Bayesian filtering for building occupancy estimation from carbon dioxide concentration

Jiang, Chaoyang; Chen, Zhenghua; Su, Rong; Masood, Mustafa K.; Soh, Yeng Chai

doi:10.1016/j.enbuild.2019.109566

Cited by 26 publications

(28 citation statements)

References 40 publications

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“…Of the multiple environmental variables that exist to determine the presence of human beings, temperature, humidity, and CO

are among the most frequently used [ 1 , 5 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 18 , 20 , 21 , 22 ]. A reason for this is the large variety of sensors available on the market and their accessible cost when compared to more traditional solutions, such as weather stations.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, both approaches obtained lower accuracies compared to other approaches [ 5 , 13 ]. On the other hand, among the works focused on CO

, the studies of Jiang et al [ 22 ] and Chitu et al [ 21 ] were identified, which went one step further and tackled the problem of estimating the level of occupation in ranges (high, medium, and low). They obtained an accuracy of 0.77 and 0.69, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…They used only these three variables and along with a network of 29 IoT sensors, trained an SVM to estimate the occupation, obtaining a promising result of 0.82 of accuracy. Using only these three variables is a very important matter because unlike other variables, it is common for HVAC systems in buildings to already have such information available; and in the absence of this, the necessary sensors can be installed in a simple, non-intrusive, and low-cost way [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

“…Regarding the occupancy estimation, several works have been carried out based on one or more environmental variables [ 1 , 8 , 11 , 12 , 20 , 21 , 22 ]. However, few works that did not use non-environmental variables as additional support for the estimation were found.…”

Section: Introductionmentioning

confidence: 99%

“…A second example is the work of Chitu et al [ 21 ], which in addition to using CO

, also registers the state of all the airflow sources, obtaining an accuracy of 0.69. On the other hand, among the works that only use environmental variables, Jiang et al [ 22 ] and Zhou et al [ 11 ] were found. Both used CO

to estimate occupancy, obtaining an accuracy of 0.77 and 0.82, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Vela

Alvarado-Uribe

Davila

et al. 2020

Sensors

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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%.

show abstract

“…Of the multiple environmental variables that exist to determine the presence of human beings, temperature, humidity, and CO

Section: Related Workmentioning

confidence: 99%

“…Moreover, both approaches obtained lower accuracies compared to other approaches [ 5 , 13 ]. On the other hand, among the works focused on CO

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A second example is the work of Chitu et al [ 21 ], which in addition to using CO

to estimate occupancy, obtaining an accuracy of 0.77 and 0.82, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Vela

Alvarado-Uribe

Davila

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

A framework to identify key occupancy indicators for optimizing building operation using WiFi connection count data

Alishahi

Nik-Bakht

Ouf

2021

Building and Environment

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

Kim,

Choi,

Moon

et al. 2023

Building and Environment

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Bayesian filtering for building occupancy estimation from carbon dioxide concentration

Cited by 26 publications

References 40 publications

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

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

A framework to identify key occupancy indicators for optimizing building operation using WiFi connection count data

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

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