A Modular and Multimodal Generative AI Framework for Urban Building Energy Data: Generating Synthetic Homes

• URL: http://arxiv.org/abs/2509.09794v1
• Date: Thu, 11 Sep 2025 18:53:21 GMT
• Title: A Modular and Multimodal Generative AI Framework for Urban Building Energy Data: Generating Synthetic Homes
• Authors: Jackson Eshbaugh, Chetan Tiwari, Jorge Silveyra,
• Abstract summary: We introduce a modular multimodal framework to produce data from publicly accessible information and images using generative artificial intelligence (AI)<n>By reducing dependence on costly or restricted data sources, we pave a path towards more accessible and reproducible research.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Computational models have emerged as powerful tools for energy modeling research, touting scalability and quantitative results. However, these models require a plethora of data, some of which is inaccessible, expensive, or raises privacy concerns. We introduce a modular multimodal framework to produce this data from publicly accessible residential information and images using generative artificial intelligence (AI). Additionally, we provide a pipeline demonstrating this framework, and we evaluate its generative AI components. Our experiments show that our framework's use of AI avoids common issues with generative models. Our framework produces realistic, labeled data. By reducing dependence on costly or restricted data sources, we pave a path towards more accessible and reproducible research.

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