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Sensitivity analysis for minimization of input data for urban scale heat demand forecasting.
Abstract
In the paper a methodology based on 3D building and city models is presented to calculate urban heat demand for different refurbishment scenarios. This methodology is validated on six case study buildings of the City of Essen in Germany.
The influences of the availability and quality of data input regarding the geometrical and physical parameters on the accuracy of simulation models are analysed. Different CityGML Levels of Details (LoDs) of the building models as well as different sources of the physical parameters are tested in order to investigate the uncertainty of the methods used.
In the first step, a semi-automated method with data pre-processing (FME software) as well as simulation of the heat demand (INSEL8 software) are used. The results are compared with a fully-automated method implemented in the urban simulation platform SimStadt, whose development is ongoing in the project with the same name (www.simstadt.eu). This platform has a special module integrated, which allows an automatic data pre-processing. Both methods calculate heat demand based on monthly energy balance (standardised in Germany with the DIN V 18599, or in Europe with the ISO 13790).
The calculation of the heat demand with different accuracy of the data input enables to make a statement about which parameters have the most influence on the results. Considering the difficulties in obtaining data available at a city scale this information is very useful for future reductions of the effort of data capturing. For example, the analysis showed that the geometrical Level of Detail can give up to 10% of error depending which of the LoDs are available for the analysed building.
In the next stage, the methods tested first on the six case study buildings can be extrapolated for the whole City of Essen. This methodology could be even extended to other cities on condition that they have 3D city models available.
... In this sense, Besuievsky et al. (2014) and Peronato et al. (2016a) investigated the effect of using 3D models with coarser LODs. Similarly, but with an application on simulation of building energy demand, previous work analyzed the effect of LOD (Strzalka et al., 2015;Nouvel et al., 2017), semantic data quality (Nouvel et al., 2017) and spatial accuracy (Wate 125 et al., 2016) on the simulation results. Biljecki et al. (2015a) investigated the propagation of measurement errors in 3D models at different LODs on the calculated solar irradiation. ...
... • with reference to a ground-truth, which can be either measured (Šúri & Hofierka, 2004) or assumed to represent more accurate results (Besuievsky et al., 2014;Peronato et al., 2016a;Strzalka et al., 2015;Nouvel et al., 2017;Robinson & Stone, 2004a,b); ...
... The studies evaluating the quality of 3D models in environmental simulations are 260 typically based on data created by either procedural modeling (Besuievsky et al., 2014;Biljecki et al., 2015aBiljecki et al., , 2017 or extracted from datasets of existing cities (Strzalka et al., 2015;Nouvel et al., 2017;Peronato et al., 2016b). However, procedural modeling engines cannot easily reproduce the variety of building morphologies and configurations typical of many historical cities. ...
The increasing interest in solar energy production in urban areas requires an accurate simulation of solar irradiation on building surfaces, including vertical surfaces. However, solar potential analyses are usually conducted on 2.5D models, which are limited to roof surfaces. Methods based on 3D models, instead, allow the simulation of solar radiation on all building surfaces also accounting for inter-reflections. 3D models are thus discretized by grids of sensor points on which the solar potential is calculated.
This paper investigates the discretization error in the assessment of solar potential based on 3D models. To this end, we tested the sensitivity of simulated solar irradiation to the resolution of the sensor grid. We analyzed the impact of the grid resolution using typical discretization approaches affecting the spatial arrangement of the sensor points.
The study was conducted in a dense area of the city of Geneva represented at level of detail (LOD) 2. The simulated solar irradiation on 109 buildings was analyzed at different spatial, i.e. per surface and per building, and temporal granularities, i.e. hourly and yearly.
The results show that the error increases linearly for grids spaced at up to 4 m with maximum relative root mean square error lower than 7%. The impact of the grid resolution was found greater for structured grids than unstructured grids. The results also highlight that finer grid resolutions (i.e. smaller spacing) are necessary if the analysis is conducted at high spatial or temporal granularity, notably when analyzing roof surfaces with shading artifacts.
