Life Cycle Assessment of Technical Building Services of Large Residential Ruilding Stocks using Semantic 3D City Models

Abstract

Reducing the demand for non-renewable resources and the resulting environmental impact is an objective of sustainable development, to which buildings contribute significantly. In order to realize the goal of reaching a climate-neutral building stock, it must first be analyzed and evaluated in order to develop optimization strategies. The life cycle based consideration and assessment of buildings plays a key role in this process. Approaches and tools already exist for this purpose, but they mainly take the operational energy demand of buildings and not a life cycle based approach into account, especially when assessing technical building services (TBS). Therefore, this paper presents and applies a methodical approach for the life cycle based assessment of the TBS of large residential building stocks, based on semantic 3D city models (CityGML). The methodical approach developed for this purpose describes the procedure for calculating the operational energy demand (already validated) and the heating load of the building, the dimensioning of the TBS components and the calculation of the life cycle assessment. The application of the methodology is illustrated in a case study with over 115,000 residential buildings from Munich, Germany. The study shows that the methodology calculates reliable results and that a significant reduction of the life cycle based energy demand can be achieved by refurbishment measures/scenarios. Nevertheless, the goal of achieving a climate-neutral building stock is a challenge from a life cycle perspective.

Full text

LIFE CYCLE ASSESSMENT OF TECHNICAL BUILDING SERVICES OF LARGE
RESIDENTIAL BUILDING STOCKS USING SEMANTIC 3D CITY MODELS
H. Harter1,∗
, B. Willenborg2, W. Lang1, T. H. Kolbe2
1Institute of Energy Efficient and Sustainable Design and Building,
Technical University of Munich (TUM), Munich, Germany - (hannes.harter, sekretariat.enpb.bgu)@tum.de
2Chair of Geoinformatics, Technical University of Munich (TUM), Munich, Germany - (b.willenborg, thomas.kolbe)@tum.de
Commission VI, WG VI/4
KEY WORDS: Life Cycle Assessment (LCA), Technical Building Services, Energy Demand Calculation, Sustainable Building,
Sustainable City Development, Semantic 3D City Models, CityGML, Urban Simulation
ABSTRACT:
Reducing the demand for non-renewable resources and the resulting environmental impact is an objective of sustainable develop-
ment, to which buildings contribute significantly. In order to realize the goal of reaching a climate-neutral building stock, it must
first be analyzed and evaluated in order to develop optimization strategies. The life cycle based consideration and assessment of
buildings plays a key role in this process. Approaches and tools already exist for this purpose, but they mainly take the operational
energy demand of buildings and not a life cycle based approach into account, especially when assessing technical building services
(TBS). Therefore, this paper presents and applies a methodical approach for the life cycle based assessment of the TBS of large
residential building stocks, based on semantic 3D city models (CityGML). The methodical approach developed for this purpose
describes the procedure for calculating the operational energy demand (already validated) and the heating load of the building,
the dimensioning of the TBS components and the calculation of the life cycle assessment. The application of the methodology is
illustrated in a case study with over 115,000 residential buildings from Munich, Germany. The study shows that the methodology
calculates reliable results and that a significant reduction of the life cycle based energy demand can be achieved by refurbishment
measures/scenarios. Nevertheless, the goal of achieving a climate-neutral building stock is a challenge from a life cycle perspective.
1. INTRODUCTION
Worldwide, buildings and construction account for about 35%
of final energy consumption and are responsible for almost 40%
of energy related carbon dioxide (CO2) emissions. Buildings
thus account for 30% (22% residential buildings) of global fi-
nal energy consumption and 28% (17% residential buildings)
of global energy-related CO2-emissions by sector (UN Envir-
onment and IAE, 2017).
These numbers clearly indicate the urgent need to cut down en-
ergy consumption and CO2-emissions of the worldwide build-
ing stock. For that reason, the European Union has set the goal
to achieve a climate-neutral building stock in its 2050 long-term
strategy (EU Commission, 2018). However, it must be kept in
mind, that these numbers and goals generally only reflect the
energy consumption and CO2-emissions related to the opera-
tion of buildings. All energy consumptions and CO2-emissions
resulting from life cycle stages before and after the building’s
use stage are generally not taken into account.
With tightening regulations concerning the reduction of build-
ing energy consumption, e. g. the Energy Performance of
Building Directive (EU Commission, 2018) on the European
level, or the Energy Saving Ordinance (EnEV) (German Fed-
eral Ministry of EAE, 2013) on the German national level, min-
imum energy efficiency standards for (non-)residential build-
ings are defined. The introduction of these regulations led to a
decrease in energy consumption during the use stage of build-
ings. However, the decreasing energy consumption in the use
stage is related, for example, to an increasing use of insula-
tion materials and efficient, more complex components of the
∗Corresponding author
TBS (but with lower output from the heat generators), or to
the trend towards reducing building automation and lowering
energy consumption through the use of more passive building
measures (orientation of buildings, natural ventilation, indoor
light control). In both cases this leads to more material re-
sources beeing needed to fulfill the task of increasing energy
efficiency and decreasing CO2-emissions in the buildings use
stage. Thus, it can happen that for buildings, that have a very
low energy consumption in the use stage, it is no longer the
energy consumption in this stage that accounts for the largest
share over the entire life cycle, but rather the life cycle phases
before and after the use of the building (Cabeza et al., 2014).
