  Urban Building Energy Modeling (UBEM) is a critical tool to provide
quantitative 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 to
build and calibrate UBEM. However, open data are from thousands of sources
covering various perspectives of weather, building characteristics, etc.
Besides, a lack of semantic features of open data further increases the
engineering effort to process information to be directly used for UBEM as
inputs. In this paper, we first reviewed open data types used for UBEM and
developed a taxonomy to categorize open data. Based on that, we further
developed a semantic data schema for each open data category to maintain data
consistency and improve model automation for UBEM. In a case study, we use
three popular open data to show how they can be automatically processed based
on the proposed schematic data structure using large language models. The
accurate results generated by large language models indicate the
machine-readability and human-interpretability of the developed semantic data
schema.
