Virtual dynamic coupling of computational fluid dynamics-building energy simulation-artificial intelligence: Case study of urban neighbourhood effect on buildings’ energy demand

Abstract

Coupling of building energy simulation (BES) tools with computational fluid dynamics (CFD) technique offers the ability to include the commonly neglected, but significantly important, neighbourhood effect on the local airflow patterns and thus buildings’ energy demand. Amongst various coupling approaches, the fully dynamic coupling is considered as the most accurate technique although not a practical one for medium-to-long-term simulations due to the associated high computational cost.This study, therefore, aims to propose a novel framework of virtual dynamic BES-CFD-artificial intelligence (AI) coupling to prevent intensive computational calculations. The prediction is performed by artificial neural network (ANN), which is trained over a series of fully dynamic BES-CFD coupling results to replace the local flow characteristics, in particular, convective heat transfer coefficient (CHTC). Furthermore, a case study of a city block performed in a typical hot month (September) in Los Angeles is undertaken to assess the proposed framework.The predictions of the local CHTCs on the external surfaces are found satisfactory with an accuracy of 0.88. Moreover, 10 is found as the effective size of days to train the neural network tools for a one-month simulation. The proposed approach results in saving approximately 2/3 of the required computational time using an ordinary approach.