New AI Framework Generates Synthetic Residential Building Data to Address Data Scarcity in Energy Research
Researchers have developed a modular, multimodal generative AI framework called 'Synthetic Homes' that produces synthetic residential building datasets from publicly available county records and images. The pipeline integrates image, tabular, and simulation-based components, and uses occlusion-based visual focus analysis to evaluate model reliability, finding their selected vision-language model outperforms a GPT-based alternative for building image processing. The work addresses a critical bottleneck in urban energy modeling by enabling scalable, privacy-respecting data generation for tasks such as retrofit analysis and city-scale energy simulation.
A team of researchers has introduced a generative AI pipeline designed to synthesize residential building datasets in contexts where real data is scarce, expensive, or restricted by privacy regulations. The framework, presented in a preprint on arXiv, combines image processing, tabular data generation, and building energy simulation components into a modular end-to-end system that draws solely on publicly available county records and building photographs. To address common reliability concerns with large language models, the authors employed occlusion-based visual focus analysis to verify that the vision-language model attends to architecturally relevant image regions, finding it superior to a GPT-based alternative for this task. Validation against a national reference dataset showed that synthetic outputs overlapped with real-world distributions by more than 95% for three of four key building variables, suggesting strong realism. The pipeline is intended to lower barriers for researchers and planners who need large building parameter datasets for machine learning-driven urban energy modeling, retrofit planning, and climate impact assessments. Code for the framework has been made publicly available by the authors. The paper has undergone five revisions since its initial submission in September 2025, with the latest version posted in June 2026.
What's missing
The paper does not appear to report performance on the fourth building variable that fell below the 95% overlap threshold, leaving its magnitude and potential impact on downstream modeling tasks unclear. Additionally, the geographic scope of validation (which county or counties were used) and how well the pipeline generalizes across diverse regional building stocks, climates, or data availability conditions are not addressed in the abstract.
What different sources said
- arXiv cs.AICenter
Synthetic Homes: A Multimodal Generative AI Pipeline for Residential Building Data Generation under Data Scarcity
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