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Comparison of building modelling assumptions and methods for urban scale heat demand forecasting

Comparison of building modelling assumptions and methods for urban scale heat demand forecasting Building energy evaluation tools available today are only able to effectively analyse individual buildings and usually either they require a high amount of input data or they are too imprecise in energy predictions at a city (district) scale because of too many assumptions made. In this paper, two tools based on 3D models are compared to see whether there is an approach that would probably be able to fit both – the amount of data available and the number of assumptions made. A case study in the German town of Essen was chosen in the framework of the research project WeBest, where six building types representing the most important building periods were analysed. The urban simulation tool SimStadt, an in-house development of HFT Stuttgart, based on 3D urban geometry, is used to calculate the heat demand for both single building scale and city district scale. The individual building typology results are compared with the commercial dynamic building simulation software TRNSYS. The influence of the availability and quality of data input regarding the geometrical building parameters on the accuracy of simulation models are analysed. Different Levels of Details (LoDs) of the 3D building models are tested to prove the scalability of SimStadt from single buildings to city districts without loss of quality and accuracy in larger areas with a short computational time. Keywords: Heat demand simulation, 3D city model, CityGML, Scalability of urban models Introduction of heat demand for different scales from single buildings The building sector has a large potential in the EU- to an urban (district) level. economy for energy efficiency gains and CO -reductions 3D city models might be a solution to master this bal- and is thus a priority area for achievement of the ambi- ancing act as they can be used for urban simulation, but tious climate and energy targets for 2020 and 2050 [1]. allow an analysis for individual buildings, too. Until now, In order to reach a 2% energy refurbishment rate pro- 3D city models were mostly used for visualization pur- moted by the European Union and to realise long-term poses, but more and more studies attempt to utilize 3D climate neutral communities, a change of rhythm and of city models for other purposes beyond visualization. scale is highly required. Biljecki et al. [2] carried out a systematic survey on Building energy evaluation tools available today are documents related to the application of 3D city models. either only able to effectively analyse individual buildings They identified 29 distinct use cases in several applica- and require a high amount of input data or they are too tion domains, like e.g. estimation of solar irradiation, imprecise at a city (district) scale because of too many energy demand estimation, urban planning etc. Zhang assumptions made. Therefore, there is a strong need to et. al [3]. investigated the applications of 3D city models develop tools to precisely and easily perform a forecast to urban design. Virtual city models, which store geo- metrical and semantic data of whole cities, are also a good solution in order to perform urban energy analyses, * Correspondence: dirk.monien@hft-stuttgart.de Centre for Sustainable Energy Technology, Stuttgart University of Applied like e.g. in Karlsruhe and Ludwigsburg [4] or in Berlin Sciences, Schellingstr. 24, Stuttgart 70174, Germany Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Monien et al. Future Cities and Environment (2017) 3:2 Page 2 of 13 [5] and other cities [6, 7]. The urban simulation platform divided in a two-stage analysis; the first one is based on SimStadt [8] was developed for such urban heat demand six single buildings, the second one on four city districts simulations using precise dimensions and building in Essen. geometries that support accuracy in heat demand calcu- lation [9]. In recent years, especially in Germany Single building analysis researchers have analysed the applicability of 3D city The single building analysis is based on six case study models for energy demand estimation purposes [10–14]. buildings representing different building categories and Most available methods use the volume of buildings, years of construction (see Table 1). Each of the buildings number of floors, type of the buildings and other charac- represents a typical building decade of Essen. teristics to forecast the energy demand for heating or cooling. A general applicability of 3D geo-information systems Geometrical data to solve industrial and research problems does not exist. The case study buildings were geometrically modelled in Depending on the application, specific data are required. the CityGML (http://www.opengeospatial.org/standards/ The data quality of the city models varies, depending on citygml) standard. Such three dimensional models are the available public databases and the information col- widely available in Germany. Combined with further lected on-site. Nouvel et. al [15]. present a methodology building information on age, usage type and construc- of urban heat demand analysis that enables the investi- tion type they represent a powerful database for urban gation of the uncertainty of the model, analysing the energy demand estimation. CityGML as an OGC stand- influence of the building information (geometrical and ard is a common information model to represent, store semantical) on the simulated heat demand. The work of and exchange 3D city models. The models can be avail- Carrion [5] showed an average error of 19% between the able in different Levels of Detail (LoD) defined as follows calculated and measured data of heat demand/consump- (see also Fig. 1, left): tion. Kaden & Kolbe [16] recognised that these errors are mainly caused by the fact that in most available data  LoD1: Extrusion solid sets the actual building rehabilitation state is not known  LoD2: Geometrically simplified outer shell and city wide, so that the estimates are mostly based on en- simplified roof shapes ergy characteristic values or heat transfer coefficients as  LoD3: Geometrically detailed outer shell represented a function of the year of construction. by detailed outer surfaces and detailed roof shapes In this work, a refurbishment scenario based on actual  LoD4: Geometrically detailed outer shell and refurbishment states as an application of the software interior is represented by detailed outer and inner SimStadt will be analysed and compared to monitoring surfaces and detailed roof shapes [http:// data. en.wiki.quality.sig3d.org/index.php/Modeling]. Previous works by the authors analysed the deviations between measured and calculated values for a new build The city of Essen delivered CAD-files in LoD2 and low energy city quarter of Scharnhauser Park/Germany, LoD3 for all six case study buildings. Before using these in which the buildings had high energy efficiency stan- files with the different software packages, they had to be dards and were built over a period of approximately 10 transferred in the respective data formats. LoD2 and years. That is why the building characteristics are quite LoD3 models were extracted directly from the given well known and the rehabilitation state could not have CAD data sets. A LoD1 model was retrieved from the an influence on these deviations [11]. The analysis done available LoD2 model: The LoD1 building model uses showed a reasonable correlation for many buildings, but the mean-height bounding box of the LoD2 building there were also many buildings with extreme deviations between measured and simulated heat demand larger than 100%, which led to an average deviation between Table 1 List of reference buildings 30 and 40%. These high deviations were mainly caused Building no Building type Year of construction by the lack of detailed geometrical information regarding 1 Multi-family house (MFH) 1907 the building height for many buildings. This fact encour- 2 Multi-family house (MFH) 1954 aged the author for further analysis within this paper. 