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Urban Building Energy Modeling (UBEM) is a critical tool to providequantitative analysis on building decarbonization, sustainability,building-to-grid integration, and renewable energy applications on city,regional, and national scales. Researchers usually use open data as inputs tobuild and calibrate UBEM. However, open data are from thousands of sourcescovering various perspectives of weather, building characteristics, etc.Besides, a lack of semantic features of open data further increases theengineering effort to process information to be directly used for UBEM asinputs. In this paper, we first reviewed open data types used for UBEM anddeveloped a taxonomy to categorize open data. Based on that, we furtherdeveloped a semantic data schema for each open data category to maintain dataconsistency and improve model automation for UBEM. In a case study, we usethree popular open data to show how they can be automatically processed basedon the proposed schematic data structure using large language models. Theaccurate results generated by large language models indicate themachine-readability and human-interpretability of the developed semantic dataschema.

Taxonomy, Semantic Data Schema, and Schema Alignment for Open Data in Urban Building Energy Modeling