Urban micro-scale street thermal comfort prediction using a ‘graph attention network’ model

Abstract

<p>Outdoor thermal comfort (OTC) directly affects human behavior and building operations. It is also a key factor in the achievement of smart living. When modeling OTC, existing studies tend to reduce the computational burden by conceptualizing urban spatial elements (e.g., buildings, streets) as static entities without considering the intricate dynamics of their synergistic influence. This research presents an innovative network-based framework for urban micro-scale street OTC prediction. The proposed graph attention network (GAT), in conjunction with building energy modeling (BEM), treating building clusters as graph nodes and streets as edges, capturing the interrelations between urban spatial elements, and modeling the street's universal thermal climate index (UTCI) at different time periods. The GAT model is trained and tested using simulated data from representative high-density residential areas in Hong Kong. Its performance is evaluated against an artificial neural network (ANN) model that disregards interrelations among urban spatial elements. Results demonstrate that, compared to the ANN model, the GAT model achieves an improvement in overall mean absolute error (MAE) of 37.5 %, root mean square error (RMSE) of 36.07 %, and correlation coefficient (r) of 10.58 %. Furthermore, the GAT model can better predict situations with significant amplitude changes and rapid frequency variations.</p>