3 Multi-family house (MFH) 1910 Data description 4 Multi-family house (MFH) 1955 The city of Essen as the coming European Green Capital 5 High tower (HH) 1974 2017 is very committed to climate protection issues and 6 Single-family house (EFH) 2004 delivered the initial data for the present study, which is Monien et al. Future Cities and Environment (2017) 3:2 Page 3 of 13 Fig. 1 Levels of Detail (left); Creating LoD1 out of LoD 2 (right) model, i.e. the arithmetic average between eaves and which are available in monthly as well as hourly time ridge height (see Fig. 1, right). resolution. The authors have already carried out a range of studies in urban areas to compare the monthly energy balance Validation method with monitoring data (Karlsruhe Rintheim, Lud- As no reliable monitoring data were available for the in- wigsburg Grünbühl, Rotterdam, Scharnhauser Park Ost- dividual buildings in the town of Essen, a simulation tool fildern and others). It has been shown that on a city (see below the simulation tool description) comparison quarter level, the difference between simulations and was used here to check the results. Validation with mon- monitoring is typically below 10%, but can be higher on itoring data was then done on a city quarter aggregation an individual building level [12]. level. Care was taken on using the same input data for all models. Physical data Software tools The building physics properties like U-values (heat The building scale analysis was done to check the accur- transfer coefficients) of the building elements were acy of the urban modelling approach by comparing the assessed from the IWU building typology library [17] SimStadt results with the simulation tool TRNSYS further developed in the TABULA [18] project. Here (http://www.trnsys.com/). The settings, like physical buildings are classified as to their type (e.g. multi-family parameters, user behaviour etc. were kept the same in house, single-family house or high tower) and building order to make calculations comparable. age class. Building age (or year of construction) and building type for the six reference buildings were deliv- SimStadt Since 2012, the urban energy simulation plat- ered by the city of Essen to link the buildings to the re- form SimStadt, jointly developed by the research centers spective building typology library. for Sustainable Energy Technology (zafh.net) and for Usage and operating parameters (occupancy time, air Geo-informatics at the University of Applied Sciences change requirement, set-point temperatures, etc.) have Stuttgart and in cooperation with the companies been determined by mapping building function codes M.O.S.S. GmbH (https://www.moss.de/) and GEF AG from ALKIS (Authoritative Real Estate Cadastre Infor- (http://www.gef.de/en/home/), aims at supporting urban mation System [http://www.adv-online.de/icc/exteng/ planners and city managers with defining and coordinat- nav/443/4431019a-8c61-b111-a3b2-1718a438ad1b&sel_u ing low-carbon energy strategies for their cities with a Con=7f770498-bd6a-ef01-3bbc-251ec0023010&uTem=7 variety of multi-scale energy analyses. Two particular 3d607d6-b048-65f1-80fa-29f08a07b51a.htm]) with the features mark the SimStadt design: It is based on the reference building usages of building energy norm DIN open 3D city model standard CityGML and its V 18599 (ISO 13790). workflow-driven structure is modular and extensible. SimStadt allows to automatically calculate the monthly Meteorological data heat demand of every building of a 3D city model, using The meteorological data used for the simulation are the standardized steady state calculation (thermal mono- test reference years (TRY) weather data delivered by zone) of DIN V 18599 (ISO 13790). Mandatory input German Meteorological Service for the city of Essen, data are just the 3D city model itself and the building Monien et al. Future Cities and Environment (2017) 3:2 Page 4 of 13 Table 2 List of city districts District number Construction period Characteristic Webest1 until 1918 predominant Wilhelminian style buildings, multi-family houses WeBest2 1949-1959 predominant post-war buildings, multi-family houses Webest3 after 2004 predominant new buildings, single family houses Webest4 1970-1977 predominant large residential units, multi-family houses usage respectively the building function to link the Both simulation tools also used the same weather buildings to the corresponding libraries. dataset (tmy3-hour). City District analysis TRNSYS TRNSYS is a modular simulation tool primar- The second part of the analysis represents an analysis of ily used for transient system and building simulations four city districts in Essen (see Table 2). By comparing with a history (of development) over 30 years inter- aggregated measured consumption values and simula- nationally. In this study, TRNSYS was used for a dy- tion results of heat demand, this analysis section also namic building simulation used as reference for gives an indication on the accuracy of the urban simula- SimStadt. The geometry, building physics and also pa- tion platform SimStadt. rameters like occupation and internal gains of each ref- erence buildings were specified in the integrated building interface TRNBuild. For the transferability the Geometrical data same cubature (geometric data from the original CAD- Analogous to the single building scale analysis, the refer- datasets) as for the SimStadt simulations were used. ence districts were chosen according to their predomin- Moreover, other parameters such as temperature set ant building types to cover a large range (see Table 2). points and internal gains etc. were kept the same due to The city of Essen also delivered 3D city models for the the comparability of both simulation methods. selected reference districts (see Fig. 2 below). Fig. 2 CityGML- model visualization for four districts of Essen (using FZK-Viewer of Karlsruhe Institute of Technology KIT) Monien et al. Future Cities and Environment (2017) 3:2 Page 5 of 13 Physical data LoD3) regarding geometries. Figure 3 shows the com- Again, the building physics properties like U-values (heat parison between LoD1, LoD2 and LoD3 for all case transfer coefficients) of the building elements were study buildings. LoD3 in this case differs from LoD2 assessed from the IWU building typology library further only by the real window areas compared to standard developed in the TABULA project. values from the norms. Neither the real positioning of window areas nor overhangs and dormers have been Monitoring data taken into account in this study. The city of Essen delivered measurement data across all The deviations between LoD1 and LoD2 is obvious due reference districts. The data were provided as energy to the varying wall/roof ratio. SimStadt automatically as- consumption values (gas and electricity in case of night signs U-values from its building physics library for differ- storage heating systems) aggregated at building blocks. ent roof forms depending on the respective geometry. A Due to reasons of data protection, the consumption building model in LoD1 in this context gets another U- values were pooled for each district. value assigned as e.g. a saddle roof. Similar considerations apply to internal gains. That is why (SimStadt) simulation Meteorological data results improve in accuracy using LoD2 compared to The meteorological data used for the simulation are test LoD1. However, the deviation