A Method to Generate Input Data for Urban Scale Building Energy Models

In the context of increasing energy demand and greenhouse gas emissions as a result of the rising global urbanization rates, urban scale modeling has become significantly important. Typically, the foundation for creating an Urban Building Energy Model (UBEM) is building footprints, to which other essential building information (e.g., building type) are added. However, in most cases building footprints are not available. This research presents an approach for preparing UBEM datasets by extracting building footprints and estimating building heights from Airborne Light Detection and Ranging (LiDAR) data. Raw LiDAR data are reclassified on ArcGIS before using ENVI LiDAR to extract preliminary building footprints. The resulting shapefile is then regularized and building average heights are added through a random points sampling approach. Building type and year built information are acquired from local tax assessor databases and spatially joined with the generated footprints, successfully matching 92% of the total addresses. The UBEM dataset is uploaded on CityBES to create a 3D energy model of Downtown San Antonio. The baseline model energy use breakdown shows dominance of lighting and air-conditioning systems over other energy end users, accounting for approximately 50% in all building types. The effect of using Actual Meteorological Year (AMY) weather data instead of Typical Meteorological Year (TMY3) data is analyzed, resulting in an average of 0.9% and 22% lower electricity and natural gas use intensities respectively. In addition, the impact of including neighboring building shadows on the simulation results is evaluated, revealing 2.1% less electricity demand and 22.8% higher natural gas use. Finally, the baseline model is used to investigate three Energy Conservation Measure (ECM) packages.