  The world is increasingly urbanizing and the building industry accounts for
more than 40% of energy consumption in the United States. To improve urban
sustainability, many cities adopt ambitious energy-saving strategies through
retrofitting existing buildings and constructing new communities. In this
situation, an accurate urban building energy model (UBEM) is the foundation to
support the design of energy-efficient communities. However, current UBEM are
limited in their abilities to capture the inter-building interdependency due to
their dynamic and non-linear characteristics. Those models either ignored or
oversimplified these building interdependencies, which can substantially affect
the accuracy of urban energy modeling. To fill the research gap, this study
proposes a novel data-driven UBEM synthesizing the solar-based building
interdependency and spatial-temporal graph convolutional network (ST-GCN)
algorithm. Especially, we took a university campus located in downtown Atlanta
as an example to predict the hourly energy consumption. Furthermore, we tested
the feasibility of the proposed model by comparing the performance of the
ST-GCN model with other common time-series machine learning models. The results
indicate that the ST-GCN model overall outperforms all others. In addition, the
physical knowledge embedded in the model is well interpreted. After discussion,
it is found that data-driven models integrated engineering or physical
knowledge can significantly improve the urban building energy simulation.