between LoD1 and LoD2 is reference years (TRY) weather data delivered by German smaller or equal to 10% except for building 5 (no deviation Meteorological Service for the city of Essen, which are is seen as this building has a flat roof). available in monthly as well as hourly time resolution. This corresponds to the results of a study in Ludwigsburg, Germany where the Mean Absolute Percentage Error (MAPE) of all building Energy Reference Areas Software Tools reached 9.2% between LoD1 and LoD2 [12]. At district scale, SimStadt simulation results should The deviation between LoD2 and LoD3 (“real” win- be compared with measurement data. The database dow/wall ratio) is also smaller or equal to 6% in case for SimStadt simulation on the whole does not vary of all buildings as SimStadt overestimates the demand between single building calculation and a large num- for LoD3 compared to LoD2. This surprisingly is due ber of buildings, as SimStadt performs in terms of to the fact that LoD3 in this analysis throughout cor- batch processing. responds to lower window/wall ratios as in case of LoD2 with standard values out of the norms. This on Results the one hand leads to a lower median U-value for the Building scale whole building because wall constructions normally Influence of geometrical parameters (Levels of Detail) transmit less heat than windows do. However, that ef- The SimStadt platform was firstly used to compare the fect on the other hand is more than offset by lower results for different Levels of Detail (from LoD1 to Fig. 3 Comparison of specific heat demand by varied LoDs using SimStadt Monien et al. Future Cities and Environment (2017) 3:2 Page 6 of 13 Fig. 4 Variations of the window to wall ratio and window type for the building 6 solar gains and results in an overall higher heat de- the models, but also in the modelling approach with mand for LoD3. monthly energy balances in SimStadt and hourly energy The influence of the window/wall ratio as well as demand simulations in TRNSYS. Comparing SimStadt thewindowtypeisshown on theexample of building and TRNSYS for the less detailed building geometry 6 (see Fig. 4). Window to wall ratio here means the models (LoD1), Fig. 5 shows the deviations obtained. actual window to wall ratio taken from the construc- Despite identical input data, results from TRNSYS and tion files. Unless otherwise known, SimStadt uses SimStadt differ by up to 17%. standard default values corresponding to the German The comparison between SimStadt and TRNSYS for building typology. more detailed building geometry models (LoD2) is shown in Fig. 6. Despite identical input data, results Comparison between SimStadt and TRNSYS from TRNSYS and SimStadt differ by up to 24%. SimStadt and TRNSYS differ not only in effort on enter- In both comparisons, the deviations by older buildings ing input data or defining e.g. wall constructions within (Building 1 to Building 4) are much lower than in case Fig. 5 Comparison between the SimStadt and TRNSYS simulation for the specific heat demand (LoD1 geometry) Monien et al. Future Cities and Environment (2017) 3:2 Page 7 of 13 Fig. 6 Comparison between the SimStadt and TRNSYS simulation for the specific heat demand (LoD2 geometry) of new buildings (Building 5 and Building 6). This is in Comparing both software packages (SimStadt and keeping with the views that the monthly energy balance TRNSYS), there are some advantages for the urban in general overestimates heat demand for older buildings simulation platform SimStadt: whereas it underestimates heat demand for more effi- cient buildings. Monthly simulation values underline the  SimStadt can provide an automatic calculation impression that the monthly energy balance method un- (static energy balance) of heat demand at urban derrates heat gains in older buildings (see Fig. 7 for scale as well as at single building scale (scalability building 1 built in 1907) and credits them too much in without loss of accuracy because of batch energy efficient buildings (see Fig. 8 for building number processing), while TRNSYS enables detailed 6 built in 2004). dynamic simulation but only for single buildings Fig. 7 Monthly comparison between SimStadt and TRNSYS for Building1 (LoD2) Monien et al. Future Cities and Environment (2017) 3:2 Page 8 of 13 Fig. 8 Monthly comparison between SimStadt and TRNSYS for Building1 (LoD2) with higher accuracy because internal storage an application over several buildings or whole capacities are more adequately modelled. districts, SimStadt performs much faster. Data input preparation in SimStadt is reduced to preparing the CityGML file with information District scale regarding building age and building usage; in Following, the analysis of four districts in the city of case of TRNSYS, all data inputs must be put Essen with regard to the year of construction, building manually in detail which is time consuming. In usage/typology as well as number of floors was done in Fig. 9 Year of construction Monien et al. Future Cities and Environment (2017) 3:2 Page 9 of 13 Fig. 10 Thematic maps for the building usage/typology order to see the structural differences between all Regarding only the residential buildings, the percent- districts. age ratio of each building type differs from district to district. As Table 4 shows, most buildings are multi- family houses in the districts WeBest1, WeBest2 and Year of construction WeBest3. In the districts WeBest1 and WeBest4 exhibit As shown in Fig. 9, the year of construction of the build- high amounts of big multi-family houses, within WeBest ings differs. In the districts WeBest1 and WeBest2 most 3 and especially in WeBest4 there is a significant buildings (more than 70%) were built before 1958. Most amount of high towers. buildings in the districts WeBest3 and WeBest4 were built between 1966 and 1978. Number of floors Building usage/Typology Considering the number of floors (see Table 5), in the In all districts, the majority of the buildings is in residen- districts WeBest3 and WeBest4 most buildings are tial use (see green buildings in Fig. 10). As shown in Table 3, there is also a quite high amount of commercial buildings, especially in the districts WeB- Table 4 Classification of the residential buildings for each est1 and WeBest3 (about 19%). In the districts WeBest3 district and WeBest4 there are many adjoining buildings such as District WeBest1 WeBest2 WeBest3 WeBest4 garages, which haven’t been taken into consideration by BuildingType Heated area [%] the calculation of heat demand. a EFH 4263 RH 11 16 15 7 MFH 50 51 60 14 Table 3 Classification of the building usage for each district GMH 34 28 1 42 District WeBest1 WeBest2 WeBest3 WeBest4 HH 1319 34 Building Usage Number of buildings [%] EFH – Single family houses Residential 69 73 54 60 RH – Row-houses Commercial 19 14 19 2 MFH – Multi-family houses GMH – Big multi-family houses Garages/Public parking 3 6 24 36 HH – High towers Monien et al. Future Cities and Environment (2017) 3:2 Page 10 of 13 Table 5 Classification of the number of floors for each district Table 6 Considered refurbishment ratios for each district District WeBest 01 WeBest 02 WeBest 03 WeBest 04 District Refurbishment ratio [%] Number of Floors Number of Buildings [%] WeBest01 62,5 1 29,9 25,8 68,0 56,9 WeBest02 50,0 2 7,1 15,5 21,2 15,5 WeBest03 68,1 3 41,7 36,4 3,4 1,2 WeBest04 57,6 4 20,3 17,7 5,7 3,5 5 0,3 3,0 0 1,7 gas heating systems, 83% for electrical night storage 6 0 0,4 0 8,7 heating systems) to achieve comparability. 