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Modelling Urban Housing Stocks for Building Energy Simulation Using CityGML EnergyADE

1
School of Geography, University of Nottingham, Nottingham, NG7 2RD, UK
2
School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK
3
School of Physics & Astronomy, University of Nottingham, Nottingham, NG8 1BB, UK
4
Sheffield School of Architecture, University of Sheffield, Sheffield, S10 2TN, UK
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(4), 163; https://doi.org/10.3390/ijgi8040163
Received: 16 January 2019 / Revised: 19 March 2019 / Accepted: 24 March 2019 / Published: 29 March 2019
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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. View Full-Text
Keywords: city modelling; energy simulation; CityGML; EnergyADE; generalisation city modelling; energy simulation; CityGML; EnergyADE; generalisation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rosser, J.F.; Long, G.; Zakhary, S.; Boyd, D.S.; Mao, Y.; Robinson, D. Modelling Urban Housing Stocks for Building Energy Simulation Using CityGML EnergyADE. ISPRS Int. J. Geo-Inf. 2019, 8, 163.

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