The rising awareness about energy conservation calls for more energy-efficient designs of heating, ventilation and air-conditioning (HVAC) systems and advanced control strategies. A rapid and accurate prediction method for indoor environment is thus of great importance. As one of the popular artificial intelligence models, a deep neural network (DNN) performs well in establishing a non-linear relationship between variables. This study aims to explore the feasibility of adopting DNN for predicting indoor airflow distribution. The detailed process of computational fluid dynamics (CFD) database establishment and construction of DNN are presented herein. To reveal the influence of DNN architecture on prediction performance, two DNNs, namely DNN A and DNN B, were constructed in consideration of different prediction strategies, and their performances on both training dataset and test dataset were compared. DNN A represents a DNN that outputs velocity values of the whole domain simultaneously, whereas DNN B outputs one velocity value of a target location for each run. Results indicate that the performance of DNN A and DNN B on the training dataset show slightly difference; however, DNN A performs much better than DNN B on test dataset. DNN A shows similar accuracies on predicting airflow distributions with different resolutions, whereas the prediction accuracy of DNN B deteriorated for airflow distributions with higher resolution. Both DNNs would output unreliable predictions for cases of which inputs are out of the range of those of training cases. The results confirm the possibility of rapid prediction of indoor airflow via a well-trained DNN which requires about 320 μs for each case, resulting in about 1.9 million times faster than CFD simulation in this study.

越来越多的节能意识要求对采暖，通风和空调（HVAC）系统进行更节能的设计，并采用先进的控制策略。因此，对于室内环境的快速准确的预测方法非常重要。作为流行的人工智能模型之一，深度神经网络（DNN）在建立变量之间的非线性关系方面表现良好。这项研究旨在探讨采用DNN预测室内气流分布的可行性。本文介绍了计算流体动力学（CFD）数据库建立和DNN构建的详细过程。为了揭示DNN架构对预测性能的影响，考虑了不同的预测策略，构造了两个DNN，即DNN A和DNN B，比较了它们在训练数据集和测试数据集上的表现。DNN A表示同时输出整个域的速度值的DNN，而DNN B每次运行都输出目标位置的一个速度值。结果表明，DNN A和DNN B在训练数据集上的表现略有差异；但是，在测试数据集上，DNN A的性能要优于DNNB。DNN A在预测具有不同分辨率的气流分布时显示出相似的准确度，而DNN B的预测精度对于分辨率较高的气流分布却有所下降。对于输入超出训练案例范围的案例，两个DNN都会输出不可靠的预测。