7 0,1 0,2 0 2,9 The first heat demand simulation was done using the 8 0 0,1 0,7 4,1 SimStadt urban simulation platform without consideration Over 8 0 0,1 0 2,1 of any refurbishment scenario. This logically led to a sig- nificant overestimation of the heat demand values in all districts. The second heat demand simulation was done by considering statistical refurbishment ratios taken from single-storey buildings. In the older districts WeBest1 a representative survey in Essen (https://media.essen.de/ and WeBest2, most buildings are three or four storeys media/klimawerkstadtessen/klimawerkstadtessen_dokume high. nte/netzwerk_1/Potenziale-fuer-energieeffizientes-Modern isieren-in-Essen.pdf), which are shown in Table 6. Comparison between heat demand and heat Refurbishment scenarios can easily be defined within consumption the SimStadt graphical user interface and calculated The comparison between the SimStadt heat demand automatically. As there were no detailed information and the measured heat consumption was done separ- about the refurbishment measures in the survey, a ately for each district (see Fig. 11). As SimStadt cal- moderate package (“medium”) was assumed considering culates the net heat demand and the monitoring data oldest buildings first to be refurbished. This second calcu- were given as final energy consumption values, the lation than gave heat demand results, which are very close SimStadt simulation results had to be applied with a to the measured heat consumption in all districts, except factor for efficiency of the heating systems (88% for district WeBest2. The reason for these huge deviations Fig. 11 Comparison between SimStadt heat demand calculation and measured heat consumption (including hot water demand and consumption) Monien et al. Future Cities and Environment (2017) 3:2 Page 11 of 13 Fig. 12 Specific heat demand (space heating) as function of year of construction decade might be due to the fact that the survey only gives refur- Figure 13 shows that more compact building forms bishment ratios for larger urban districts whereas parts such as high towers and multi family houses accompany within the urban district may deviate significantly. For with lower heat demand values because of the beneficial WeBest2 the actual refurbishment rate may be higher ratio of surface to volume (A/V ratio). than the adopted refurbishment rate for the entire district. Figure 14 is in the same direction as the bigger and A deeper analysis of the four districts respectively more dense in terms of higher numbers of floors build- building periods showed that older building classes are ings are, the lower the specific heat demand per square- characterized by higher heat demand values (see Fig. 12). meter and year. Fig. 13 Specific heat demand (space heating) as function of building type Monien et al. Future Cities and Environment (2017) 3:2 Page 12 of 13 Fig. 14 Specific heat demand (space heating) as function of number of floors Conclusions Thanks are due to the work of Marcel Bruse, Claudia Schulte, Romain Nouvel, Dr.-Ing. Egbert Casper, Maximilian Haag (all HFT Stuttgart), Dr. Frank Knospe, This paper presents a method for urban scale simulation Dr.-Ing. Christian Lindner and Thomas Brune (all city of Essen). of the heat demand. To validate the monthly energy bal- ance method used in the urban simulation platform Sim- Authors’ contributions HFT Stuttgart coordinated the research work and evaluated the parametric study Stadt, the results of six different building typologies were in different scenarios by employing urban simulation platform SimStadt compared with dynamic building simulations. (www.simstadt.eu), INSEL software (www.insel.eu) and TRNSYS (www.trnsys.como) The simulation method comparison resulted in a and wrote the paper. The city of Essen delivered building data (geometries, semantics) for the six case study buildings as well as the aggregated energy reasonable agreement for four buildings out of six consumption data for the four analysed districts. All authors read and approved (deviation </= 10%) and larger deviations in two cases the final manuscript. (17 and 24%). Preparing and transferring building Competing interests models to TRNSYS proved to be timeintensive, while The authors declare that they have no competing interests. the SimStadt process is fully automated. At district scale, the SimStadt results were compared Author details Centre for Sustainable Energy Technology, Stuttgart University of Applied with aggregated consumption values. When realistic Sciences, Schellingstr. 24, Stuttgart 70174, Germany. Centre for Geodesy and refurbishment percentages were included in the urban Geoinformatics, Stuttgart University of Applied Sciences, Schellingstr. 24, simulation, the deviation was less than 9% in three dis- Stuttgart 70174, Germany. tricts and 40% in one of the four districts. This high Received: 4 February 2016 Accepted: 12 January 2017 deviation might due to differences in detail relating to the survey on refurbishment states per district. References The analysis showed that heat demand forecast with 1. Federal Ministry of Economics and Technology (2010) Energy Concept for the urban simulation platform SimStadt based on 3D an Environmentally Sound, Reliable and Affordable Energy Supply. http:// city models and building construction data bases as a www.bmwi.de/English/Redaktion/Pdf/energy-concept 2. Biljecki F, Stoter J, Ledoux H, Zlatanova S, Çöltekin A (2015) Applications function of building type and year of construction is of 3D city models: state of the art review. ISPRS Int J Geo-Inf 4:2842–2889. suitable for both single buildings and city (district) doi:10.3390/ijgi4042842 scale without loss of quality by scaling up the 3. Zhan X, Zhu Q (2004) Applications of 3D city models based spatial analysis to urban design. Int. Arch. Photogramm. Remote Sens Spat Inf Sci XXXV/B2: granularity. 325–329 4. Nouvel R, Schulte C, Eicker U, Pietruschka D (2013) CityGML- based 3D city Acknowledgment Model for energy diagnostics and urban energy policy support. Proceedings This research was funded by the BLE (Bundesanstalt für Landwirtschaft und of IBSA2013: 13th Conference of International Building Performance Ernährung, English: Federal Agency for Agriculture and Food), Project WeBest Simulation Association, Chambéry, France. August 26–28 (Wärmebedarfsprognose von Gebäuden und Stadtquartieren, English: Heat 5. Carrion D, Lorenz A, Kolbe T (2010) Estimation of the energetic demand forecast for buildings and city quarters). rehabilitation state of the buildings for the city of Berlin using a 3D city Monien et al. Future Cities and Environment (2017) 3:2 Page 13 of 13 Model represented in cityGML, Proceedings of ISPRS Conference: International Conference on 3D Geoionformation. XXXVIII-4, Berlin, p 31–35 6. Bahu JM, Koch A, Kremers E, Murshed SM (2014) Towards a 3D spatial urban energy modelling approach. Int J 3-D Inf Model 3:1–16 7. Robinson D, Haldi F, Kämpf J, Leroux P, Perez D, Rasheed A, Wilke U (2009) CITYSIM: Comprehensive micro-simulation of resource flows for sustainable urban planning. Proceedings of 11th International IBPSA Conference, Building Simulation 2009, Glasgow, Scotland 8. SimStadt Project (2012-2014) Heat demand simulation of city quarters. Website http://www.simstadt.eu/en/index.html 9. Bruse M, Nouvel R, Wate P, Kraut V, Coors V. An energy-related CityGML ADE and its application for heating demand calculation. Int J 3-D Inform Model (IJ3DIM) 4(3):59–77. IGI GLobal, doi: 10.4018/IJ3DIM.2015070104 10. Blesl M, Kempe S, Huther H (2010) Capture of spatial high-resolution building heating energy demand. Euroheat Power 39(1-2):28–33 [in German] 11. Strzalka A, Boghdan J, Coors V, Eicker U (2011) 3D city modelling for urban scale heating energy demand forecasting. J HVAC&R Res 17(4):526–539 12. Nouvel R, Zirak M, Dastageeri H, Coors V, Eicker U (2014) Urban energy analysis based on 3D city model for national scale applications, BauSIM 5th German-Austrian IBPSA Conference. RWTH Aachen University 13. Nouvel R, Brassel KH, Bruse M, Duminil É, Coors V, Eicker U, Robinson D (2015) SimStadt, a new workflow-driven urban energy simulation platform for cityGML city models. CISBAT Intern. Conference 2015. Lausanne, September 14. Kaden R, Kolbe T (2014) Simulation-based total energy demand estimation of buildings using semantic 3D city models. Int J 3-D Inf Model 3:35–53 15. Nouvel R, Mastrucci A, Leopold U, Baume O, Coors V, Eicker U (2015) Combining GIS–based statistical and engineering urban heat consumption models: towards a new framework for multi-scale policy support. Energy Build 107:204–212 16. Kaden R, Kolbe T (2013) Citywide energy demand estimation of buildings using semantic 3D city models and statistical data. ISPRS Anna Photogrammetry Remote Sensing Spat Inf Sci II-2/W1:163–171 17. IWU (2015) German Building Typology 18. IWU (2015) http://www.iwu.de/1/forschung/energie/completed-projects/tablua/ Submit your manuscript to a journal and benefi t from: 7 Convenient online submission 7 Rigorous peer review 7 Immediate publication on acceptance 7 Open access: articles freely available online 7 High visibility within the fi eld 7 Retaining the copyright to your article Submit your next manuscript at 7 springeropen.