SCHMEAR: scalable construction of holistic models for energy analysis from rooftops
SCHMEAR: scalable construction of holistic models for energy analysis from rooftops
复制标题
SCHMEAR：用于屋顶能源分析的整体模型的可扩展构建
DOI：
10.1145/3486611.3486666
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Rajagopal, Ram
中科院分区：
文献类型：
--
作者：
Dougherty, Thomas R.;Huang, Tianyuan;Chen, Yirong;Jain, Rishee K.;Rajagopal, Ram
随着世界走向脱碳，建筑环境因其能源消耗强度而受到关注。通过建筑能源建模，可以发现建筑环境脱碳的潜在途径，这有助于确定潜在的改造策略，并模拟与可再生能源的整合。然而，由于建筑材料、结构设计和城市形态之间的复合相互作用，能源建模是复杂的。为了适应这种复杂性，需要大量的领域知识、建模专业知识和大量的时间投入来进行准确的建模。在这项工作中，我们探索了通过应用现代计算机视觉算法来大规模准确建模建筑能耗的潜力。我们证明了我们的计算机视觉系统可以通过提取卫星图像中包含的有意义的特征来准确地预测能源消耗。为了实现这一目标，我们引入了数据收集管道和计算机视觉架构来处理卫星照片和城市纹理的上下文信息。我们还展示了一种方法，比较自动提取的特征在通知建筑脱碳决策和政策方面的相对重要性。我们的研究结果表明，这种方法揭示了有价值的见解，在城市规模上的建筑能源消耗的动态，并使城市能源动态的快速分析与现成的数据。
As the world moves to decarbonize, the built environment commands attention for its intensity of energy consumption. Potential pathways for decarbonizing the built environment can be discovered through the aid of building energy modeling, which helps identify potential retrofit strategies and simulate integration with renewable energy sources. Energy modeling is complicated however, due to compound interactions between building materials, structural design, and urban form. Significant domain knowledge, modeling expertise, and extensive time investment are required for accurate modeling to accommodate this complexity. In this work, we explore the potential of accurately modeling building energy consumption at scale through the application of modern computer vision algorithms. We demonstrate that our computer vision system can accurately predict energy consumption through the extraction of meaningful features contained in satellite imagery. To accomplish this, we introduce a data-collection pipeline and a computer vision architecture to process satellite photos and contextual information from the urban texture. We also demonstrate a method of comparing the relative significance of the automatically extracted features in informing building decarbonization decision making and policy. Our results indicate that this approach reveals valuable insights into the dynamics of building energy consumption on the city scale and enables the rapid analysis of urban energy dynamics with readily available data.
登录
查看更多内容
影响因子：
11.2
作者：
Alex Nutkiewicz;Zheng Yang;Rishee K. Jain
通讯作者：
Alex Nutkiewicz;Zheng Yang;Rishee K. Jain
上下文映射：单个地理地图中高维空间模式的可视化
DOI：
10.1016/j.compenvurbsys.2016.08.005
发表时间：
2017
期刊：
Comput. Environ. Urban Syst.
影响因子：
--
作者：
V. Moosavi
通讯作者：
V. Moosavi
使用街景图像进行细粒度分布网格映射
DOI：
--
发表时间：
2019
期刊：
影响因子：
--
作者：
Q. Tang;Zhecheng Wang;Arun Majumdar;R. Rajagopal
通讯作者：
R. Rajagopal
影响因子：
3.6
作者：
A. Shortland;K. Christopoulou;C. Makatsoris
通讯作者：
A. Shortland;K. Christopoulou;C. Makatsoris
影响因子：
39.8
作者：
Yu, Jiafan;Wang, Zhecheng;Rajagopal, Ram
通讯作者：
Rajagopal, Ram