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Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks
Ventilation plays a noteworthy role in maintaining a healthy, comfortable and energy-efficient indoor environment and mitigating the risk of aerosol transmission and disease infection (e.g., SARS-COV-2). In most commercial and office buildings, demand-controlled ventilation (DCV) systems are widely...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553470/ https://www.ncbi.nlm.nih.gov/pubmed/36247734 http://dx.doi.org/10.1016/j.buildenv.2022.109207 |
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author | Zhuang, Chaoqun Choudhary, Ruchi Mavrogianni, Anna |
author_facet | Zhuang, Chaoqun Choudhary, Ruchi Mavrogianni, Anna |
author_sort | Zhuang, Chaoqun |
collection | PubMed |
description | Ventilation plays a noteworthy role in maintaining a healthy, comfortable and energy-efficient indoor environment and mitigating the risk of aerosol transmission and disease infection (e.g., SARS-COV-2). In most commercial and office buildings, demand-controlled ventilation (DCV) systems are widely utilized to conserve energy based on occupancy. However, as the presence of occupants is often inherently stochastic, accurate occupancy prediction is challenging. This study, therefore, proposes an autoencoder Bayesian Long Short-term Memory neural network (LSTM) model for probabilistic occupancy prediction, taking account of model misspecification, epistemic uncertainty, and aleatoric uncertainty. Performances of the proposed models are evaluated using real data in an educational building at the University of Cambridge, UK. The models trained on data of one open-plan space are used to predict occupant numbers for other spaces (with similar layout and function) in the same building. The probabilistic occupant profiles are then used for estimating optimal ventilation rates for two scenarios (i.e., normal DCV mode for energy conservation and anti-infection mode for virus transmission prevention). Results show that, during the test period, for the 1-h ahead prediction, the proposed model achieved better performance with up to 5.8% mean absolute percentage error reduction than the traditional LSTM model. More flexible alternatives for ventilation can be offered by the proposed risk-aware decision-making schemes serving different purposes under real operation. The findings from this study provide new occupancy forecasting solutions and explore the potential of probabilistic decision making for building ventilation optimization. |
format | Online Article Text |
id | pubmed-9553470 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Authors. Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95534702022-10-12 Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks Zhuang, Chaoqun Choudhary, Ruchi Mavrogianni, Anna Build Environ Article Ventilation plays a noteworthy role in maintaining a healthy, comfortable and energy-efficient indoor environment and mitigating the risk of aerosol transmission and disease infection (e.g., SARS-COV-2). In most commercial and office buildings, demand-controlled ventilation (DCV) systems are widely utilized to conserve energy based on occupancy. However, as the presence of occupants is often inherently stochastic, accurate occupancy prediction is challenging. This study, therefore, proposes an autoencoder Bayesian Long Short-term Memory neural network (LSTM) model for probabilistic occupancy prediction, taking account of model misspecification, epistemic uncertainty, and aleatoric uncertainty. Performances of the proposed models are evaluated using real data in an educational building at the University of Cambridge, UK. The models trained on data of one open-plan space are used to predict occupant numbers for other spaces (with similar layout and function) in the same building. The probabilistic occupant profiles are then used for estimating optimal ventilation rates for two scenarios (i.e., normal DCV mode for energy conservation and anti-infection mode for virus transmission prevention). Results show that, during the test period, for the 1-h ahead prediction, the proposed model achieved better performance with up to 5.8% mean absolute percentage error reduction than the traditional LSTM model. More flexible alternatives for ventilation can be offered by the proposed risk-aware decision-making schemes serving different purposes under real operation. The findings from this study provide new occupancy forecasting solutions and explore the potential of probabilistic decision making for building ventilation optimization. The Authors. Published by Elsevier Ltd. 2022-07-01 2022-05-18 /pmc/articles/PMC9553470/ /pubmed/36247734 http://dx.doi.org/10.1016/j.buildenv.2022.109207 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Zhuang, Chaoqun Choudhary, Ruchi Mavrogianni, Anna Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks |
title | Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks |
title_full | Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks |
title_fullStr | Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks |
title_full_unstemmed | Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks |
title_short | Probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder Bayesian deep neural networks |
title_sort | probabilistic occupancy forecasting for risk-aware optimal ventilation through autoencoder bayesian deep neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553470/ https://www.ncbi.nlm.nih.gov/pubmed/36247734 http://dx.doi.org/10.1016/j.buildenv.2022.109207 |
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