Bayesian filtering for building occupancy estimation from carbon dioxide concentration

This paper proposes a new framework based on Bayesian filtering for building occupancy estimation from the observation of carbon dioxide concentration. The proposed framework can fuse a statistical model and an observation model for better occupancy estimation. The statistical model can capture the...

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Main Authors: Jiang, Chaoyang, Chen, Zhenghua, Su, Rong, Masood, Mustafa Khalid, Soh, Yeng Chai
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/159609
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1596092022-06-28T08:14:38Z Bayesian filtering for building occupancy estimation from carbon dioxide concentration Jiang, Chaoyang Chen, Zhenghua Su, Rong Masood, Mustafa Khalid Soh, Yeng Chai School of Electrical and Electronic Engineering Agency for Science, Technology and Research (A*STAR) Engineering::Electrical and electronic engineering Bayesian Filtering Inhomogeneous Markov Model This paper proposes a new framework based on Bayesian filtering for building occupancy estimation from the observation of carbon dioxide concentration. The proposed framework can fuse a statistical model and an observation model for better occupancy estimation. The statistical model can capture the temporal dependency of the building occupancy, and the first-order inhomogeneous Markov model is utilized for the estimation of occupancy transition probability. The observation model can estimate the occupancy level from carbon dioxide concentration. The likelihood is obtained from the solution of the observation model. To identify the observation model, we present a novel ensemble extreme learning machine technique. Applying the Bayes filter technique, we can fuse the transition probability and the likelihood for better occupancy estimation. The proposed framework can be applied for general cases of occupancy estimation, and the solution outperforms the results of the observation model. The results of a real experiment show the effectiveness of the proposed method. Building and Construction Authority (BCA) National Research Foundation (NRF) This work was supported by the Beijing Institute of Technology Research Fund Program for Young Scholars, and the Building and Construction Authority (BCA) of Singapore through the NRF GBIC Program with the project reference NRF2015ENC-GBICRD001-057. 2022-06-28T08:14:37Z 2022-06-28T08:14:37Z 2020 Journal Article Jiang, C., Chen, Z., Su, R., Masood, M. K. & Soh, Y. C. (2020). Bayesian filtering for building occupancy estimation from carbon dioxide concentration. Energy and Buildings, 206, 109566-. https://dx.doi.org/10.1016/j.enbuild.2019.109566 0378-7788 https://hdl.handle.net/10356/159609 10.1016/j.enbuild.2019.109566 2-s2.0-85074580878 206 109566 en NRF2015ENC-GBICRD001-057 Energy and Buildings © 2019 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Bayesian Filtering
Inhomogeneous Markov Model
spellingShingle Engineering::Electrical and electronic engineering
Bayesian Filtering
Inhomogeneous Markov Model
Jiang, Chaoyang
Chen, Zhenghua
Su, Rong
Masood, Mustafa Khalid
Soh, Yeng Chai
Bayesian filtering for building occupancy estimation from carbon dioxide concentration
description This paper proposes a new framework based on Bayesian filtering for building occupancy estimation from the observation of carbon dioxide concentration. The proposed framework can fuse a statistical model and an observation model for better occupancy estimation. The statistical model can capture the temporal dependency of the building occupancy, and the first-order inhomogeneous Markov model is utilized for the estimation of occupancy transition probability. The observation model can estimate the occupancy level from carbon dioxide concentration. The likelihood is obtained from the solution of the observation model. To identify the observation model, we present a novel ensemble extreme learning machine technique. Applying the Bayes filter technique, we can fuse the transition probability and the likelihood for better occupancy estimation. The proposed framework can be applied for general cases of occupancy estimation, and the solution outperforms the results of the observation model. The results of a real experiment show the effectiveness of the proposed method.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Jiang, Chaoyang
Chen, Zhenghua
Su, Rong
Masood, Mustafa Khalid
Soh, Yeng Chai
format Article
author Jiang, Chaoyang
Chen, Zhenghua
Su, Rong
Masood, Mustafa Khalid
Soh, Yeng Chai
author_sort Jiang, Chaoyang
title Bayesian filtering for building occupancy estimation from carbon dioxide concentration
title_short Bayesian filtering for building occupancy estimation from carbon dioxide concentration
title_full Bayesian filtering for building occupancy estimation from carbon dioxide concentration
title_fullStr Bayesian filtering for building occupancy estimation from carbon dioxide concentration
title_full_unstemmed Bayesian filtering for building occupancy estimation from carbon dioxide concentration
title_sort bayesian filtering for building occupancy estimation from carbon dioxide concentration
publishDate 2022
url https://hdl.handle.net/10356/159609
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