Consequently, when talking about sustainable building or city
development, the ecological and energetic performance of
buildings must be evaluated and analyzed over the whole life
cycle. For this purpose, Life Cycle Assessment (LCA) meth-
ods can be used. However, the implementation of LCA has not
yet been made mandatory in legislation at neither the European
nor German national level. For this reason, a LCA for buildings
is very rarely calculated in practice and is hardly included in
the decision-making process of building planning. In addition,
even if LCAs are performed for buildings, in most cases this
is only done on the basis of a single building considering only
the building construction, without consideration of the technical
building services (TBS). As life cycle based analysis allows for
the identification of savings potentials and to support political
decision making regarding regulations, it is important to calcu-
late LCAs for large building stocks.
This study presents a methodological approach for an auto-
mated calculation of life cycle based energy assessment of TBS
of large residential building stocks based on semantic 3D city
models according to the CityGML standard.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-85-2020 | © Authors 2020. CC BY 4.0 License.
85

1.1 Related work
The use of semantic 3D city models for energetic considera-
tions and assessment of the building stock is an active field of
research. Several projects are based on the EnergyADE, an ex-
tension of the CityGML standard for urban energy modeling
and simulation (Nouvel et al., 2015a).
In his PhD thesis Kaden developed EnEV-compliant methods
(German Federal Ministry of EAE, 2013) for the estimation of
the heat demand and saving potentials by refurbishing as well
as for the estimation of electricity and warm water demand of
residential buildings, based on the Energy Atlas of the City of
Berlin. The input and energy demand values were modeled ac-
cording to the EnergyADE (Kaden, 2014). In 2018, the work
of Kaden was re-implemented using the software FME by Safe
Software resulting in a fully automated workflow for building
energy demand estimation. Moreover, a solar potential analysis
tool was included to improve the energy demand estimation
with more realistic solar energy estimations that include local
climate conditions and the surrounding 3D topography (Fuchs,
2018, Willenborg et al., 2017).
Another example based on CityGML’s EnergyADE is the urban
energy simulation platform SimStadt. It offers a highly modu-
lar and flexible workflow-driven architecture. Its first version
contained workflows for solar and PV potential analysis, en-
ergy demand and CO2emission calculation, and refurbishment
scenario generation and simulation (Nouvel et al., 2015b).
At Ecole Polytechnique F´
ed´
erale de Lausanne (EPFL) a build-
ing energy simulation database using CityGML EnergyADE
was developed, including the integration of outdoor human
comfort, urban micro-climate and its impact on human activit-
ies and well-being (Coccolo et al., 2016, Walter, K¨
ampf, 2015).
Another approach offering dynamic simulation of the heat de-
mand using the simulation framework Modelica was presented
at RWTH Aachen. CityGML data of more than 2,800 buildings
were enriched with statistical data and then converted to a Mod-
elica model for the simulation (Remmen et al., 2016).
One of the few tools which offer simulations on city-scale is
City Building Energy Saver (CityBES), a web-based data and
computing platform, focusing on energy modeling and analysis
of a city’s building stock. CityBES uses CityGML to repres-
ent and exchange 3D city models and employs EnergyPlus to
simulate building energy use and savings from energy efficient
retrofits (Chen et al., 2017).
The Urban Modeling Interface (UMI) (Reinhart et al., 2013),
developed at the Massachusetts Institute of Technology, is one
of the few tools that can perform analyses at the city and neigh-
borhood level, using a life cycle-based approach. However, the
LCA does not go into detail about the TBS.
1.2 Distinction from other work
The existing tools and models are mainly specialized in calcu-
lating the energy demand during the building’s use stage on the
building block or city quarter level. Approaches for LCA do ex-
ist, but only with the help of a large number of building specific
input parameters for the assessment, which make extrapolation
to large existing building stocks difficult. Furthermore, none of
these tools allow for the important life cycle based assessment
of TBS. As described above, semantic 3D city models are used
for urban energy simulations in different ways. Many applica-
tions use the city model data as an input for existing tools based
on Modelica or Energy Plus. However, only a few approaches
use the city model as a data integration platform and export the
simulation results back to the model, which offers various be-
nefits that will be discussed later. Moreover, most applications
are evaluated using rather small data sets that only contain a few
buildings. Bigger studies with several thousands of buildings or
even at the city scale are rare.
2. THEORETICAL BACKGROUND
The method introduced in this work is based on a series of
standards and existing tools, which are briefly listed in the fol-
lowing sections.
2.1 Standards and tools for building energy and LCA
The methodical approach for calculating the energy demand,
heating load and dimensioning of TBS components is based
on the standards and norms listed in the following reference.
