Synthetic Homes: An Accessible Multimodal Pipeline for Producing Residential Building Data with Generative AI

arXiv:2509.09794v3 Announce Type: replace
Abstract: Computational models have emerged as powerful tools for multi-scale energy modeling research at the building level as well as urban scale. However, these models require a plethora of data on building parameters, some of which can be inaccessible, expensive, or can raise privacy concerns. We introduce a modular multimodal framework to synthetically produce this data from publicly accessible images and residential information using generative Artificial Intelligence (AI). Additionally, we provide a modeling pipeline demonstrating this framework and we evaluate its generative AI components for realism. Our experiments show that our framework’s use of AI avoids common issues with generative models and produces realistic multimodal data at the building scale. Resulting datasets can be used for assessing influence of energy efficiency upgrades at the building scale, as well as to simulate larger patterns of energy consumption across regions. This work will support research in building and energy simulation by reducing dependence on costly or restricted data sources, and pave a path towards more accessible research in Machine Learning (ML) and other data-driven disciplines.