Increasing population and urbanisation threaten sustainable urban development due to increased resource consumption and emissions. As buildings are one of the largest energy consumers, it is crucial that their thermal load can be inspected on a large scale and at the highest resolution possible. The proposed method is performed in two stages. First, the LiDAR data and buildings’ metadata are preprocessed to generate high-resolution 3D building models that are represented by a triangle mesh. Thermal load of buildings throughout the year is then calculated per-triangle in a parallelised manner, while considering local micro-climate and shadowing from surroundings. Parallel design of the estimation enables significant speed-up of large-scale workloads, while maintaining accurate shadowing estimation. In experiments, the method was applied over a part of the city of Maribor, where heating and cooling loads were inspected in addition to other factors of thermal load estimation. Yearly thermal load calculation with an hourly time-step for 4,817 buildings with over 9.17 million triangles took about 8 min on a modern GPU. When comparing the run-times using a GPU and a modern CPU, the GPU was more than 60-times faster than a CPU for a million triangles. The speed-up grew with the number of triangles.

中文翻译：

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使用激光雷达数据大规模估算建筑物的热负荷


由于资源消耗和排放增加，人口和城市化的增长威胁着城市的可持续发展。由于建筑物是最大的能源消耗者之一，因此能够以尽可能高的分辨率大规模检查其热负荷至关重要。所提出的方法分两个阶段执行。首先，对 LiDAR 数据和建筑物元数据进行预处理，以生成由三角形网格表示的高分辨率 3D 建筑模型。然后以并行方式计算每个三角形的全年建筑物热负荷，同时考虑当地的微气候和周围环境的阴影。估计的并行设计可以显着加速大规模工作负载，同时保持准确的阴影估计。在实验中，该方法应用于马里博尔市的部分地区，除了热负荷估算的其他因素外，还检查了热负荷和冷负荷。在现代 GPU 上，以每小时时间步长计算 4,817 座建筑物（包含超过 917 万个三角形）的年度热负荷大约需要 8 分钟。比较使用 GPU 和现代 CPU 的运行时间时，对于一百万个三角形，GPU 比 CPU 快 60 倍以上。加速比随着三角形数量的增加而增加。