In the Method section, the associated standards and norms are
listed again for each defined calculation step (DIN EN ISO
14040:2009-11, 2009, German Federal Ministry of EAE, 2013,
DIN EN 15978:2012-10, 2012, DIN V 4108-6:2003-06, 2003,
DIN V 4701-10:2003-08, 2003, DIN EN 12831-1:2017-09,
2017, DIN V 18599-5:2018-09, 2018, DIN V 18599-8:2018-
09, 2018). For the LCA calculation the ¨
Okobaudat database is
used. With the ¨
Okobaudat platform , the German Federal Min-
istry of the Interior, Building and Community provides stake-
holders with a standardised database for the LCA of buildings.
At the centre of the platform is the online database with LCA
data sets on building materials, construction, transport, energy
and disposal processes. This database is used for all LCA re-
lated calculations in this study (Federal Ministry of IBC, 2020).
2.2 Semantic 3D City Models
Semantic 3D city models describe the spatial, visual, and them-
atic aspects of the most common objects of cities and land-
scapes. The real world objects are decomposed and classified
according to a semantic data model, that represents them in an
ontological structure by thematic classes with attributes includ-
ing their aggregations and interrelations. CityGML is an inter-
national standard by the Open Geospatial Consortium (OGC)
and an open data model and encoding specification for repres-
enting and exchanging semantic 3D city models (Kolbe, 2009,
Open Geospatial Consortium, 2012).
Today, CityGML is widely adopted and offers a good variety
of tools to edit, analyze and visualize 3D city model data, that
increasingly becomes available, in many cases as open data. A
study from 2015 identified more than 29 use cases including
more than 100 applications of 3D city models (Biljecki et al.,
2015). The method and implementation introduced in the fol-
lowing is based on citygml4j1, an open source library reflecting
the CityGML data model to the programming language Java.
Moreover, the SharedWallSurface calculator developed by Sin-
dram for Kaden (Kaden, 2014) and the Voluminator 2.0 (Sin-
dram et al., 2016) are integrated for shared wall surface and
building volume calculation. For creating 3D browser visual-
izations of the city model data and the method results the open
source tool chain of the 3D City Database, the 3D City DB Im-
porter/Exporter and the 3D City DB Web-Map-Client is applied
(Yao et al., 2018).
3. METHOD
The presented method allows the life cycle based energy and
environmental assessment of the TBS of large residential build-
ing stocks. A life cycle based energy and environmental assess-
ment means, in the scope of this study, that the energy demand
1https://www.3dcitydb.org/3dcitydb/citygml4j/
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-85-2020 | © Authors 2020. CC BY 4.0 License.
86

and emissions of the manufacturing (A1-A3), use (B4 & B6)
and end-of-life (EOL) (C2 & C4) stages, according to DIN EN
15978 (DIN EN 15978:2012-10, 2012) of the TBS compon-
ents are calculated and considered for the assessment. These
LCA stages have been selected based on the data available in
the ¨
Okobaudat database, because consistent data is available
for all selected LCA stages across all TBS components con-
sidered. The calculation of further LCA stages can be carried
out analogously to the methodology described here, insofar as
consistent data is available for the new LCA stage for all TBS
components. The implementation of the LCA is based on the
principles defined in DIN 14040 (DIN EN ISO 14040:2009-11,
2009). To achieve this, a methodological approach based on a
few input data and definitions was developed. In the following,
the main steps of the method and related standards are briefly
described. The methodology was implemented in Java to cre-
ate a usable tool. We named our developed tool urbi+ (urban
improvement +) and use this name for further explanations in
the text. In the calculation of large residential building stocks
all energy demand, heating load and LCA calculations are car-
ried out iteratively for each defined scenario for each residential
building to be considered. In turn, this means that theoretically
the life cycle based energy and environmental assessment of in-
dividual buildings can also be carried out using this method.
3.1 Assumptions
Before the individual methodological steps are explained, the
following assumptions are made:
1) The Energy Demand Calculation and Heating Load Calcu-
lation are based on a single zone model. No building specific
floor plan or zoning is used for the calculations, since this in-
formation is generally not available on a large building stock
level.
2) Since the information on the refurbished standard of build-
ings is also generally not available, we assume that the ener-
getic standard of the building, described by U-values, refers to
its year of construction.
3) The energy demand calculation refers to the German Energy
Saving Ordinance (EnEV) and therefore to DIN V 4108-6 and
DIN V 4701-10 (used for energy demand calculation of resid-
ential buildings). Specifically, the heating period procedure is
chosen as an approach within DIN V 4108-6. The length of the
heating period is defined in the norm to 185 days.
4) The CityGML model used must be available in level of de-
tail 2 (LoD2). This means, that the cubature of the building,
including the roof shape, is available, but no information about
windows or internal walls, etc. is given.
5) Underground car parks are excluded from the calculation. It
is assumed that they are not thermally relevant/not heated.
6) In the refurbishment scenario it is assumed that the cubature
of the buildings does not change, i. e. that there is no increase in
height (urban redensification) and that no additional new build-
ings are added. All buildings are assumed to be refurbished
and not demolished and rebuilt. An in depth refurbishment of
buildings has proven to be the more ecological and less energy-
intensive option (Weiler et al., 2017).
