Characterizing and structuring urban GIS data for housing stock energy modelling and retrofitting

Abstract

Addressing the gap between available urban building data, energy performance evaluation, and spatial distribution of end-users is a growing concern of municipalities to support energy planning and urban retrofitting towards low carbon emission. These days, many existing urban building energy models (UBEM) use the non-geo-specified 3D building stock geometries or the digital twins of cities as their primary input for energy simulation. The digital twins of cities or the built environment is an accurate scale virtual representation (geo-specified geometry and information) of city objects enabled to be paired with dynamic predictive models of their energy performance. The use of built environment digital twins for their energy assessment works well for those cities whose digital twin datasets are readily available for public or private use. In the cities without digital twin datasets, their energy assessment is limited by tools that use 2D building stock footprints as their primary input or apply a top-down urban energy simulation approach. This gap motivated the current research to identify an extended UBEM data preparation workflow focused on generating city digital twins using accessible GIS datasets as well as national and local data sources. The workflow is majorly developed to deal with GIS data processing utilizing multilevel spatial data integration and refinement to fill in the recognized inconsistencies of building databases. Then, procedural 3D city modelling and an automated transformation to a semantic, CityGML format are successfully provided to generate digital twin of the study area in an open data model. The archetype modelling method is also employed to create a set of customized housing energy profiles to support tailored bottom-up UBEM and retrofitting scenarios relying on the simulation platform Insel4Cities, under development at Concordia University. The workflow was practically carried out on a case study, Kelowna city in British Columbia (BC)/Canada, to validate the district heating load. The average simulated heating energy use intensity showed a deviation of less than 2.5% from the BC measured data. As an example of workflow usability, the developed model was also utilized to assess a retrofit scenario, combining a decentralized heat pump and photovoltaic system, to present the district's potential for net-zero action planning.