Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics at the individual building level are often unknown and costly to acquire, or unavailable. Through this work, we propose using a generative modeling approach to generate realistic building attributes to fill in the data gaps and finally provide complete characteristics as inputs to energy models. Our model learns complex, building-level patterns from training on a large-scale residential building stock model containing 2.2 million buildings. We employ a tabular diffusion-based framework that is designed to handle heterogeneous (discrete and continuous) features in tabular building data, such as occupancy, floor area, heating, cooling, and other equipment details. We develop a capability for conditional diffusion, enabling the imputation of missing building characteristics conditioned on known attributes. We conduct a comprehensive validation of our conditional diffusion model, firstly by comparing the generated conditional distributions against the underlying data distribution, and secondly, by performing a case study for a Baltimore residential region, showing the practical utility of our approach. Our work is one of the first to demonstrate the potential of generative modeling to accelerate building energy modeling workflows.

翻译：理解社区当前的能源消费行为，对于指导未来能源使用决策及实现高效能源管理至关重要。用于模拟这些能源使用模式的城市能源模型，需要包含详细建筑特征的大规模数据集才能获得准确结果。然而，单个建筑层面的此类详细特征往往未知、获取成本高昂，或难以获得。本研究提出采用生成式建模方法生成真实的建筑属性，以填补数据空白，最终为能源模型提供完整的特征输入。我们的模型从包含220万栋建筑的住宅建筑存量模型训练中，学习了复杂的建筑层面模式。我们采用基于表格数据的扩散模型框架，该框架专为处理建筑表格数据中异质性（离散与连续）特征（如入住率、建筑面积、供暖、制冷及其他设备细节）而设计。我们开发了条件扩散能力，能够基于已知属性对缺失的建筑特征进行填补。我们对条件扩散模型进行了全面验证：首先，将生成的条件分布与底层数据分布进行对比；其次，以巴尔的摩住宅区为例进行案例研究，展示了本方法的实际应用价值。本工作率先证明了生成式建模在加速建筑能源建模流程中的潜力。