Compiling Open Datasets to Improve Urban Building Energy Models with Occupancy and Layout Data

Abstract

Urban building energy modelling (UBEM) has great potential for assessing the energy performance of the existing building stock and exploring various actions targeting energy efficiency. However, the precision and completeness of UBEM models can be challenged due to the lack of available and reliable datasets related to building occupant and layout information. This study presents an approach that aims to augment UBEM with open-data sources. Data collected from open data sources are integrated into UBEM in three steps. Step (1) involves the generation of occupant profiles from census data collected from governmental institutions. Step (2) relates to the automated generation of building plan layouts by extracting data on building area and number of rooms from an online real-estate website. Results of Steps (1) and (2) are incorporated into Step (3) to generate residential units with layouts and corresponding occupant profiles. Finally, we make a comparative analysis between data-augmented and standard UBEM based on building energy use and occupant thermal comfort. The initial results point to the importance of detailed, precise energy models for reliable performance analysis of buildings at the urban scale. 0864108000