Modelling Urban Housing Stocks for Building Energy Simulation using CityGML EnergyADE

Rosser, Julian F.; Long, Gavin; Zakhary, Sameh; Boyd, Doreen S.; Mao, Yong; Robinson, Darren

Abstract

Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity.

Citation

Rosser, J. F., Long, G., Zakhary, S., Boyd, D. S., Mao, Y., & Robinson, D. (2019). Modelling Urban Housing Stocks for Building Energy Simulation using CityGML EnergyADE. ISPRS International Journal of Geo-Information, 8(4), Article 163. https://doi.org/10.3390/ijgi8040163

• Journal Article Type: Article
• Acceptance Date: Mar 24, 2019
• Online Publication Date: Mar 29, 2019
• Publication Date: 2019-04
• Deposit Date: Apr 1, 2019
• Publicly Available Date: Apr 1, 2019
• Journal: ISPRS International Journal of Geo-Information
• Electronic ISSN: 2220-9964
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 4
• Article Number: 163
• DOI: https://doi.org/10.3390/ijgi8040163
• Public URL: https://nottingham-repository.worktribe.com/output/1725163
• Publisher URL: https://www.mdpi.com/2220-9964/8/4/163