A Modular and Multimodal Generative AI Framework for Urban Building Energy Data: Generating Synthetic Homes
This addresses data scarcity and privacy issues for researchers in urban energy modeling, though it appears incremental as it builds on existing generative AI methods.
The paper tackles the problem of inaccessible, expensive, or private data for urban building energy modeling by introducing a modular multimodal generative AI framework that produces realistic, labeled synthetic residential data from publicly accessible information and images.
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.