7) The refurbishment of wall, ground and roof surfaces is rep-
resented by the improvement of the U-values. When refurbish-
ing the TBS components, it is assumed that all components are
replaced completely.
8) In case of refurbishment of a large building stock, the aver-
age of the energy systems defined for the entire building stock
is used for the iterative calculation at building level. This means
that if 80% of the residential buildings of the considered build-
ing stock are heated with gas boilers and 20% with oil boil-
ers, then exactly this percentage distribution of energy systems
is used for all calculations at building level. Similarly, the re-
spective primary energy factors (PEF) are used to calculate the
primary energy demand (PED).
9) To distribute the refurbishment of all considered residential
buildings over the period of the refurbishment scenario, a se-
quence of refurbishment is defined. The sequence results from a
three-dimensional decision space between the building’s year of
construction, specific primary energy heating demand and ab-
solute primary energy heating demand. The sequence is based
on the fact that the oldest buildings with the highest specific and
absolute heating requirement are refurbished first and are then
graded in a similar way. In addition, the total number of build-
ings to be refurbished is divided by the development period to
obtain the number of buildings to be refurbished per year.
10) The PEF required to calculate the PED can be adjusted over
the development period. The PEF does not change abruptly
in the course of a building refurbishment, but adapts gradually
over time by calculating the difference between the old and new
PEF, distributed over the entire period.
11) When calculating the LCA of the TBS, the buildings that
were refurbished first during the refurbishment period may re-
quire further replacement of components within the develop-
ment period. If, for example, a reference service life (RSL)
of 15 years is assumed for an instantaneous water heater, then
in year 16 of the development period, the water heaters of the
buildings refurbished in year one of the development period are
replaced, insofar as they are installed in those buildings. The
energy demand for the EOL and manufacturing stage of the new
water heaters and the resulting emissions are calculated in LCA
stage B4 and included in the energy balance of the 16th year of
the development period. In the scope of this research the RSLs
are defined based on the sustainable building assessment system
(BNB) (Federal Ministry of IBC, 2019).
3.2 Methodological Approach
Data Collection The overall methodical process starts in
the first step with data collection. This works in two ways:
CityGML files and user input from the graphical user interface
(GUI) of urbi+, as shown in Table 1. The GUI also allows the
Data/Information CityGML GUI
Building geometry (walls, ground, roof) X -
Building year of construction X -
Building usage/function X -
Building roof type X -
Building’s storeys above ground X -
Building’s geographic location (ZIP-Code) X -
Range of year of construction for assessment - X
Percentage of heated top floors - X
Percentage distribution of energy systems - X
Percentage distribution of heat transfer systems - X
RSL of all considered TBS components - X
Primary energy factors of considered energy sources - X
Percentage share (non-)renewable energy of energy sources - X
Table 1. Input data sources for the calculations.
definition of further building, energy sources and TBS specific
data. The data from the CityGML file is building-specific data,
whereas the user input data from the GUI defines average val-
ues for all considered buildings. The average values replace
missing building-specific data (e. g. exact definition of the heat
generator) which in most cases is not available in CityGML data
for large building stocks.
Data Processing and Precalculations All collected data is
either cached or used for precalculations, to have all data avail-
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-85-2020 | © Authors 2020. CC BY 4.0 License.
87

able for the following Energy Demand Calculation and Heating
Load Calculation:
1) All residential buildings to be assessed are selected accord-
ing to their use/function and area from the year of construction.
2) The area of wall, ground and roof surfaces is computed based
on the geometry of the CityGML model.
3) With the information about roof type, percentage of heated
top floors and geometry, the building’s net volume is calculated.
4) The information of storeys above ground is used to calculate
the building’s living area. Therefore, the ground surface area
is multiplied with the number of storeys above and the factor
0.89, according to (Bogenst¨
atter, 2007).
5) Using the SharedWallSurface calculator the external wall
areas, which are ’shared’ between buildings are calculated.
6) Building volumes are calculated using Voluminator, which
allows the building volumes to be determined, even if the build-
ing topology is not correct.
7) The information about the year of construction of the build-
ing is used to sort the building to a specific building age class.
According to the specific class, U-values are sourced from a self
developed and integrated LCA-database and cached for calcu-
lation. Both, the building age classes as well as the U-values
are based on the definitions developed by Loga et al. in the
TABULA project (Loga et al., 2015).
8) The living area and building age class are used to calculate
the approximate window area. As (Heinrich, 2019) defined, ac-
cording to (Diefenbach et al., 2010) and (Loga et al., 2005),
approximate window areas refer to the living area of residential
buildings of different building age classes. These values were
used for the extrapolation. The resulting window area is dis-
tributed evenly over the difference between the total area of all
external walls and the shared wall surface area.
9) Based on the geographical location (ZIP-Code), the min-
imum outside air temperature of the region is taken from the
LCA-database. These temperatures are defined in DIN 12831.
