Dynamic forecasting model for indoor pollutant concentration using recurrent neural network

Abstract

Accurate and reliable indoor pollutant concentration prediction is essential to solve the time-lag problem of indoor air quality control systems. Thus, the representation of time in pollutant forecasting models is very important. One approach is to introduce an Elman neural network using a direct inference strategy into the time series forecast of indoor pollutant concentration. In this study, measurements of CO₂ (ppm), total volatile organic compounds (mg/m³), particulate matter with a diameter smaller than 2.5 µm (PM_2.5; µg/m³), the indoor dry bulb temperature (°C) and relative humidity (%) were carried out in a classroom at a middle school in Beijing, China. To identify air pollution antecedents, input selection was conducted based on correlation analysis. The results show that the information provided by the PM_2.5 time series can better simulate the dynamic relationship between input and output data ( $CC\mathrm{ }$ = 0.963 and R² = 0.928). In addition to the overall goodness of fit ( $R^2$  = 0.982) of the CO₂ time series, the peak and valley prediction capability of the model was evaluated using the relative peak error (RPE) metric. Information from the valleys of the CO₂ time series gives good results ( $\mathit{RPE}<0.1$ ). Therefore, a dynamic forecasting model with a direct inference strategy is a capable tool for identifying proper air pollution antecedents.