... Moreover, based on the characterizations in Wate and Coors (2015) and Kolbe (2009), certain building parameters do play an important role in the energetic analysis of an urban area. Studies such as Strzalka, et al. (2015) and Jaeger, et al. (2018) related to the sensitivity analysis of building geometries also highlight the importance of attributes such as building orientation, etc. for energy simulation. ...
This paper describes a CityGML Building Interpolation tool (CityBIT) for the creation of user-defined and interpolated building models for planned and/or existing buildings. A brief explanation of the tool's functionalities and the methodologies adapted to develop the tool are explained in this paper. The CityBIT aims to help urban planners and simulation scientists to facilitate CityGML model developments for energy performance simulations.
... This workflow was validated with districts in different German cities with actual consumption data (mainly gas demands for space heating) and indicated positive results (Eicker et al. (2012)). It was further tested in various scenarios with in depth investigations about the impact of heated attics (Nouvel et al. (2014)), the influence of information uncertainties like window-to-wall-ratio and usage profiles (Nouvel et al. (2013)), varying level of detail (from LoD1 to LoD3) (Strzalka et al. (2015)), deviations in the computed results comparing to a transient simulation engine (Monien et al. (2017)), and over the obstacles encountered in urban-scale energy modeling using CityGML and archetype method of data enrichment (Braun et al. (2018)). The amount of required information for district energy performance simulations highly depends on the temporal resolution and targeted application area. ...
... Therefore, the acquisition of LOD2 representations was considered to be 'nice-to-have' rather than 'must-have', although Nouvel et al. conclude that LOD2 is preferred to LOD1 in order to estimate the heat demand at building instead of district level. A similar study, conducted by Strzalka et al. [11], also concludes that the heat demand calculation -based on monthly heat balances -deviated less than 10% between LOD1 and LOD2, but only six buildings were studied, mostly apartment buildings. ...
To assess the feasibility of district energy systems as well as to design them in an optimal way, district energy simulations are often deployed, requiring an accurate spatial and temporal quantification of the district energy demand. Geographical information models and systems can provide input data to quantify the district energy demand, but the available levels of detail (LOD) of these data vary significantly between regions. Therefore, this work investigates the usability of LOD1 and LOD2 representations as well as the impact of building geometry within district energy simulations, by quantifying the differences in geometrical and energy characteristics between five variants of LOD1 or LOD2 representations. The most detailed LOD2 representation is thereby used as a reference. The results show that the significantly decreasing accuracy using LOD1 models may be compensated by assuming the roof shape from regional statistics. Also, aggregation of wall and roof components into a limited number of orientations significantly reduces simulation time, while maintaining the accuracy. It is concluded that geographical information models contain a significant amount of useful data, but the error that results from the deployed level of detail must be kept in mind when assessing the simulation results.
... at different LODs (Besuievsky, Barroso, Beckers, & Patow, 2014;Biljecki, Ledoux, & Stoter, 2017;Billger, Thuvander, & Wästberg, 2016;Brasebin, Perret, Mustière, & Weber, 2012;Deng, Cheng, & Anumba, 2016;Ellul, Adjrad, & Groves, 2016;Fai & Rafeiro, 2014;Neto, 2006;Peronato, Bonjour, Stoeckli, Rey, & Andersen, 2016;Strzalka, Monien, Koukofikis, & Eicker, 2015). In general, research has demonstrated that in certain spatial analyses the benefit of a finer LOD may be overestimated and even detrimental, as the potential small benefit may be countervailed by cost and complexity. ...
There has been a great deal of research about errors in geographic information and how they affect spatial analyses. A typical GIS process introduces various types of errors at different stages, and such errors usually propagate into errors in the result of a spatial analysis. However, most studies consider only a single error type thus preventing the understanding of the interaction and relative contributions of different types of errors. We focus on the level of detail (LOD) and positional error, and perform a multiple error propagation analysis combining both types of error. We experiment with three spatial analyses (computing gross volume, envelope area, and solar irradiation of buildings) performed with procedurally generated 3D city models to decouple and demonstrate the magnitude of the two types of error, and to show how they individually and jointly propagate to the output of the employed spatial analysis. The most notable result is that in the considered spatial analyses the positional error has a much higher impact than the LOD. As a consequence, we suggest that it is pointless to acquire geoinformation of a fine LOD if the acquisition method is not accurate, and instead we advise focusing on the accuracy of the data.