Energy Demand Calculation The energy demand calcula-
tion for heating and domestic hot water (DHW) is based on DIN
V 4108-6 and DIN V 4701-10. Equation 1 describes the first
basic step in energy demand calculation. The transmission heat
losses are calculated using the U-values and areas of all wall,
ground and roof surfaces. The infiltration losses and internal
heat gains are calculated by using the building’s net volume
and multiplying it with a respectively given factor, defined in
DIN V 4108-6. The solar gains are calculated by multiplying
the window area with an average solar irradiation value from all
directions, also according to DIN V 4108-6.
Qu=Ql−ηp(Qs+Qi)(1)
where Qu= useful energy demand [kWh]
Ql= heat losses (transmission + infiltration) [kWh]
ηp= utilization factor [-]
Qs= solar heat gains [kWh]
Qi= internal heat gains [kWh]
In addition, the heat losses as well as the auxiliary energies for
the transfer of heat into the room and for heat storage and dis-
tribution are added to the useful energy demand. The resulting
sum is then multiplied by the product of the heat generator cost
figure and the degree of coverage for heating, both defined in
DIN V 4701-10. The resulting value is then multiplied by the
PEF. This finally results in the PED of the building. For the PED
calculation of DHW a useful energy demand of 12.5 kWh/m2*a
is assumed as the initial value (according to DIN V 4701-10).
The procedure for calculating the primary energy requirement
for DHW is the same as for heating. The time component of the
heating period of 185 days is taken into account for all calcula-
tions.
Heating Load Calculation The heating load calculation
refers to DIN 12831. Equation 2 shows the main considered
variables. The calculation refers to the minimum temperature of
the year. This ensures that the calculated heating load for satis-
fying the heating energy requirement is guaranteed even on the
coldest days. Multiplied by the average efficiency, which res-
ults from the percentage share of efficiency of the energy sys-
tem distribution at building stock level, this gives the required
heating load to be covered.
ΦHL,build=X(ΦT,i)+ΦV,build+X(Φhu,i)−X(Φgain,i )(2)
where ΦHL,build = heating load building [kW]
ΦT,i = transmission heat losses [kW]
ΦV,build = infiltration heat losses [kW]
Φhu,i = heating capacity [kW]
Φgain,i = heat gains [kW]
Dimensioning TBS components All considered TBS com-
ponents are dimensioned on the basis of different assumptions
and norms, which are briefly presented in the following.
The dimensioning or required power of the heat generator is
derived from the rounded value of the calculated heating load
for heating and DHW multiplied by the efficiency of the con-
sidered heat generator. The dimensioning of the oil tank results
from dividing the building specific PED by the energy content
of heating oil. The energy content of the heating oil is assumed
to be 10 kWh/l.
The length of earth probes is calculated by dividing the heat
pump extraction capacity by the specific extraction capacity.
The heat pump heat extraction rate is calculated from the heat-
ing load of the building and the coefficient of performance
(COP) of the heat pump. The COP is assumed to have an aver-
age value of 3.1. For the specific extraction capacity, an average
value of 50 W/m across all types of flooring is assumed (see
guideline VDI 4640 (VDI, 2019)). The length of the earth col-
lector and the depth of the water well is calculated according
to the power of the associated sole-water, or water-water heat
pump, based on technical specification in LCA-datasets of the
¨
Okobaudat.
The heating load of the building divided by the average heat
output of a radiator results in the length of radiators required to
transfer the heat output into the rooms. On the basis of further
assumptions regarding average length, height, width and mater-
ial, the material mass of the radiators can be calculated. When
dimensioning the floor heating pipes, the installation distance
of the pipes is decisive. A distinction is made between 100 and
200mm. According to this, the heat output transferred into the
room can be calculated. Based on the installation distance and
the living area, the material mass of the floor heating pipes can
also be calculated. These calculations are also based on tech-
nical specification in ¨
Okobaudat LCA-datasets.
Four different pipe systems are used to calculate the pipe
lengths for heating. A distinction is made between floor ring
type, floor distributor type radiators, floor distributor type, riser
pipe type (average values multiplied by the defined share of ra-
diators via GUI) and floor heating (multiplied by a defined share
of floor heating via GUI). The respective pipe lengths are cal-
culated by multiplying the living area with specific fixed calcu-
lation parameters according to DIN V 18599-5. Three different
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-85-2020 | © Authors 2020. CC BY 4.0 License.
88

piping systems are used to calculate the pipe lengths for DHW.
A distinction is made between decentralized supply (multiplied
by a defined share of instantaneous water and gas storage heat-
ers), riser line type and level type (multiplied by the remaining
proportion of DHW systems). The respective pipe lengths are
calculated by multiplying the living area with specific fixed cal-
culation parameters according to DIN V 18599-8.
The material volume of the pipe insulation for heating and
DHW pipes is calculated using the formula for the volume of
a hollow cylinder. The diameters used for the calculation are
derived from the previously calculated average pipe diameter
and the insulation standard, based on the energy standard after
refurbishment.
The dimensioning of heat storage tanks for heating water is cal-
culated according to DIN V 18599-5, whereby the daily standby
heat loss results from DIN 4701-10.
