Using artificial neural networks to predict indoor particulate matter and TVOC concentration in an office building: Model selection and method development

Indoor particulate matter and TVOC pose a significant threat to the health of staff working in office buildings. The accurate prediction of indoor particulate matter and TVOC concentrations is crucial for the automatic operation of ventilation and filtration system to control these indoor pollutants. Artificial neural networks have the advantages of high accuracy, real-time monitoring, and multi-source data integrations. Their performances in predicting particulate matter and TVOC concentrations in office buildings need to be further studied. In this study, on-site sampling was conducted in a typical office building at northern China. A standardized database of indoor parameters for four different offices was created with a total of 6072 datasets for each parameter. Three artificial neural network models were used in this study: the back propagation neural network (BP-ANN), the multi-layer neural network (MLNN), as well as the long-term and short-term memory neural network (LSTM). Among these three models, the performance of the MLNN model was the best in predicting PM2.5 and PM10 concentrations. It achieved a FB ranging from -0.02 to -0.01, an NMSE ranging from 0.46 to 0.49 μg/m3, and an R2 between 0.78 and 0.81. The MLNN model and the random forest (RF) classification method were further used to predict indoor TVOC concentrations. The RF model achieved a relatively better performance with a prediction accuracy of 89.2%. In addition, the models' generalization abilities were further evaluated by using some smaller datasets. For the MLNN model, when predicting indoor PM2.5 concentration, as the amount of training data decreased from 80% to 20%, its FB decreased from 0.41 to 0.03, its NMSE changed from 1.53 μg/m3 to 0.53 μg/m3, and its R2 decreased from 0.69 to 0.07. The results in this study can contribute to the use of artificial intelligence algorithm in office buildings aiming at indoor pollutants control.