A new workflow for detailed urban scale building energy modeling using spatial joining of attributes for archetype selection

Abstract

Cities play an essential role in energy consumption and its environmental impacts. Urban Building Energy Modeling (UBEM) can help optimize the built environment's energy efficiency and improve the design and operation of building energy systems. In order to develop UBEM, individual buildings' characteristics such as constructions, internal loads, energy systems, etc., are required. To develop a comprehensive urban building energy model requires detailed 3D urban building geometry information, comprehensive building attribute libraries and a detailed archetype selection to automatically assign parameters to the building construction and usage. Most of the already developed tools do not take full advantage of external data sources of building characteristics with different formats and integrate them into the UBEM workflow. This study introduces a workflow to automatically extract, collect, and pre-process the energy-related parameters from open-source data to enrich the UBEM using spatial joining of attributes and detailed archetype selection. Two different 3D urban models (low and high resolution) are proposed to be used for urban building energy simulation. The workflow using the high-resolution model is demonstrated by applying it to the downtown Montreal buildings as a Canadian case study. A novel method for assigning the building's attributes to the building surfaces and thermal zones is developed, which is based on a detailed and automatic archetype selection. The archetype-selected data and other required information for urban building energy demand calculation are fed into EnergyPlus by introducing a hierarchical concept. The high-resolution enriched UBEM is calibrated using monthly measured data of a reference building, which resulted in an acceptable root mean square error. The method was then applied to the whole district, and it could be shown that ventilation and infiltration rates have the highest impact on energy demand. This study shows that using high-resolution UBEM allows detailed urban building energy analysis, which helps decision-makers to better understand their built environment.