Imputing missing indoor air quality data via variational convolutional autoencoders: Implications for ventilation management of subway metro systems

Abstract

Missing data represents a common problem in environmental and building-related processes, especially in the indoor air quality (IAQ) system of subway stations, where the collected information leads to actions in ventilation management. For these reasons, imputation approaches have been used to avoid information loss due to downsampling or sensor malfunction. This paper introduces an imputation approach for IAQ data via variational autoencoders (VAE) coupled with convolutional layers (VAE-CNN). Two scenarios were introduced: first, the IAQ dataset was corrupted by removing data intervals at different missing rates (i.e., 20%, 50%, and 80%), and second, a point-to-point removal of three sensors was conducted. The performance of the proposed method was compared with different techniques, showing that the VAE-CNN was superior to other methods even for massive amounts of missing data. Finally, the effects of missing and imputed data on the IAQ system in the D-subway station were evaluated in terms of ventilation energy demand, CO2 emissions, and IAQ level. The IAQ management with the imputed data could decrease by approximately 20% of CO2 emissions by reducing the energy demand, while the IAQ level increased by 3% in another scenario.