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The world is increasingly urbanizing and the building industry accounts formore than 40% of energy consumption in the United States. To improve urbansustainability, many cities adopt ambitious energy-saving strategies throughretrofitting existing buildings and constructing new communities. In thissituation, an accurate urban building energy model (UBEM) is the foundation tosupport the design of energy-efficient communities. However, current UBEM arelimited in their abilities to capture the inter-building interdependency due totheir dynamic and non-linear characteristics. Those models either ignored oroversimplified these building interdependencies, which can substantially affectthe accuracy of urban energy modeling. To fill the research gap, this studyproposes a novel data-driven UBEM synthesizing the solar-based buildinginterdependency and spatial-temporal graph convolutional network (ST-GCN)algorithm. Especially, we took a university campus located in downtown Atlantaas an example to predict the hourly energy consumption. Furthermore, we testedthe feasibility of the proposed model by comparing the performance of theST-GCN model with other common time-series machine learning models. The resultsindicate that the ST-GCN model overall outperforms all others. In addition, thephysical knowledge embedded in the model is well interpreted. After discussion,it is found that data-driven models integrated engineering or physicalknowledge can significantly improve the urban building energy simulation.

Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network