Development of an Indoor Particulate Matter (PM2.5) Prediction Model for Improving School Indoor Air Quality Environment

Purpose: In this study, an indoor particulate matter (PM2.5) prediction model was developed to improve air quality in the classrooms. Employing the artificial neural network, the developed model is able to conduct iterative self-training in real-time and adapt itself to the various class environments. Method: A school building, which was used for data acquisition and performance evaluation of predictive model, was modeled by coupling 3 simulation programs to consider various factors that influence the formation of indoor PM2.5 concentration. The ANN prediction model was developed using the Bayseian Regularization learning algorithm following the performance optimization. The optimized prediction model was applied to different classroom in the same building for the adaptive performance evaluation. Result: As a result of the performance evaluation, Cv(RMSE) of the optimized prediction model was 5% and R2 was 0.8757, indicating high accuracy and stability. According to the real-time training, the error gradually decreased after occurrence. Therefore, it was demonstrated that the developed ANN prediction model is able to be adapted to various environmental conditions and expected to be applied in the optimal control algorithm through future research.