Conditional Distribution Estimation of Building Characteristics with Diffusion Models for Urban Energy Modeling

TL;DR

This paper introduces a diffusion-based generative model to accurately impute missing building characteristics, enhancing urban energy modeling by providing complete data for large-scale residential building datasets.

Contribution

The work presents a novel conditional diffusion framework for generating realistic, heterogeneous building attributes conditioned on known features, validated on a large-scale dataset and a real-world case study.

Findings

The model accurately reproduces the distribution of building features.

Conditional imputation improves data completeness for energy models.

Case study demonstrates practical utility in Baltimore.

Abstract

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.