Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong SAR, China.
School of Civil Engineering, Tsinghua University, Beijing, China.
Sci Data. 2023 Jun 2;10(1):352. doi: 10.1038/s41597-023-02261-5.
City-scale building energy simulation provides a significant reference for planning and urban management. However, large-scale building energy simulation is often unfeasible due to the huge amount of computational resources required and the lack of high-precision building models. For such reasons, this study developed a tiled multi-city urban objects dataset and a distributed data ontology. Such a data metric not only transforms the conventional whole-city simulation model into patch-based distributed simulations but also incorporates interactive relationships among objects in cities. The dataset stores urban objects (8,196,003 buildings; 238,736 vegetations; 2,381,6698 streets; 430,364 UrbanTiles; 430,464 UrbanPatches) from thirty major cities in the United States. It also aggregated morphological features for each UrbanTile. To validate the performance of the developed dataset, a sample test was conducted in one city subset (Portland). The results conclude that the linear increase of time usage of modeling and simulation with the increase of building numbers. With the tiled data structure, the proposed dataset is also efficient for the building microclimate estimation.
城市尺度的建筑能源模拟为规划和城市管理提供了重要参考。然而，由于需要大量的计算资源和缺乏高精度的建筑模型，大规模的建筑能源模拟往往是不可行的。出于这些原因，本研究开发了一个平铺多城市城市对象数据集和分布式数据本体。这种数据度量不仅将传统的全市模拟模型转换为基于补丁的分布式模拟，而且还纳入了城市中对象之间的交互关系。该数据集存储了来自美国 30 个主要城市的城市对象（8196003 栋建筑；238736 个植被；23816698 条街道；430364 个 UrbanTile；430464 个 UrbanPatch），并对每个 UrbanTile 进行了形态特征聚合。为了验证所开发数据集的性能，在一个城市子集（波特兰）中进行了样本测试。结果表明，建模和模拟的时间使用随着建筑物数量的增加呈线性增加。通过平铺数据结构，所提出的数据集对于建筑微气候估计也很有效。
