Prediction of Indoor Air Quality using Long Short-Term Memory with Adaptive Gated Recurrent Unit

Abstract

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.

Keywords

• indoor air quality
• prediction
• machine learning
• long short-term memory
• hybrid