An Energy-Related CityGML ADE and Its Application for Heating Demand Calculation

Bruse, Marcel; Nouvel, Romain; Wate, Parag; Kraut, Volker; Coors, Volker

doi:10.4018/ij3dim.2015070104

Cited by 14 publications

(11 citation statements)

References 10 publications

(15 reference statements)

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“…For example, they can help to assess the impact that environmental factors, such as noise (Stoter et al, 2008), wind (Janssen et al, 2013), and temperature (Lee et al, 2013;Hsieh et al, 2011), have on the citizens. Along with their increasing usage for spatial analyses, the topic of their quality and consistency has attracted attention, as several researchers started to recognise the importance of their validity (Agugiaro, 2016;Bruse et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The Most Common Geometric and Semantic Errors in Citygml Datasets

Biljecki

Ledoux

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:To be used as input in most simulation and modelling software, 3D city models should be geometrically and topologically valid, and semantically rich. We investigate in this paper what is the quality of currently available CityGML datasets, i.e. we validate the geometry/topology of the 3D primitives (Solid and MultiSurface), and we validate whether the semantics of the boundary surfaces of buildings is correct or not. We have analysed all the CityGML datasets we could find, both from portals of cities and on different websites, plus a few that were made available to us. We have thus validated 40M surfaces in 16M 3D primitives and 3.6M buildings found in 37 CityGML datasets originating from 9 countries, and produced by several companies with diverse software and acquisition techniques. The results indicate that CityGML datasets without errors are rare, and those that are nearly valid are mostly simple LOD1 models. We report on the most common errors we have found, and analyse them. One main observation is that many of these errors could be automatically fixed or prevented with simple modifications to the modelling software. Our principal aim is to highlight the most common errors so that these are not repeated in the future. We hope that our paper and the open-source software we have developed will help raise awareness for data quality among data providers and 3D GIS software producers.

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Section: Introductionmentioning

confidence: 99%

The Most Common Geometric and Semantic Errors in Citygml Datasets

Biljecki

Ledoux

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Fig. 4 shows the distribution of the heat demand mean values in 4 different districts of the 3D City model as used in the project "Webest" [19]. It can clearly be seen that various levels of refurbishment have been taken into account in the software, the values for the heating consumption become better the more comprehensive the refurbishments are.…”

Section: B a Citygml 3d Model Applied To The City Of Essen (De)mentioning

confidence: 99%

Building energy consumption modeling at urban scale: three case studies in Europe for residential buildings

Mutani

Todeschi

Kämpf

et al. 2018

2018 IEEE International Telecommunications Energy Conference (INTELEC)

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In urban contexts, the use of energy in buildings is one of the main causes of greenhouse gas emissions. The reduction of energy-use in buildings could be one of the main drivers to improve the sustainability, livability and quality of urban environment, together with the production of energy from the available renewable sources. To achieve energy sustainability in the most critical high-density urban contexts, it is necessary to optimize: the energy consumptions compatibility of different users; the distribution of heat, for example through the district heating network; the use of all urban spaces, such as building envelopes and urban surfaces, to produce energy from the available renewable sources. The energy models at urban scale are a complex issue as they should be simplified to be applied on a vast territory. Indeed, detailed information are not given at territorial scale and short time of simulation are preferable; however, information should be detailed enough to describe properly the energy consumption of the whole urban environment from building to urban scale. Therefore, energy models should take into account also the urban morphology, people's behavior, social and economic conditions, local and national regulation, and the use of outdoor public spaces. The challenge of this work is to present three different energy-use models, to compare their characteristics and to find the best features of an "optimum" model to analyze and represent energy resources, future scenarios, energy efficiency solutions and best energy policies. The aim is to drive a smarter use of energy, matching it with the available and more efficient energy sources to help also public administrations in defining policies adapted to the real buildings heritage. The urban energy models can also be applied in future climatic scenarios, to evaluate the impact of climate change in the energy demand/supply of buildings, as well as in the potential of retrofit scenarios.

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“…Until now, CityGML has been used in a wide range of applications (Biljecki et al, 2015;Löwner et al 2013b), e.g. energy estimation (Kaden and Kolbe, 2013;Bruse et al 2016) or fine dust distribution modelling (Ghassoun and Löwner, 2017). A main quality of CityGML is its Level of Detail concept (LoD) offering the possibility to generalize CityGML features from very detailed description (LoD4) including indoor features to a less detailed representation of a building by a planar ground surface (LoD0).…”

Section: Introductionmentioning

confidence: 99%

The Influence of Levels of Detail (Lod0-2) and Buffer Sizes on Parameter Effectiveness for Fine Dust Distribution Modelling

Löwner

Ghassoun

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Building models represented in CityGML Level of Detail 0 to 2 were used to calculate urban morphological parameters to test their effectiveness of correlation with measured total number concentration of fine dust in Berlin. Land use regression modelling as an alternative to physical based models explains the distribution of urban fine and ultrafine particles applying a multi linear regression model. Descriptive parameters are identified by high correlations with measured fine dust values. Here, different height information and geometry representations from LoD0-LoD2 were used to calculate six parameters associated with the ventilation and advection capacity of an urban environment (‘averaged heights of buildings’, ‘height-width ratio’, ‘porosity’, ‘frontal area index’, and ‘building surfaces’ for wall surfaces and for wall and roof surfaces). Parameters were correlated with measurements of the total number concentration of fine dust in the city of Berlin. Initial results show ambivalent correlations for both, different buffer sizes and implementation of the parameters with building representations in different levels of detail.</p>

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An Energy-Related CityGML ADE and Its Application for Heating Demand Calculation

Cited by 14 publications

References 10 publications

The Most Common Geometric and Semantic Errors in Citygml Datasets

The Most Common Geometric and Semantic Errors in Citygml Datasets

Building energy consumption modeling at urban scale: three case studies in Europe for residential buildings

The Influence of Levels of Detail (Lod0-2) and Buffer Sizes on Parameter Effectiveness for Fine Dust Distribution Modelling

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