In the AEC sector, energy performance targets of buildings continuously increase for contributing to reduce carbon dioxide. This is usually done on building level, but the focus continuously shifts to larger scales such as neighborhoods, e.g. for identifying buildings with the most retrofitting potential. For this, low detailed GIS models can serve as a basis for energy simulations and are broadly available. However, neighborhood energy simulations hold many challenges, such as the lack of accurate and sufficient data to perform reliable simulations. Information such as window positions or thermal parameters of the building elements can thereby help to increase the quality of the energy simulation results. Therefore, in this paper, challenges of data collection are presented and discussed. To enable users to find a trade-off between accuracy and reliability of a neighborhood simulation and the effort to provide this data, the authors developed the concept of the Neighborhood Model States (NMS). Furthermore, occurring challenges in enriching the GIS model for each NMS are discussed on the example of buildings from the UBC campus.
La recherche doit répondre aux enjeux énergétiques globaux afin de réduire les consommations énergétiques et les émissions de gaz à effet de serre pour limiter l’impact du changement climatique. Cette recherche s’appuie notamment sur le développement de nouveaux outils de simulation urbaine, appelés UBEM (Urban Building Energy Modelling), afin d’aider les collectivités, les bureaux d’études et autres acteurs de la transition énergétique à réduire les consommations d’énergie du secteur du bâtiment. Ces UBEM sont composés de modèles devant intégrer les problématiques de manque de données de paramétrage et de coût de calcul liés à la simulation urbaine. De nombreux modèles existent avec des niveaux de détail différents, afin de simuler l’ensemble des phénomènes physiques liés au bâti, aux systèmes énergétiques ou encore aux sollicitations extérieures, en respectant ces contraintes. De par cette grande diversité de modèles à disposition de l’utilisateur, ce dernier peut se retrouver dans des situations où la sélection des modèles les plus adaptés à son étude peut s’avérer fastidieuse et complexe. Ainsi, une méthodologie permettant de réaliser une simulation dite « parcimonieuse » est proposée dans cette thèse. La parcimonie de simulation a pour objectif de trouver le bon niveau de modélisation en déterminant le point d’équilibre entre : le nombre de paramètres d’entrée et leurs incertitudes, le niveau de détail du modèle avec ses hypothèses simplificatrices, la précision obtenue vis-à-vis d’une référence et le temps de simulation, le tout pour une sortie et un contexte donnés. Pour cela, des KGI (Key Guidance Indicators), créés à partir des caractéristiques liées à l’échelle quartier, sont utilisés afin de déterminer des valeurs seuils permettant de choisir quel niveau de modélisation utiliser suivant le quartier. Cette contribution permet de pouvoir conseiller et guider divers utilisateurs et modélisateurs, dans leurs modélisations urbaines, afin de proposer une simulation non pas la plus précise, mais la plus adaptée au cas d’étude. Cette thèse propose ainsi une méthodologie pour le développement d’outils d’aide à la décision plus parcimonieux et donc plus efficaces, permettant passer de la logique du toujours plus à juste ce qu’il faut.
In the last decades, 3D city models appear to have been predominantly used for visualisation; however, today they are being increasingly employed in a number of domains and for a large range of tasks beyond visualisation. In this paper, we seek to understand and document the state of the art regarding the utilisation of 3D city models across multiple domains based on a comprehensive literature study including hundreds of research papers, technical reports and online resources. A challenge in a study such as ours is that the ways in which 3D city models are used cannot be readily listed due to fuzziness, terminological ambiguity, unclear added-value of 3D geoinformation in some instances, and absence of technical information. To address this challenge, we delineate a hierarchical terminology (spatial operations, use cases, applications), and develop a theoretical reasoning to segment and categorise the diverse uses of 3D city models. Following this framework, we provide a list of identified use cases of 3D city models (with a description of each), and their applications. Our study demonstrates that 3D city models are employed in at least 29 use cases that are a part of more than 100 applications. The classified inventory could be useful for scientists as well as stakeholders in the geospatial industry, such as companies and national mapping agencies, as it may serve as a reference document to better position their operations, design product portfolios, and to better understand the market.