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Future Cities and Environment Springer Journals

Comparison of building modelling assumptions and methods for urban scale heat demand forecasting

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Springer Journals
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Copyright © 2017 by The Author(s)
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Energy; Energy Efficiency; Renewable and Green Energy; Energy Technology; Landscape/Regional and Urban Planning
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2363-9075
DOI
10.1186/s40984-017-0025-7
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Abstract

Building energy evaluation tools available today are only able to effectively analyse individual buildings and usually either they require a high amount of input data or they are too imprecise in energy predictions at a city (district) scale because of too many assumptions made. In this paper, two tools based on 3D models are compared to see whether there is an approach that would probably be able to fit both – the amount of data available and the number of assumptions made. A case study in the German town of Essen was chosen in the framework of the research project WeBest, where six building types representing the most important building periods were analysed. The urban simulation tool SimStadt, an in-house development of HFT Stuttgart, based on 3D urban geometry, is used to calculate the heat demand for both single building scale and city district scale. The individual building typology results are compared with the commercial dynamic building simulation software TRNSYS. The influence of the availability and quality of data input regarding the geometrical building parameters on the accuracy of simulation models are analysed. Different Levels of Details (LoDs) of the 3D building models are tested to prove the scalability of SimStadt from single buildings to city districts without loss of quality and accuracy in larger areas with a short computational time. Keywords: Heat demand simulation, 3D city model, CityGML, Scalability of urban models Introduction of heat demand for different scales from single buildings The building sector has a large potential in the EU- to an urban (district) level. economy for energy efficiency gains and CO -reductions 3D city models might be a solution to master this bal- and is thus a priority area for achievement of the ambi- ancing act as they can be used for urban simulation, but tious climate and energy targets for 2020 and 2050 [1]. allow an analysis for individual buildings, too. Until now, In order to reach a 2% energy refurbishment rate pro- 3D city models were mostly used for visualization pur- moted by the European Union and to realise long-term poses, but more and more studies attempt to utilize 3D climate neutral communities, a change of rhythm and of city models for other purposes beyond visualization. scale is highly required. Biljecki et al. [2] carried out a systematic survey on Building energy evaluation tools available today are documents related to the application of 3D city models. either only able to effectively analyse individual buildings They identified 29 distinct use cases in several applica- and require a high amount of input data or they are too tion domains, like e.g. estimation of solar irradiation, imprecise at a city (district) scale because of too many energy demand estimation, urban planning etc. Zhang assumptions made. Therefore, there is a strong need to et. al [3]. investigated the applications of 3D city models develop tools to precisely and easily perform a forecast to urban design. Virtual city models, which store geo- metrical and semantic data of whole cities, are also a good solution in order to perform urban energy analyses, * Correspondence: dirk.monien@hft-stuttgart.de Centre for Sustainable Energy Technology, Stuttgart University of Applied like e.g. in Karlsruhe and Ludwigsburg [4] or in Berlin Sciences, Schellingstr. 24, Stuttgart 70174, Germany Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Monien et al. Future Cities and Environment (2017) 3:2 Page 2 of 13 [5] and other cities [6, 7]. The urban simulation platform divided in a two-stage analysis; the first one is based on SimStadt [8] was developed for such urban heat demand six single buildings, the second one on four city districts simulations using precise dimensions and building in Essen. geometries that support accuracy in heat demand calcu- lation [9]. In recent years, especially in Germany Single building analysis researchers have analysed the applicability of 3D city The single building analysis is based on six case study models for energy demand estimation purposes [10–14]. buildings representing different building categories and Most available methods use the volume of buildings, years of construction (see Table 1). Each of the buildings number of floors, type of the buildings and other charac- represents a typical building decade of Essen. teristics to forecast the energy demand for heating or cooling. A general applicability of 3D geo-information systems Geometrical data to solve industrial and research problems does not exist. The case study buildings were geometrically modelled in Depending on the application, specific data are required. the CityGML (http://www.opengeospatial.org/standards/ The data quality of the city models varies, depending on citygml) standard. Such three dimensional models are the available public databases and the information col- widely available in Germany. Combined with further lected on-site. Nouvel et. al [15]. present a methodology building information on age, usage type and construc- of urban heat demand analysis that enables the investi- tion type they represent a powerful database for urban gation of the uncertainty of the model, analysing the energy demand estimation. CityGML as an OGC stand- influence of the building information (geometrical and ard is a common information model to represent, store semantical) on the simulated heat demand. The work of and exchange 3D city models. The models can be avail- Carrion [5] showed an average error of 19% between the able in different Levels of Detail (LoD) defined as follows calculated and measured data of heat demand/consump- (see also Fig. 1, left): tion. Kaden & Kolbe [16] recognised that these errors are mainly caused by the fact that in most available data  LoD1: Extrusion solid sets the actual building rehabilitation state is not known  LoD2: Geometrically simplified outer shell and city wide, so that the estimates are mostly based on en- simplified roof shapes ergy characteristic values or heat transfer coefficients