Automated detection of commissioning changes in connected lighting systems

We consider the problem of detecting commissioning changes in connected lighting systems. Commissioning changes can occur due to repositioning of luminaires/sensors or space renovation. This results in incorrect commissioning mapping of devices to areas, thereby impacting analysis and interpretation of data from such devices. We propose an automated method to detect changes in commissioning mapping using occupancy sensor data. We use similarity features across occupancy sensors and employ a random forest binary classifier to detect changes. The proposed method is evaluated using data from a simulated office environment and an experimental testbed, and is shown to have high accuracy.