In this paper, we present a methodology based on 3D city modelling to manage a realistic energy analysis of the building stock, building per building, at a very large scale (national application for instance). This methodology is tested on the City of Ludwigsburg and its more than 14.000 buildings. The influences of the data availability and quality on the model accuracy are analysed, for both geometrical and semantical information data. This paper is concluded by exposing some technological trends and policy needs to improve the accuracy and potentials of this methodology.
An urban energy management tool was developed, which is able to predict the heating energy demand of urban districts and analyze strategies for improving building standards. Building models of different Levels of Detail are investigated and analyzed according to their suitability for forecasting energy demand. Based on the specific 3D city model, an input file is generated, which can be read by the building simulation model. Special focus is put on a method for modeling the heating energy demand of the buildings with the fewest input parameters possible, but one which will give reliable forecast results. A simple transmission heat loss method and an energy-balance method were tested. In both cases, there was a good correlation between the measured and calculated annual values for a case study area of over 700 buildings in Ostfildern, Germany. The results also show that a D city model (with low geometrical detail) can be used for energy demand forecasting on an urban scale.
ISPRS Annals of the Photogrammetry
- R Kolbe
KADEN, R., KOLBE, T.H., 2013. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information
Sciences, Vol II-2/W1, SPRS 8 th 3DGeoInfo Conference & WG II/2 Workshop, Nov 2013, Istanbul, Turkey
NOUVEL, Romain, SCHULTE, Claudia, EICKER, Ursula, PIETRUSCHKA, Dirk, 2013. IBSA World 2013/CityGMLbased 3D City Model for energy diagnostics and urban energy policy support. Place: Publisher.
Website Modelling Energy Demand for Heating at City Scale
- Project Simstadt
SimStadt PROJECT, Heat demand simulation of city quarters. 2012-2014. Website (June 2014): www.simstadt.eu
STRZALKA ET AL., 2010, Modelling Energy Demand for Heating at City Scale/4 th National Conference of IBPSA-
USA, August 11-13, New York, USA
STRZALKA ET AL., 2011, 3D City modelling for urban scale heating energy demand forecasting. Journal HVAC&R
Research, Vol. 17/4, pages 526-539.
- R Kolbe
KADEN, R., KOLBE, T.H., 2013. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information
Sciences, Vol II-2/W1, SPRS 8 th 3DGeoInfo Conference & WG II/2 Workshop, Nov 2013, Istanbul, Turkey
NOUVEL, Romain, SCHULTE, Claudia, EICKER, Ursula, PIETRUSCHKA, Dirk, 2013. IBSA World 2013/CityGML-
based 3D City Model for energy diagnostics and urban energy policy support. Place: Publisher.
Methods for Dynamic Analysis of Measured Energy Use
RABL, A., 1988. Methods for Dynamic Analysis of Measured Energy Use. Journal of Solar Energy Engineering, Vol.
110, pages 52-64.
DIN V 18599-Energy efficiency of buildings-Calculations of the net, final and primary energy demand for heating, cooling and ventilation, domestic hot water and lighting-Supplement 1: Balancing of demand and consumption
- . V Din E
DIN e.V., 2010. DIN V 18599-Energy efficiency of buildings-Calculations of the net, final and primary energy
demand for heating, cooling and ventilation, domestic hot water and lighting-Supplement 1: Balancing of demand
and consumption.
Heat demand simulation of city quarters
- Project Simstadt
SimStadt PROJECT, Heat demand simulation of city quarters. 2012-2014. Website (June 2014): www.simstadt.eu
STRZALKA ET AL., 2010, Modelling Energy Demand for Heating at City Scale/4 th National Conference of IBPSAUSA, August 11-13, New York, USA