as  LoD3: Geometrically detailed outer shell represented a function of the year of construction. by detailed outer surfaces and detailed roof shapes In this work, a refurbishment scenario based on actual  LoD4: Geometrically detailed outer shell and refurbishment states as an application of the software interior is represented by detailed outer and inner SimStadt will be analysed and compared to monitoring surfaces and detailed roof shapes [http:// data. en.wiki.quality.sig3d.org/index.php/Modeling]. Previous works by the authors analysed the deviations between measured and calculated values for a new build The city of Essen delivered CAD-files in LoD2 and low energy city quarter of Scharnhauser Park/Germany, LoD3 for all six case study buildings. Before using these in which the buildings had high energy efficiency stan- files with the different software packages, they had to be dards and were built over a period of approximately 10 transferred in the respective data formats. LoD2 and years. That is why the building characteristics are quite LoD3 models were extracted directly from the given well known and the rehabilitation state could not have CAD data sets. A LoD1 model was retrieved from the an influence on these deviations [11]. The analysis done available LoD2 model: The LoD1 building model uses showed a reasonable correlation for many buildings, but the mean-height bounding box of the LoD2 building there were also many buildings with extreme deviations between measured and simulated heat demand larger than 100%, which led to an average deviation between Table 1 List of reference buildings 30 and 40%. These high deviations were mainly caused Building no Building type Year of construction by the lack of detailed geometrical information regarding 1 Multi-family house (MFH) 1907 the building height for many buildings. This fact encour- 2 Multi-family house (MFH) 1954 aged the author for further analysis within this paper. 3 Multi-family house (MFH) 1910 Data description 4 Multi-family house (MFH) 1955 The city of Essen as the coming European Green Capital 5 High tower (HH) 1974 2017 is very committed to climate protection issues and 6 Single-family house (EFH) 2004 delivered the initial data for the present study, which is Monien et al. Future Cities and Environment (2017) 3:2 Page 3 of 13 Fig. 1 Levels of Detail (left); Creating LoD1 out of LoD 2 (right) model, i.e. the arithmetic average between eaves and which are available in monthly as well as hourly time ridge height (see Fig. 1, right). resolution. The authors have already carried out a range of studies in urban areas to compare the monthly energy balance Validation method with monitoring data (Karlsruhe Rintheim, Lud- As no reliable monitoring data were available for the in- wigsburg Grünbühl, Rotterdam, Scharnhauser Park Ost- dividual buildings in the town of Essen, a simulation tool fildern and others). It has been shown that on a city (see below the simulation tool description) comparison quarter level, the difference between simulations and was used here to check the results. Validation with mon- monitoring is typically below 10%, but can be higher on itoring data was then done on a city quarter aggregation an individual building level [12]. level. Care was taken on using the same input data for all models. Physical data Software tools The building physics properties like U-values (heat The building scale analysis was done to check the accur- transfer coefficients) of the building elements were acy of the urban modelling approach by comparing the assessed from the IWU building typology library [17] SimStadt results with the simulation tool TRNSYS further developed in the TABULA [18] project. Here (http://www.trnsys.com/). The settings, like physical buildings are classified as to their type (e.g. multi-family parameters, user behaviour etc. were kept the same in house, single-family house or high tower) and building order to make calculations comparable. age class. Building age (or year of construction) and building type for the six reference buildings were deliv- SimStadt Since 2012, the urban energy simulation plat- ered by the city of Essen to link the buildings to the re- form SimStadt, jointly developed by the research centers spective building typology library. for Sustainable Energy Technology (zafh.net) and for Usage and operating parameters (occupancy time, air Geo-informatics at the University of Applied Sciences change requirement, set-point temperatures, etc.) have Stuttgart and in cooperation with the companies been determined by mapping building function codes M.O.S.S. GmbH (https://www.moss.de/) and GEF AG from ALKIS (Authoritative Real Estate Cadastre Infor- (http://www.gef.de/en/home/), aims at supporting urban mation System [http://www.adv-online.de/icc/exteng/ planners and city managers with defining and coordinat- nav/443/4431019a-8c61-b111-a3b2-1718a438ad1b&sel_u ing low-carbon energy strategies for their cities with a Con=7f770498-bd6a-ef01-3bbc-251ec0023010&uTem=7 variety of multi-scale energy analyses. Two particular 3d607d6-b048-65f1-80fa-29f08a07b51a.htm]) with the features mark the SimStadt design: It is based on the reference building usages of building energy norm DIN open 3D city model standard CityGML and its V 18599 (ISO 13790). workflow-driven structure is modular and extensible. SimStadt allows to automatically calculate the monthly Meteorological data heat demand of every building of a 3D city model, using The meteorological data used for the simulation are the standardized steady state calculation (thermal mono- test reference years (TRY) weather data delivered by zone) of DIN V 18599 (ISO 13790). Mandatory input German Meteorological Service for the city of Essen, data are just the 3D city model itself and the building Monien et al. Future Cities and Environment (2017) 3:2 Page 4 of 13 Table 2 List of city districts District number Construction period Characteristic Webest1 until 1918 predominant Wilhelminian style buildings, multi-family houses WeBest2 1949-1959 predominant post-war buildings, multi-family houses Webest3 after 2004 predominant new buildings, single family houses Webest4 1970-1977 predominant large residential units, multi-family houses usage respectively the building function to link the Both simulation tools also used the same weather buildings to the corresponding libraries. dataset (tmy3-hour). City District analysis TRNSYS TRNSYS is a modular simulation tool primar- The second part of the analysis represents an analysis of ily used for transient system and building simulations four city districts in Essen (see Table 2). By comparing with a history (of development) over 30 years inter- aggregated measured consumption values and simula- nationally. In this study, TRNSYS was used for a dy- tion results of heat demand, this analysis section also namic building simulation used as reference for gives an indication on the accuracy of the urban simula- SimStadt. The geometry, building physics and also pa- tion platform SimStadt. rameters like occupation and internal gains of each ref- erence buildings were specified in the integrated building interface TRNBuild. For the transferability the Geometrical data same cubature (geometric data from the original CAD- Analogous to the single building scale analysis, the refer- datasets) as for the SimStadt simulations were used. ence districts were chosen according to their predomin- Moreover, other parameters such as temperature set ant building types to cover a large range (see Table 2). points and internal gains etc. were kept the same due to The city of Essen also delivered 3D city models for the the comparability of both simulation methods. selected reference districts (see Fig. 2 below). Fig. 2 CityGML- model visualization for four districts of Essen (using