TY - JOUR
T1 - Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent Unit
AU - Rahim, Muhamad Sharifuddin Abd
AU - Yakub, Fitri
AU - Omar, Mas
AU - Ghani, Rasli Abd
AU - Salim, Sheikh Ahmad Zaki Shaikh
AU - Masuda, Shiro
AU - Dhamanti, Inge
N1 - Publisher Copyright:
© The Authors, published by EDP Sciences, 2023.
PY - 2023/6/16
Y1 - 2023/6/16
N2 - 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.
AB - 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.
KW - Hybrid
KW - Indoor air quality
KW - Long Short-Term memory
KW - Machine learning
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85164452936&partnerID=8YFLogxK
U2 - 10.1051/e3sconf/202339601095
DO - 10.1051/e3sconf/202339601095
M3 - Conference article
AN - SCOPUS:85164452936
SN - 2555-0403
VL - 396
JO - E3S Web of Conferences
JF - E3S Web of Conferences
M1 - 01095
T2 - 11th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IAQVE C2023
Y2 - 20 May 2023 through 23 May 2023
ER -