Predicting indoor 3D airflow distribution using artificial neural networks with two different architectures

Abstract

To meet the thermal comfort requirements of room occupants, a fast and accurate method for predicting indoor high-resolution 3D airflow distribution is necessary, which can be combined with heating, ventilation, and air conditioning (HVAC) systems to adjust the indoor environment. Artificial neural networks (ANN) can establish complex mappings between variables with nonlinear relationships. The aim of this study was to verify the feasibility of an ANN for the fast and accurate prediction of indoor 3D airflow distribution. Considering two prediction strategies, ANN A and B were constructed. The inputs of both ANNs were the air supply velocity, air supply direction, and section location. ANN A output the airflow distribution of the entire room directly, whereas ANN B output the airflow distribution of the characteristic sections first and then the airflow distribution of other sections according to the contribution of the characteristic sections. The two ANNs’ performances on training and testing datasets were compared. Both ANNs performed well in the training and interpolation cases. However, their performances decreased rapidly in the extrapolation cases. In the case of a large gap between the input and output dimensions of the ANNs, the two-step prediction strategy improved the prediction accuracy, accelerated convergence, and alleviated data imbalance problems. The prediction time of ANN A and B was 0.89 s and 12.38 s, respectively. The ANN can predict indoor high-resolution 3D airflow distribution quickly and accurately, and can add functions that include identifying internal heat sources and air supply inlet position changes. ANN combined with HVAC could allow real-time control to regulate indoor airflow.