A new method for optimizing operation of large neighborhoods of buildings using thermal simulation

Zucker, Gerhard; Judex, Florian; Blöchle, Max; Köstl, Mario; Widl, E.; Hauer, Stefan; Bres, Aurelien; Zeilinger, Jyoti

doi:10.1016/j.enbuild.2016.04.081

Cited by 23 publications

(9 citation statements)

References 11 publications

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“…Such applications are calculating the volume of buildings in the neighbourhood, e.g. for energy demand estimation and thermal simulations (Amado et al, 2016;Zucker et al, 2016), population estimation (Xie et al, 2015), and estimating the material stock (Schebek et al, 2016). In fact, Rotterdam was already a subject of similar analyses (Nouvel et al, 2015) in which aggregated data on the energy demand of neighbourhoods have been estimated.…”

Section: Potential Applications and Large-scale Experimentsmentioning

confidence: 99%

Generating 3D city models without elevation data

Biljecki

Ledoux

Stoter

2017

Computers, Environment and Urban Systems

121

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Elevation datasets (e.g. point clouds) are an essential but often unavailable ingredient for the construction of 3D city models. We investigate in this paper to what extent can 3D city models be generated solely from 2D data without elevation measurements. We show that it is possible to predict the height of buildings from 2D data (their footprints and attributes available in volunteered geoinformation and cadastre), and then extrude their footprints to obtain 3D models suitable for a multitude of applications. The predictions have been carried out with machine learning techniques (random forests) using 10 different attributes and their combinations, which mirror different scenarios of completeness of real-world data. Some of the scenarios resulted in surprisingly good performance (given the circumstances): we have achieved a mean absolute error of 0.8 m in the inferred heights, which satisfies the accuracy recommendations of CityGML for LOD1 models and the needs of several GIS analyses. We show that our method can be used in practice to generate 3D city models where there are no elevation data, and to supplement existing datasets with 3D models of newly constructed buildings to facilitate rapid update and maintenance of data.

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Section: Potential Applications and Large-scale Experimentsmentioning

confidence: 99%

Generating 3D city models without elevation data

Biljecki

Ledoux

Stoter

2017

Computers, Environment and Urban Systems

121

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“…Any roadmap to decarbonization should therefore include sound solutions for the building stock management and renovation. As nearly 80% of the energy consumed by buildings refers to its operation, proper energy management may substantially allow the increase of buildings' energy efficiency [6,7]. Single building interventions, although useful, are limited in their effects, and do not allow to maximize the benefits of sharing (costs, energy, etc.)…”

Section: Introductionmentioning

confidence: 99%

Neighborhood Energy Modeling and Monitoring: A Case Study

et al. 2021

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Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to be further exploited.

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“…These use cases range from large-scale studies to micro analyses focused at the level of buildings. For example, 3D city models stored in CityGML (but also other formats) may be used in energy planning (Agugiaro 2016), change detection (Pedrinis et al 2015), facilitating property taxation (Çagdaş 2013), calculating the sky view factor (Brasebin et al 2012), visibility studies (Wrózyński et al 2016), and thermal simulations (Zucker et al 2016).…”

Section: Applications Of 3d City Modelsmentioning

confidence: 99%

Modeling Cities and Landscapes in 3D with CityGML

Ohori

Biljecki

Kumar

et al. 2018

Building Information Modeling

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CityGML is the most important international standard used to model cities and landscapes in 3D with extensive semantics. Compared to BIM standards such as IFC, CityGML models are usually less detailed but they cover a much greater spatial extent. They are also available in any of five standardized levels of detail. CityGML serves as an exchange format and as a data source for visualizations, either in dedicated applications or in a web browser. It can also be used for a wide range of spatial analyses, such as visibility studies and solar potential. Ongoing research will improve the integration of BIM standards with CityGML, making improved data exchange possible throughout the life-cycle of urban and environmental processes. IntroductionMunicipalities and other governmental organizations are increasingly using 3D city and landscape models to maintain and plan the environment (see Fig. 11.1 for an example). These models contain 3D data about urban objects such as buildings, roads, and waterways, and the data is collected, maintained and used in applications for urban planning and environmental simulations. Examples of such applications are estimating the shadows cast by buildings and vegetation, simulations of floods and noise propagation, and predicting exposure of roof surfaces to sunlight to assess the potential of installing solar panels. An overview of applications of 3D city

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A new method for optimizing operation of large neighborhoods of buildings using thermal simulation

Cited by 23 publications

References 11 publications

Generating 3D city models without elevation data

Generating 3D city models without elevation data

Neighborhood Energy Modeling and Monitoring: A Case Study

Modeling Cities and Landscapes in 3D with CityGML

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