Estimating Occupancy Levels in Enclosed Spaces Using Environmental Variables: A Fitness Gym and Living Room as Evaluation Scenarios.

Andree Vela,Joanna Alvarado-Uribe,Hector G Ceballos,Manuel Davila,Neil Hernandez-Gress

doi:10.3390/s20226579

Andree Vela, Joanna Alvarado-Uribe + Show 3 more

Open Access

https://doi.org/10.3390/s20226579

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Abstract

The understanding of occupancy patterns has been identified as a key contributor to achieve improvements in energy efficiency in buildings since occupancy information can benefit different systems, such as HVAC (Heating, Ventilation, and Air Conditioners), lighting, security, and emergency. This has meant that in the past decade, researchers have focused on improving the precision of occupancy estimation in enclosed spaces. Although several works have been done, one of the less addressed issues, regarding occupancy research, has been the availability of data for contrasting experimental results. Therefore, the main contributions of this work are: (1) the generation of two robust datasets gathered in enclosed spaces (a fitness gym and a living room) labeled with occupancy levels, and (2) the evaluation of three Machine Learning algorithms using different temporal resolutions. The results show that the prediction of 3–4 occupancy levels using the temperature, humidity, and pressure values provides an accuracy of at least 97%.

Highlights

The understanding and modeling of occupancy patterns have been identified as key contributors to achieve improvements in energy efficiency [1,2]
The results and findings related to the evaluation scenarios for the fitness gym and living room datasets are described
In the case of the living room, the data behave in a similar way to the data collected in the fitness gym; lower average values are observed for humidity and higher average values for temperature

Summary

Objectives

This research work aims to generate an open dataset consisting of environmental variables collected from real scenarios as well as evaluating different Machine Learning algorithms on this dataset in order to estimate the occupancy level in an enclosed space. This work aims to focus on the gaps in the field of occupation mentioned above, developing a Machine Learning model based only on the use of environmental variables, which allows obtaining better results than those in the current literature

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Results

Conclusion