FZK-Viewer of Karlsruhe Institute of Technology KIT) Monien et al. Future Cities and Environment (2017) 3:2 Page 5 of 13 Physical data LoD3) regarding geometries. Figure 3 shows the com- Again, the building physics properties like U-values (heat parison between LoD1, LoD2 and LoD3 for all case transfer coefficients) of the building elements were study buildings. LoD3 in this case differs from LoD2 assessed from the IWU building typology library further only by the real window areas compared to standard developed in the TABULA project. values from the norms. Neither the real positioning of window areas nor overhangs and dormers have been Monitoring data taken into account in this study. The city of Essen delivered measurement data across all The deviations between LoD1 and LoD2 is obvious due reference districts. The data were provided as energy to the varying wall/roof ratio. SimStadt automatically as- consumption values (gas and electricity in case of night signs U-values from its building physics library for differ- storage heating systems) aggregated at building blocks. ent roof forms depending on the respective geometry. A Due to reasons of data protection, the consumption building model in LoD1 in this context gets another U- values were pooled for each district. value assigned as e.g. a saddle roof. Similar considerations apply to internal gains. That is why (SimStadt) simulation Meteorological data results improve in accuracy using LoD2 compared to The meteorological data used for the simulation are test LoD1. However, the deviation between LoD1 and LoD2 is reference years (TRY) weather data delivered by German smaller or equal to 10% except for building 5 (no deviation Meteorological Service for the city of Essen, which are is seen as this building has a flat roof). available in monthly as well as hourly time resolution. This corresponds to the results of a study in Ludwigsburg, Germany where the Mean Absolute Percentage Error (MAPE) of all building Energy Reference Areas Software Tools reached 9.2% between LoD1 and LoD2 [12]. At district scale, SimStadt simulation results should The deviation between LoD2 and LoD3 (“real” win- be compared with measurement data. The database dow/wall ratio) is also smaller or equal to 6% in case for SimStadt simulation on the whole does not vary of all buildings as SimStadt overestimates the demand between single building calculation and a large num- for LoD3 compared to LoD2. This surprisingly is due ber of buildings, as SimStadt performs in terms of to the fact that LoD3 in this analysis throughout cor- batch processing. responds to lower window/wall ratios as in case of LoD2 with standard values out of the norms. This on Results the one hand leads to a lower median U-value for the Building scale whole building because wall constructions normally Influence of geometrical parameters (Levels of Detail) transmit less heat than windows do. However, that ef- The SimStadt platform was firstly used to compare the fect on the other hand is more than offset by lower results for different Levels of Detail (from LoD1 to Fig. 3 Comparison of specific heat demand by varied LoDs using SimStadt Monien et al. Future Cities and Environment (2017) 3:2 Page 6 of 13 Fig. 4 Variations of the window to wall ratio and window type for the building 6 solar gains and results in an overall higher heat de- the models, but also in the modelling approach with mand for LoD3. monthly energy balances in SimStadt and hourly energy The influence of the window/wall ratio as well as demand simulations in TRNSYS. Comparing SimStadt thewindowtypeisshown on theexample of building and TRNSYS for the less detailed building geometry 6 (see Fig. 4). Window to wall ratio here means the models (LoD1), Fig. 5 shows the deviations obtained. actual window to wall ratio taken from the construc- Despite identical input data, results from TRNSYS and tion files. Unless otherwise known, SimStadt uses SimStadt differ by up to 17%. standard default values corresponding to the German The comparison between SimStadt and TRNSYS for building typology. more detailed building geometry models (LoD2) is shown in Fig. 6. Despite identical input data, results Comparison between SimStadt and TRNSYS from TRNSYS and SimStadt differ by up to 24%. SimStadt and TRNSYS differ not only in effort on enter- In both comparisons, the deviations by older buildings ing input data or defining e.g. wall constructions within (Building 1 to Building 4) are much lower than in case Fig. 5 Comparison between the SimStadt and TRNSYS simulation for the specific heat demand (LoD1 geometry) Monien et al. Future Cities and Environment (2017) 3:2 Page 7 of 13 Fig. 6 Comparison between the SimStadt and TRNSYS simulation for the specific heat demand (LoD2 geometry) of new buildings (Building 5 and Building 6). This is in Comparing both software packages (SimStadt and keeping with the views that the monthly energy balance TRNSYS), there are some advantages for the urban in general overestimates heat demand for older buildings simulation platform SimStadt: whereas it underestimates heat demand for more effi- cient buildings. Monthly simulation values underline the  SimStadt can provide an automatic calculation impression that the monthly energy balance method un- (static energy balance) of heat demand at urban derrates heat gains in older buildings (see Fig. 7 for scale as well as at single building scale (scalability building 1 built in 1907) and credits them too much in without loss of accuracy because of batch energy efficient buildings (see Fig. 8 for building number processing), while TRNSYS enables detailed 6 built in 2004). dynamic simulation but only for single buildings Fig. 7 Monthly comparison between SimStadt and TRNSYS for Building1 (LoD2) Monien et al. Future Cities and Environment (2017) 3:2 Page 8 of 13 Fig. 8 Monthly comparison between SimStadt and TRNSYS for Building1 (LoD2) with higher accuracy because internal storage an application over several buildings or whole capacities are more adequately modelled. districts, SimStadt performs much faster. Data input preparation in SimStadt is reduced to preparing the CityGML file with information District scale regarding building age and building usage; in Following, the analysis of four districts in the city of case of TRNSYS, all data inputs must be put Essen with regard to the year of construction, building manually in detail which is time consuming. In usage/typology as well as number of floors was done in Fig. 9 Year of construction Monien et al. Future Cities and Environment (2017) 3:2 Page 9 of 13 Fig. 10 Thematic maps for the building usage/typology order to see the structural differences between all Regarding only the residential buildings, the percent- districts. age ratio of each building type differs from district to district. As Table 4 shows, most buildings are multi- family houses in the districts WeBest1, WeBest2 and Year of construction WeBest3. In the districts WeBest1 and WeBest4 exhibit As shown in Fig. 9, the year of construction of the build- high amounts of big multi-family houses, within WeBest ings differs. In the districts WeBest1 and WeBest2 most 3 and especially in WeBest4 there is a significant buildings (more than 70%) were built before 1958. Most amount of high towers. buildings in the districts WeBest3 and WeBest4 were built between 1966 and 1978. Number of floors Building usage/Typology Considering the number of floors (see Table 5), in the In all districts, the majority of the buildings is in residen- districts WeBest3 and WeBest4 most buildings are tial use (see green buildings in Fig. 10). As shown in Table 3, there is also a quite high amount of commercial buildings, especially in the districts WeB- Table 4 Classification of the residential buildings for each est1 and WeBest3 (about 19%). In the districts WeBest3 district and WeBest4 there are many adjoining buildings such as District WeBest1 WeBest2 WeBest3 WeBest4 garages, which haven’t been taken into consideration by BuildingType Heated area [%] the calculation of heat demand. a EFH 4263 RH 11 16 15 7 MFH 50 51 60 14 Table 3 Classification of the building usage for each district GMH 34 28 1 42 District WeBest1 WeBest2 WeBest3 WeBest4 HH 1319 34 Building Usage Number of buildings [%] EFH – Single family houses Residential 69 73 54 60 RH – Row-houses Commercial 19 14 19 2 MFH – Multi-family houses GMH – Big multi-family houses Garages/Public parking 3 6 24 36 HH – High towers Monien et al. Future Cities and Environment (2017) 3:2 Page 10 of 13 Table 5 Classification of the number of floors for each district Table 6 Considered refurbishment ratios for each district District WeBest 01 WeBest 02 WeBest 03 WeBest 04 District Refurbishment ratio [%] Number of Floors Number of Buildings [%] WeBest01 62,5 1 29,9 25,8 68,0 56,9 WeBest02 50,0 2 7,1 15,5 21,2 15,5 WeBest03 68,1 3 41,7 36,4 3,4 1,2 WeBest04 57,6 4 20,3 17,7 5,7 3,5 5 0,3 3,0 0 1,7 gas heating systems, 83% for electrical night storage 6 0 0,4 0 8,7 heating systems) to achieve comparability. 7 0,1 0,2 0 2,9 The first heat demand simulation was done using the 8 0 0,1 0,7 4,1 SimStadt urban simulation platform without consideration Over 8 0 0,1 0 2,1 of any refurbishment scenario. This logically led to a sig- nificant overestimation of the heat demand values in all districts. The second heat demand simulation was done by considering statistical refurbishment ratios taken from single-storey buildings. In the older districts WeBest1 a representative survey in Essen (https://media.essen.de/ and WeBest2, most buildings are three or four storeys media/klimawerkstadtessen/klimawerkstadtessen_dokume high. nte/netzwerk_1/Potenziale-fuer-energieeffizientes-Modern isieren-in-Essen.pdf), which are shown in Table 6. Comparison between heat demand and heat Refurbishment scenarios can easily be defined within consumption the SimStadt graphical user interface and calculated The comparison between the SimStadt heat demand automatically. As there were no detailed information and the measured heat consumption was done separ- about the refurbishment measures in the survey, a ately for each district (see Fig. 11). As SimStadt cal- moderate package (“medium”) was assumed considering culates the net heat demand and the monitoring data oldest buildings first to be refurbished. This second calcu- were given as final energy consumption values, the lation than gave heat demand results, which are very close SimStadt simulation results had to be applied with a to the measured heat consumption in all districts, except factor for efficiency of the heating systems (88% for district WeBest2. The reason for these huge deviations Fig. 11 Comparison between SimStadt heat demand calculation and measured heat consumption (including hot water demand and consumption) Monien et al. Future Cities and Environment (2017) 3:2 Page 11 of 13 Fig. 12 Specific heat demand (space heating) as function of year of construction decade might be due to the fact that the survey only gives refur- Figure 13 shows that more compact building forms bishment ratios for larger urban districts whereas parts such as high towers and multi family houses accompany within the urban district may deviate significantly. For with lower heat demand values because of the beneficial WeBest2 the actual refurbishment rate may be higher ratio of surface to volume (A/V ratio). than the adopted refurbishment rate for the entire district. Figure 14 is in the same direction as the bigger and A deeper analysis of the four districts respectively more dense in terms of higher numbers of floors build- building periods showed that older building classes are ings are, the lower the specific heat demand per square- characterized by higher heat demand values (see Fig. 12). meter and year. Fig. 13 Specific heat demand (space heating) as function of building type Monien et al. Future Cities and Environment (2017) 3:2 Page 12 of 13 Fig. 14 Specific heat demand (space heating) as function of number of floors Conclusions Thanks are due to the work of Marcel Bruse, Claudia Schulte, Romain Nouvel, Dr.-Ing. Egbert Casper, Maximilian Haag (all HFT Stuttgart), Dr. Frank Knospe, This paper presents a method for urban scale simulation Dr.-Ing. Christian Lindner and Thomas Brune (all city of Essen). of the heat demand. To validate the monthly energy bal- ance method used in the urban simulation platform Sim- Authors’ contributions HFT Stuttgart coordinated the research work and evaluated the parametric study Stadt, the results of six different building typologies were in different scenarios by employing urban simulation platform SimStadt compared with dynamic building simulations. (www.simstadt.eu), INSEL software (www.insel.eu) and TRNSYS (www.trnsys.como) The simulation method comparison resulted in a and wrote the paper. The city of Essen delivered building data (geometries, semantics) for the six case study buildings as well as the aggregated energy reasonable agreement for four buildings out of six consumption data for the four analysed districts. All authors read and approved (deviation </= 10%) and larger deviations in two cases the final manuscript. (17 and 24%). Preparing and transferring building Competing interests models to TRNSYS proved to be timeintensive, while The authors declare that they have no competing interests. the SimStadt process is fully automated. At district scale, the SimStadt results were compared Author details Centre for Sustainable Energy Technology, Stuttgart University of Applied with aggregated consumption values. When realistic Sciences, Schellingstr. 24, Stuttgart 70174, Germany. Centre for Geodesy and refurbishment percentages were included in the urban Geoinformatics, Stuttgart University of Applied Sciences, Schellingstr. 24, simulation, the deviation was less than 9% in three dis- Stuttgart 70174, Germany. tricts and 40% in one of the four districts. This high Received: 4 February 2016 Accepted: 12 January 2017 deviation might due to differences in detail relating to the survey on refurbishment states per district. References The analysis showed that heat demand forecast with 1. Federal Ministry of Economics and Technology (2010) Energy Concept for the urban simulation platform SimStadt based on 3D an Environmentally Sound, Reliable and Affordable Energy Supply. http:// city models and building construction data bases as a www.bmwi.de/English/Redaktion/Pdf/energy-concept 2. Biljecki F, Stoter J, Ledoux H, Zlatanova S, Çöltekin A (2015) Applications function of building type and year of construction is of 3D city models: state of the art review. ISPRS Int J Geo-Inf 4:2842–2889. suitable for both single buildings and city (district) doi:10.3390/ijgi4042842 scale without loss of quality by scaling up the 3. Zhan X, Zhu Q (2004) Applications of 3D city models based spatial analysis to urban design. Int. Arch. Photogramm. 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ISPRS Anna Photogrammetry Remote Sensing Spat Inf Sci II-2/W1:163–171 17. IWU (2015) German Building Typology 18. IWU (2015) http://www.iwu.de/1/forschung/energie/completed-projects/tablua/ Submit your manuscript to a journal and benefi t from: 7 Convenient online submission 7 Rigorous peer review 7 Immediate publication on acceptance 7 Open access: articles freely available online 7 High visibility within the fi eld 7 Retaining the copyright to your article Submit your next manuscript at 7 springeropen.com

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Future Cities and EnvironmentSpringer Journals

Published: Jan 21, 2017

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