A real-time occupancy detection system for unoccupied, normally and abnormally occupied situation discrimination via sensor array and cloud platform in indoor environment

It is significant to detect the occupancy of indoor environment from the view of saving energy. In addition, occupied situation detection can help control the density of people in room to reduce the risk of disease transmission. In preliminary, we trained nine algorithm models on the existing occupancy dataset to select the optimum algorithm. Then, all-subsets regression model is used for feature selection (sensor contribution evaluation) to optimize the size of sensor array. As a result, the voting based weighted extreme learning machine (WV-ELM) model achieved the highest prediction accuracy and the combination of light and CO2 sensors could realize a satisfied classification result. Finally, a real-time occupancy detection system based on the sensor array and cloud platform was proposed. The system used the indoor environmental data collected by the sensor array. WV-ELM model was combined with the proposed system to detect the real-time occupied situation in the indoor environment. To verify their efficiency, the system was implemented in a laboratory to collect occupancy data for one week. According to the actual test results, the proposed system realized a detection accuracy of 97.32% with running time less than 30 s. (C) 2021 Elsevier B.V. All rights reserved.