Estimating Occupancy Level in Indoor Spaces Using Infrared Values and Environmental Variables: A Collaborative Work Area as a Case Study Chapter in Scopus uri icon

abstract

  • Improving energy efficiency in indoor spaces is critical to reduce harmful effects of excessive energy consumption worldwide. For this reason, estimating occupancy level of people in indoor spaces has been identified as a significant contributor to improve energy efficiency and space utilization. In this paper, in order to contribute to the solution of this problem, it is proposed to estimate occupancy level of people in enclosed spaces through an indirect approach based on environmental and infrared data, using Machine Learning (ML) techniques. The selected environmental variables are temperature, relative humidity, and atmospheric pressure. In the process, the values of five different workstations from a collaborative work area at Tecnologico de Monterrey were collected to determine the occupancy level of each workstation. To estimate occupancy, supervised ML algorithms were used, obtaining an average accuracy for each workstation of 93%, by using both environmental and infrared data, compared to ground truth counts during occupied hours. Our results show that infrared data plus environmental variables are more accurate than infrared-only sensors for estimating indoor occupancy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

publication date

  • January 1, 2024