The dimensioning of heat storage tanks for DHW is calculated
according to DIN V 18599-8. The standard defines assumptions
for the daily energy demand for hot water, the usage factor and
cold and hot water temperatures required for the calculation.
The solar collector area is calculated according to (SBZ Mon-
teur, 2020). An average value is calculated for vacuum tube
and flat plate collectors. The values for the average German ir-
radiation intensity are sourced from the German meteorological
service (DWD, 2020).
Life Cycle Assessment In the LCA, the grey energy and
emissions of the TBS components under consideration are then
calculated and combined with the energy demand of the use
stage and the resulting emissions. The calculated dimensions
of the TBS components are offset against the LCA datasets.
The LCA data sets from the LCA database naturally refer to the
same unit that results from the dimensioning of the TBS com-
ponents. For each TBS component and LCA stage considered, a
specific average value calculated on the basis of the ¨
Okobaudat
database is stored in the LCA database. Equation 3 shows the
exemplary calculation of the embedded PED for the LCA stages
production, EOL and recycling/reuse of the heat generator.
P Egrey, hg=XP E A1-A3, C2, C4, D, hg ×Pheat generator (3)
where P Egrey, heat generator = grey primary energy [kWh]
P EA1-A3, C2, C4, D = LCA-values [kWh/kW]
Pheat generator = power of heat generator [kW]
Result data preparation All relevant calculated (intermedi-
ate) results are exported back to an Excel file as well as to the
original CityGML file. The values are appended to each build-
ing object as newly defined generic attributes. This way they
are persistently stored in the model and can be used for visualiz-
ation purposes and any further investigations and analyses. For
visualization appearances of the data are defined using a color
scale for the buildings according to their energy demand. For
this purpose we have set up a Github repository2, with a link to
a 3D Web Map Client project, where the results can be viewed
in the browser. The coloring of all buildings is set according to
their specific PED [kWh/m2a] from red to green for the status
quo and a renovation or development scenario.
4. VALIDATION
The methodological block for energy demand calculation has
already been validated by comparing the results from energy
2https://github.com/tum-gis/LCA-TGA
demand calculations at the level of individual buildings from
urbi+ with results from already established building simulation
software IDA ICE (EQUA Solutions AG, 2020) and the already
mentioned tool UMI. As the results differed only slightly, first
results were calculated for a ’small’ district with about 300 res-
idential buildings. Again, the calculation results have shown
correct trends. However, the investigations have shown that
different assumptions and definitions have to be made for the
investigations at building stock level than for the calculation at
individual building level (Harter et al., 2020).
5. CASE STUDY MUNICH
The case study used to apply the method consists of 115,305
residential buildings in Munich, Germany. This covers 81% of
all residential buildings, when comparing the number of resid-
ential buildings of 2018 published by Statista (Statista, 2019).
The discrepancy in the number of residential buildings is caused
by different data sources and data pre-processing steps. While
the CityGML LoD2 data where provided by the Bavarian State
Office for Digitization, Broadband and Surveying3(Project:
Geomassendaten), all other building-specific parameters (e. g.
year of construction) where provided by the Planning Depart-
ment of the Bavarian State Capital Munich4. The matching of
data was carried out by using a geometric intersection opera-
tion between the building ground surfaces and house coordin-
ates with statistical values attached as attributes, which resulted
in losing some unmatched buildings. In addition, the paramet-
ers required for calculations were not available for all buildings,
which also resulted in the exclusion of further buildings. The
following average values resulting from the Data Processing
and Precalculations characterize the building stock under in-
vestigation.
5.1 Definitions
1) Average number of floors is 2.6. (min: 1, max: 29). Since
the citizens’ decision in 2004, no buildings taller than 100m are
allowed to be built in Munich, the tallest residential building
has 29 floors.
2) Average living space per building 415m2, i. e. average 130m2
living space per floor.
3) Mean building volume 1,522m3, i. e. mean value 499m3
volume per floor (without consideration of interior walls and
intermediate ceilings).
These data seem consistent when comparing the distribution of
buildings over the age classes in Figure 2 with the distribution
of building volumes over their living area in Figure 1. Figure 2
shows that about 35% of the residential buildings were con-
structed before 1957 and that building age class 4 (bac 4) is the
most strongly represented age class with about 21%. This is
likely due to the fact that the shortage of materials caused by
the Second World War was slowly overcome at the beginning
of the bac 4. Figure 1 shows a correlation between building
volume and living area. In addition, the distribution shows that
buildings with a low building age class, i. e. older buildings,
have a larger volume per square meter of living area. That is
plausible, because in older buildings, high ceilings and fewer
floors were preferred. The consideration of the distribution of
the building age classes in the context of the distribution of the
3https://www.ldbv.bayern.de/
4https://www.muenchen.de/rathaus/Stadtverwaltung/Referat-fuer-
Stadtplanung-und-Bauordnung.html
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-85-2020 | © Authors 2020. CC BY 4.0 License.
89

Figure 1. Distribution of building volume vs. living area. 147
buildings with volume >20,000 m3, living area >7,500 m2
filtered out for better visibility. The range of plausible floor
heights (2.5−4.5m) lies between the two black lines.
