Occupancy estimation using IoT sensors and machine learning: Incorporating ventilation system operating state and preprocessed differential pressure data

Abstract

Indoor occupancy should be measured to reduce energy consumption in buildings and predict infection risk. Various studies used machine learning to measure occupancy using carbon dioxide concentration, Wi-Fi probes, and camera image data. The carbon dioxide concentration depends on factors such as whether doors and windows are open and whether ventilation systems are running. Carbon dioxide concentrations fluctuate depending on variables, even with the same occupants in the room. We estimated the number of occupants using indoor environmental data for considering the changes in the carbon dioxide concentration caused by these variables. The differential pressure and operating state of a ventilation system were used as input variables for machine learning. Data were acquired from a living lab, which is used for seminars, lectures, etc. For up to 50 people. We used 85,366 data for training and 8418 data for validation without training. The accuracies of Artificial Neural Network (ANN), random forest, and support vector machine models were compared for different input variables. This study considered the carbon dioxide concentration fluctuation due to leakage as an input variable coupled with differential pressure sensor data. We compared whether the accuracy increases when differential pressure data are used. In the ANN model, the lowest root mean squared error is obtained when the carbon dioxide concentration, differential pressure data, and operating state of the ventilation system are used as input variables. In the future, we plan to increase the accuracy by utilizing various Internet of Things sensors and adopt diversify the input variables.