://doi.org/10.1155/2021/8000595