Figure 2. Distribution of buildings over building age classes. No
filtering as in Figure 1 applied.
ratios between building volume and living area as well as the
already mentioned average values thus result in a coherent pic-
ture. However, the number of buildings with ceiling heights of
more than 4.5 meters has to be examined more closely for fu-
ture investigations, as there may be discrepancies between the
number of floors and the building height between the data of the
Bavarian State Office for Digitization, Broadband and Survey-
ing and the Planning Department of the Bavarian State Capital
Munich (different up-to-dateness of the data).
5.2 Parameter Definition for Simulation
In order to carry out a life cycle based assessment of the build-
ing stock for the status quo and two refurbishment scenarios,
the input parameters for the simulations must be defined first.
The development period of both refurbishment scenarios is set
to 30 years, which means that until 2050 all residential build-
ings are refurbished to defined values. The definitions used for
the calculation are based on assumptions that serve to explain
the methodological approach (status quo = scenario 0; develop-
ment scenario 1; development scenario 2):
1) percentage heated top floors: 20%; 80%; 80%
2) DHW: 20% gas, 40% oil, 10% gas-storage heaters, 10% elec-
tric flow heaters; 20% gas, 30% heat pumps (hp), 40% solar,
10% district heating; 30% solar, 70% district heating.
3) heating: 80% gas, 20% oil; 20% gas, 40% air-water hp, 5%
hp earth collector, 5% hp earth probe, 20% water-water hp, 10%
Figure 3. Comparison of absolute and specific PED for the three
scenarios.
biomass; 100% district heating
4) heat transfer system: 100% radiators; radiators 50%, floor
heating 50%; floor heating 100%
5) U-values [W/m2K]: according to bac (see Subsection 3.2);
wall: 0.24, window: 1.3, roof: 0.24, base plate: 0.35; wall:
0.15, window: 0.85, roof: 0.15, base plate: 0.3
6) g-value Window [-]: according to bac; 0.6; 0.5
7) PEFs: gas: 1.1, oil: 1.1; gas: 1.1, environmental heat and
solar radiation: 0.0, biomass: 0.2, electricity: 0.5, district heat-
ing: 0.5; solar radiation: 0.0, district heating: 0.1.
8) reference service life [years]: gas-boiler: 20, oil-boiler 20,
gas-storage heaters: 20, instantaneous flow heaters: 15, DHW
and heating pipes: 50, pipe insulation: 25, heat storage tanks:
25, radiators: 50; gas-boiler: 20, all heat-pumps: 20, biomass-
boiler 20, earth probes and collectors: 50, DHW and heating
pipes: 50, pipe insulation: 25, heat storage tanks: 25, radiators
and floor heating: 50; district heating connection station: 20,
floor heating: 50, DHW and heating pipes: 50, pipe insulation:
25, heat storage tanks: 25.
9) share of renewable and non-renewable energy of energy
sources: gas: 10% / 90%, oil: 0% / 100%; gas: 30% / 70%,
electricity: 50%, 50%, environmental heat and solar radiation:
100%, 0%, biomass: 100%, 0%; district heating: 90%, 10%,
solar radiation: 100%.
5.3 Results
Figure 3 shows the improvement of the absolute and specific
PED of the buildings’ use stage through the refurbishment of
the entire building stock in the two scenarios (scenario 1 and
2) on a logarithmic scale. The results of the status quo (scen-
ario 0) are shown next to it as a reference value. It can be seen
that the fluctuation range of the values changes according to the
ratio of the numbers within a scenario and that a significant re-
duction can be achieved for both the absolute and the specific
PED (−96% &−99%). The mean value of the specific PED
has fallen from 238 to 11, or 3 kWh/m2a and the mean value of
the absolute PED from 6,5377 to 2,823, or 361 kWh/a. Among
other things, this is due to the very strongly improving primary
energy factors (PEFs). Table 2 shows the changes of the dif-
ferent TBS component dimensions in the different scenarios,
referring always to the average values over all buildings. Due
to the reduction of the heating load and the use of efficient and
modern heat generator technologies and systems, the connec-
ted load of the heat generators has been reduced from 43 kW
to 14 kW and 11 kW respectively. Oil tanks are only existing
for the status quo and account for an average volume of 2,018
liters. DHW’s storage accounts for around 95% of hot water
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-85-2020 | © Authors 2020. CC BY 4.0 License.
90

storage (heating & DHW). The basic need for DHW does not
change with a building refurbishment, only the efficiency and
the type of technical connection of the systems that produce the
DHW. Because of this, the average value only decreases slightly
over the scenarios. Solar systems for DHW exist only in scen-
ario 1 & 2. The number of square meters is adjusted mainly
on the basis of the defined degree of coverage for the genera-
tion of DHW from solar systems. The earth collectors, probes
and pipes for the sole-water and water-water heat pumps only
appear in scenario 1. The earth collectors are logically much
longer than the probes. The mass of the pipes of the water-water
heat pump is calculated based on the calculation of the LCA. By
adapting the heat transfer through radiators, their mass portion
is reduced when comparing status quo with scenario 1. The op-
posite is the case when calculating the length of the floor heat-
ing pipes. Both are caused by the before mentioned definition
of the heat transfer system. The length of DHW pipes, given in
meters per square meter of living area, increases in comparison.
