Modelling, Validation and Quantification of Climate and Other Sensitivities of Building Energy Model on 3D City Models

Murshed, Syed Monjur; Picard, Solène; Koch, Andreas

doi:10.3390/ijgi7110447

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…According to the literature review one of the major areas of applications of the 3D city models for Smart Cities is related to modelling and simulations that have impact on the environment, such as energy efficiency of urban areas, urban building energy modelling and city-wide energy simulations [30][31][32][33][34][35], greenhouse gas inventories, CO 2 emission and low-carbon cities [36,37], urban microclimates analysis [38], solar energy resources in cities using 3D city models [39], urban energy simulation for climate protection [40], etc.…”

Section: Applicability Of 3d City Modelsmentioning

confidence: 99%

Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Jovanović¹,

Milovanov²,

Ruskovski³

et al. 2020

IJGI

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The Smart Cities data and applications need to replicate, as faithfully as possible, the state of the city and to simulate possible alternative futures. In order to do this, the modelling of the city should cover all aspects of the city that are relevant to the problems that require smart solutions. In this context, 2D and 3D spatial data play a key role, in particular 3D city models. One of the methods for collecting data that can be used for developing such 3D city models is Light Detection and Ranging (LiDAR), a technology that has provided opportunities to generate large-scale 3D city models at relatively low cost. The collected data is further processed to obtain fully developed photorealistic virtual 3D city models. The goal of this research is to develop virtual 3D city model based on airborne LiDAR surveying and to analyze its applicability toward Smart Cities applications. It this paper, we present workflow that goes from data collection by LiDAR, through extract, transform, load (ETL) transformations and data processing to developing 3D virtual city model and finally discuss its future potential usage scenarios in various fields of application such as modern ICT-based urban planning and 3D cadaster. The results are presented on the case study of campus area of the University of Novi Sad.

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Section: Applicability Of 3d City Modelsmentioning

confidence: 99%

Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Jovanović¹,

Milovanov²,

Ruskovski³

et al. 2020

IJGI

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“…Cooling mode: Q C, nd = Q gn − η ls * Q ht (1) Heating mode: The selected ISO 13790:2008 monthly energy model was implemented using an open source-based software architecture-CityBEM-in connection with data from 3D city models in the CityGML standard (LOD2) [108,109] to automate the calculation and import of GIS data. As the application to cooling energy demand has been done in few studies, a validation against TRNSYS simulation results was performed as a parallel task [108].…”

Section: Mode Formulamentioning

confidence: 99%

Between Abundance and Constraints: The Natural Resource Equation of Asia’s Diverging, Higher-Income City Models

Rode

Gomes

Adeel

et al. 2020

Land

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This paper investigates how natural resource conditions impact the physical development of cities and how, once built, the urban spatial structure leads to different patterns of resource use. The point of departure for this research is the common “resource urbanisms” assumption that cities are directly affected by the availability and costs of natural resources, and that in turn, different urbanisms result in substantial differences in resource use and consequent impact on the environment. Considering extreme and divergent, higher-income urban models of Kuwait, Abu Dhabi, Hong Kong and Singapore, the paper focusses on two resources, land and energy, and the case of building cooling and transport energy demand. The research uses a mixed methods approach which includes qualitative methods such as expert interviews, analysis of planning documents and historic planning decisions, alongside quantitative methods such as remote sensing, GIS and data analysis and energy modelling. The paper suggests that land availability is a major driver of urban form while energy prices may play a secondary role. It also finds that urban form-induced energy efficiencies for transport and cooling energy diverge in the four cities by a factor of five and two, respectively.

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“…Whereas, the SUNSHINE platform also described using CityGML for assessing and visualising energy performance [20]. Murshed et al [23] describe CityBEM, which calculates heating and cooling needs according to an ISO standard method and CityGML data. Whilst useful in their own right, Energy Atlas, SimStadt, SUNSHINE and CityBEM employ simple monthly energy balance equations to estimate the approximate annual energy demands for heating and cooling.…”

Section: The Citygml Standard and Energy Modellingmentioning

confidence: 99%

“…3D city representations are increasingly being planned for production at the national level ( [15,16]), however, in the UK, they are not yet readily available. Furthermore, quality issues can often arise when using preexisting 3D models [23,25], preventing their straightforward adoption and necessitating complex repair procedures [26]. Meanwhile, creation of detailed and accurate 3D urban geometry can require a dedicated photogrammetry or Light Detection and Ranging (LIDAR) survey and subsequent digital mapping by an expert.…”

Section: Research Gapmentioning

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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Modelling, Validation and Quantification of Climate and Other Sensitivities of Building Energy Model on 3D City Models

Cited by 12 publications

References 23 publications

Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Between Abundance and Constraints: The Natural Resource Equation of Asia’s Diverging, Higher-Income City Models

Modelling Urban Housing Stocks for Building Energy Simulation Using CityGML EnergyADE

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