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Prediction of indoor air quality using long short-term memory with adaptive gated recurrent unit.

There is significant evidence that the COVID-19 virus may be spread by inhaling aerosols leading to risk of infections across indoor environments. Having said that, it is clear that the formulation of indoor air quality (IAQ) needs to be carefully examined. In general, IAQ can be controlled by prope...

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Main Authors: Abd. Rahim, Muhamad Sharifuddin, Yakub, Fitri, Omar, Mas, Abd. Ghani, Rasli, Shaikh Salim, Sheikh Ahmad Zaki, Masuda, Shiro, Dhamanti, Inge
Format: Conference or Workshop Item
Language:English
Published: 2023
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://eprints.utm.my/107794/1/MuhammadSharifuddinAbdRahim2023_PredictionofIndoorAirQualityUsingLongTermMemory.pdf
http://eprints.utm.my/107794/
http://dx.doi.org/10.1051/e3sconf/202339601095
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spelling my.utm.1077942024-10-02T07:36:07Z http://eprints.utm.my/107794/ Prediction of indoor air quality using long short-term memory with adaptive gated recurrent unit. Abd. Rahim, Muhamad Sharifuddin Yakub, Fitri Omar, Mas Abd. Ghani, Rasli Shaikh Salim, Sheikh Ahmad Zaki Masuda, Shiro Dhamanti, Inge TJ Mechanical engineering and machinery There is significant evidence that the COVID-19 virus may be spread by inhaling aerosols leading to risk of infections across indoor environments. Having said that, it is clear that the formulation of indoor air quality (IAQ) needs to be carefully examined. In general, IAQ can be controlled by proper ventilation system across buildings. Nevertheless, different buildings require different mechanistic approaches and it may not be an effective solution for the buildings. Thus, statistical approaches have great potential to evaluate the IAQ in real occupied buildings. Numerous machine learning (ML) techniques were introduced to forecast the indoor environmental risk across buildings. However, there is inadequate data available on how well these ML techniques perform in indoor environments. Recurrent neural network (RNN) is a ML technique that deals with sequential data or time series data. However, the RNN model gradient tends to explode and vanish, leading to inaccurate prediction outcomes. Therefore, this study presents the development of a time based prediction model, Long Short-Term Memory (LSTM) with adaptive gated recurrent units for the prediction of IAQ. Using an advanced LSTM model, the study focuses on the performance of the prediction accuracy and the loss during training and validation. Also, the developed model will be assessed with other RNN models for data validation and comparisons. A set of particulate matter (PM2.5) dataset from commercial buildings is assessed, preprocessed and clean to ensure quality prediction outcomes. This study demonstrates the performance of the hybrid LSTM model to remember past information, minimize gradient error and predict the future data precisely, ensuring a healthier indoor building environment. 2023-06-16 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/107794/1/MuhammadSharifuddinAbdRahim2023_PredictionofIndoorAirQualityUsingLongTermMemory.pdf Abd. Rahim, Muhamad Sharifuddin and Yakub, Fitri and Omar, Mas and Abd. Ghani, Rasli and Shaikh Salim, Sheikh Ahmad Zaki and Masuda, Shiro and Dhamanti, Inge (2023) Prediction of indoor air quality using long short-term memory with adaptive gated recurrent unit. In: 11th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IAQVE C2023, 20 May 2023 - 23 May 2023, Tokyo, Japan. http://dx.doi.org/10.1051/e3sconf/202339601095
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Abd. Rahim, Muhamad Sharifuddin
Yakub, Fitri
Omar, Mas
Abd. Ghani, Rasli
Shaikh Salim, Sheikh Ahmad Zaki
Masuda, Shiro
Dhamanti, Inge
Prediction of indoor air quality using long short-term memory with adaptive gated recurrent unit.
description There is significant evidence that the COVID-19 virus may be spread by inhaling aerosols leading to risk of infections across indoor environments. Having said that, it is clear that the formulation of indoor air quality (IAQ) needs to be carefully examined. In general, IAQ can be controlled by proper ventilation system across buildings. Nevertheless, different buildings require different mechanistic approaches and it may not be an effective solution for the buildings. Thus, statistical approaches have great potential to evaluate the IAQ in real occupied buildings. Numerous machine learning (ML) techniques were introduced to forecast the indoor environmental risk across buildings. However, there is inadequate data available on how well these ML techniques perform in indoor environments. Recurrent neural network (RNN) is a ML technique that deals with sequential data or time series data. However, the RNN model gradient tends to explode and vanish, leading to inaccurate prediction outcomes. Therefore, this study presents the development of a time based prediction model, Long Short-Term Memory (LSTM) with adaptive gated recurrent units for the prediction of IAQ. Using an advanced LSTM model, the study focuses on the performance of the prediction accuracy and the loss during training and validation. Also, the developed model will be assessed with other RNN models for data validation and comparisons. A set of particulate matter (PM2.5) dataset from commercial buildings is assessed, preprocessed and clean to ensure quality prediction outcomes. This study demonstrates the performance of the hybrid LSTM model to remember past information, minimize gradient error and predict the future data precisely, ensuring a healthier indoor building environment.
format Conference or Workshop Item
author Abd. Rahim, Muhamad Sharifuddin
Yakub, Fitri
Omar, Mas
Abd. Ghani, Rasli
Shaikh Salim, Sheikh Ahmad Zaki
Masuda, Shiro
Dhamanti, Inge
author_facet Abd. Rahim, Muhamad Sharifuddin
Yakub, Fitri
Omar, Mas
Abd. Ghani, Rasli
Shaikh Salim, Sheikh Ahmad Zaki
Masuda, Shiro
Dhamanti, Inge
author_sort Abd. Rahim, Muhamad Sharifuddin
title Prediction of indoor air quality using long short-term memory with adaptive gated recurrent unit.
title_short Prediction of indoor air quality using long short-term memory with adaptive gated recurrent unit.
title_full Prediction of indoor air quality using long short-term memory with adaptive gated recurrent unit.
title_fullStr Prediction of indoor air quality using long short-term memory with adaptive gated recurrent unit.
title_full_unstemmed Prediction of indoor air quality using long short-term memory with adaptive gated recurrent unit.
title_sort prediction of indoor air quality using long short-term memory with adaptive gated recurrent unit.
publishDate 2023
url http://eprints.utm.my/107794/1/MuhammadSharifuddinAbdRahim2023_PredictionofIndoorAirQualityUsingLongTermMemory.pdf
http://eprints.utm.my/107794/
http://dx.doi.org/10.1051/e3sconf/202339601095
_version_ 1814043524329373696
score 13.211869

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