3D Data Models for Urban Energy Simulation

Wate, Parag; Coors, Volker

doi:10.1016/j.egypro.2015.11.753

Cited by 30 publications

(14 citation statements)

References 7 publications

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“…Data volume & data composition complexity: The majority of the 3D models need large amounts of 3D data support from various sources for analytical capability and visualisation effects, such as vector data, raster data, socio-economic data, census data, and other domain-specific data (Wate and Coors, 2015). In general, the input data can be categorised into semantic, geometric, and topological information (Li et al, 2017).…”

Section: Data Requirementsmentioning

confidence: 99%

Urban 3d Modelling Methods: A State-of-the-Art Review

Ying

Koeva

Kuffer

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. As urbanisation accelerates, the urban landscape reshapes at a fast pace. Consequently, the urban built environment continuously evolves horizontally as well as vertically. However, more attention in the field of spatial analysis is given to horizontal dynamics, despite the importance of geoinformation in the vertical dimension. 3D modelling methods have gained popularity due to their powerful capability of capturing and analysing geoinformation in the vertical dimension and visualising objects lifelike in the urban built environment. Various urban applications with diverse 3D modelling methods at different research scales and purposes have emerged. However, there is no systematic overview of these different modelling methods. Therefore, it is imperative to provide an up-to-date review of these advances. In this paper, we aim to review urban 3D modelling methods widely used in the prior 5-year period (2015–2020). Our analysis focuses on five attributes, i.e., basic characteristics, data requirements, technical requirements, users requirements and ethical considerations. The discussion presents the current status of 3D modelling methods – a wide range of applications yet with substantial development potential. This paper closes with insights for future work regarding the necessities of 3D data structure support as well as interdisciplinary research, specifically for big data management and integration.

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Section: Data Requirementsmentioning

confidence: 99%

Urban 3d Modelling Methods: A State-of-the-Art Review

Ying

Koeva

Kuffer

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…CityGML is an open semantic data model designed for representing 3D urban information across a wide range of uses and enabling interoperability between systems that support it. The relevance of 3D modelling to building energy analyses has led to CityGML playing an important role in supporting these objectives [7,13,[18][19][20][21]. For example, solar irradiance may be estimated using surface geometry extracted from CityGML [19], but additional modelling is required for heating demand estimation.…”

Section: The Citygml Standard and Energy Modellingmentioning

confidence: 99%

Modelling Urban Housing Stocks for Building Energy Simulation using CityGML EnergyADE

Rosser

Long

Zakhary

et al. 2019

IJGI

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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.

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“…Fonseca and Schlueter (2015) describe a similar GIS based model (although visualized in three-dimensions (3D)) for the analysis of building energy consumption patterns and retrofit, with a focus on energy systems, in neighborhoods and city districts utilized in Switzerland. Also, Nouvel et al (2015) and Wate and Coors (2015) demonstrate a model called SimStadt in Germany which presents 3D visualizations of energy demand, CO 2e emissions, savings, refurbishment scenarios and solar energy potential. The model data sources such as building registers and censuses, meteorological data, gross volume, surface type (roof, wall, and ground) and sun-wind exposed surface area mathematically derived from the building geometry encoded in a CityGML model and building physics attributes are the default benchmark data for given building archetypes.…”

Section: Urban Modelling For Large-scale Energy Retrofitmentioning

confidence: 99%