Analysis of Impacts of Window Opening Behavior on Indoor Air Pollutants in Residential Dorms through Deep Neural Network

Abstract

People spend more than 90% of their time in buildings. The highly stochastic behavior of occupants can alter the pollutants concentration in an indoor space. Many studies have reiterated that window opening is one of the best methods to reduce indoor pollutant concentration. In this study, we analyzed the influence of window opening behavior on indoor pollution parameters (CO2 and TVOC) in 16 student dorms in Syracuse, NY. The duration of the study encompasses all major seasons of a whole year. We found that the window opening behavior of the living room is triggered by the increased concentration of indoor pollutants. The impact of the window opening on the dilution of the concentration of the indoor pollutants is analyzed using the air exchange rates. We found that the average infiltration air exchange rate is 0.32 h-1 and the average air exchange rate during the window opening is 2.20 h-1. The exchange rates are different in different homes; infiltration ACH range from 0.31 - 0.83 h-1, and window opening ACH range from 0.46 - 3.86 h-1. The mean indoor CO2 concentration for all homes ranges between 458 - 715 ppm, and the mean TVOC concentration is 268 - 1786 ppb. The average error in the loss rate calculated from the mass-balance model and the blower door test is 2.51%. We made a Deep Neural Network model predict the concentration of CO2 in the indoor space based on the window's state. The DNN model has an RMSE of 7 ppm and a MAPE of 6.66%. The DNN predicts that the exposure during decay events at the window opening is 80.31% lower than during closed state decay.