%0 Conference Paper
%K Machine learning
%K SRG (Simulation Research Group)
%K Natural language processing
%A Wanni Zhang
%A Tianzhen Hong
%A Xuan Luo
%B 2020 Building Performance Analysis Conference and SimBuild co-organized by ASHRAE and IBPSA-USA
%C Chicago, IL
%D 2020
%G eng
%R 10.20357/B7BW2S
%T Extract useful information from building permits data to profile a city’s building retrofit history
%8 08/2020
%X <p>Building retrofit is one of the key strategies for cities to reduce energy use and GHG emissions. The historical information about changes to buildings is crucial to infer the buildings’ current energy system efficiency levels and to identify candidate buildings for retrofit. In general, a building permit is required before the start of any construction activity of a building, such as changing building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Therefore, the permits are a potentially good resource for mining information on the city’s retrofit history. In this study, we use the permit dataset from the city of San Francisco as a case study. Location and time information from the dataset is also used to depict the retrofit timeline of each building and the whole building stock. The type of work of the permit is inferred from the descriptive text by a machine learning model. At last, the limitations of the current permit dataset and potential improvements on the permit data management are discussed to better utilize the information in the future.&nbsp;</p>