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Estimating occupancy level in indoor spaces using infrared values and environmental variables
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Ovando Franco, Angelo Jean Carlo; Ceballos Cancino, Héctor Gibrán; mtyahinojosa, emimmayorquin; Dávila Delgado, Juan Manuel; Minero Re, Erik Molino; School of engineering and Sciences; Campus Monterrey; Alvarado Uribe, Joanna
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 thesis, 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. At the same way, Random Forest (RF) was the algorithm that reached the highest accuracy among Support Vector Machine (SVM), K-Nearest Neighbors (KNN).

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