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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

Authors

Julian F. Rosser

DOREEN BOYD doreen.boyd@nottingham.ac.uk
Professor of Earth Observation

YONG MAO yong.mao@nottingham.ac.uk
Associate Professor

Darren Robinson



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.

Journal Article Type Article
Publication Date 2019-04
Journal ISPRS International Journal of Geo-Information
Electronic ISSN 2220-9964
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 8
Issue 4
Article Number 163
APA6 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), https://doi.org/10.3390/ijgi8040163
DOI https://doi.org/10.3390/ijgi8040163
Publisher URL https://www.mdpi.com/2220-9964/8/4/163

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