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.
Highlights
Analysing energy use in buildings is vital for supporting reductions in greenhouse gas emissions
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Creating urban housing stock models for energy simulation is a challenging task due to the range of information required to accurately represent the characteristics of each building in the scene, and the level of detail available in existing datasets. Ensuring that such models can be consumed by different simulator applications and that computational complexity can be managed, for larger urban scenes, are important requirements in solving this challenge
Summary
Analysing energy use in buildings is vital for supporting reductions in greenhouse gas emissions. Increasing the scale of analysis from individual buildings to urban stocks of buildings brings a number of benefits: (i) economies of scale in the specification of energy systems, (ii) energetic (e.g., radiative) interactions between buildings may be handled, so improving predictive accuracy and (iii) energy system interactions may be handled more completely (e.g., distributed generation coupled with storage in the form of electric car batteries) For these reasons, there is a growing interest in thoroughly simulating urban stocks of buildings and their energetic interactions [2,3]. Housing stock models are typically abstracted representations of an area’s (e.g., neighbourhood, city, region or country) residential buildings that can be used to predict their energy use. Housing stock models enable researchers and policymakers to predict current and future energy trends across a range of potential energy scenarios [5]
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