This is due to the fact that in the status quo more decentralized
energy systems were used for DHW provision, which require
significantly shorter pipe lengths and are installed downstream
at the tap. With the switch to more decentralized energy sys-
tems for DHW generation, the average pipe length increases.
The length of heating pipes, also given in meters per square
meter of living area, is the exact opposite. This is due to the
fact that in the status quo, 100% of the heating is provided by
radiators and therefore pipes have to be added to each radiator
installed. With the increasing use of floor heating systems, the
average pipe length decreases, as pipes only have to be laid per
heated room. The pipe insulation adapts to the sum of these
two values and is expressed in cubic meters per meter of pipe
length. The embedded energy and emissions of the manufac-
TBS component Status quo Scenario 1Scenario 2
heat generator [kW] 43 14 11
oil tank [l] 2,018 - -
heat storage tank [l] 469 457 453
solar [m2]- 1.68 10.12
earth collector [m] - 34.2
earth probe [m] - 9.3 -
pipes water-water hp [kg] - 4.11 -
radiators [kg] 1,489 349 -
pipes floor heating [m/m2]- 2.17 1.33
pipes DHW [m/m2]2.9 3.2 3.2
pipes heating [m/m2]3.62 3.33 3.03
pipes insulation[m3/m] 0.0023 0.0022 0.0011
Table 2. Dimensions TBS components.
turing, use and EOL stage are calculated on the basis of the
building-specific dimensions of the TBS components. If these
values are combined with those of the use stage, i. e. for the
operation of the building, and considered over the course of the
year over the development period of the refurbishment scen-
arios, the results are as shown in Figure 4. Over the course
of 30 years, around 3,843 residential buildings are refurbished
each year, so that by 2051 all buildings have been refurbished.
The graph clearly shows the significant reduction in the total
PED (heating tpe h and DHW tpe dhw) of all building-specific
use stages as a result of the refurbishment. In addition, the total
amount of embedded energy tpe e for the EOL of old systems
to be disposed in the course of refurbishment and manufactur-
ing of the new systems is also listed. It can be seen that the
course is much steeper in the first years and then makes a small
bend around the year 2040. This is because the selection of
buildings described in chapter 3.1 will first refurbish all the old
buildings with a high specific and absolute PED. As soon as
Figure 4. Comparison PED (TPE H + TPE DHW) with the
embedded PED (TPE E) for both development scenarios.
most of these buildings have been refurbished, the course will
flatten out. The small bend around the year 2040 results from
the fact that the TBS components of the buildings which have
been renovated in the first years already have to be replaced, be-
cause they have reached the end of their RSL. A closer look at
the figures of scenario 1 & 2 shows that in the third year of re-
furbishment, i. e. 2023, the investment in embedded energy will
have amortized by comparison with the savings achieved. This
will not change over the further course of the refurbishment
scenarios. However, it must be noted that embedded energy is
not taken into account for the renovation of the building envel-
ope which must be investigated in following research. Even if
there is a significant reduction in the energy demand through
the ambitious renovation of all residential buildings to a high
energy standard and with efficient technologies in the two scen-
arios, a PED of around 325.5 million (m) kWh in scenario 1 and
41.5 m kWh in scenario 2 still remains. This results in around
255.4 m kg CO2-eq./a for scenario 1 and 71.4 m kg CO2-eq./a
for scenario 2 (calculated on the basis of ¨
Okobaudat-database
values) and this does not yet include embedded energy for the
building construction.
6. DISCUSSION
The case study shows, that the introduced method can be ap-
plied to large building stocks by using semantic 3D city mod-
els. Due to the standardization (CityGML) of the city model
the approach can easily be applied to other cities. In general,
it can be seen that if we assume that all residential buildings
will be refurbished to an ambitious energy standard, there will
be a significant reduction in PED and emissions. However, it
is very difficult to achieve a climate-neutral building stock, es-
pecially when including the embedded energy resulting from
LCA. Even if the PED can be reduced by using renewable en-
ergy technologies, PEDs and emissions are still generated in the
manufacturing, use and EOL stage. With regard to the refur-
bishment of the TBS, however, it can be said that, at least in the
scenarios considered, a rapid amortisation of the embedded en-
ergies has been achieved. In order to focus more strongly on the
use of solar thermal energy and photovoltaics, it is planned to
integrate an already developed solar tool into the framework of
urbi+. In the course of a refurbishment, not only the TBS com-
ponents but also the building envelope will be refurbished, thus
the LCA of the building construction will be integrated in future
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-85-2020 | © Authors 2020. CC BY 4.0 License.
91

work. Experience has shown that a large proportion of embed-
ded energy and emissions are generated in this process. Apart
from this, the life cycle cost (LCC) assessment plays an import-
ant role in construction projects. Especially with regard to the
upcoming pricing of CO2-emissions, it makes sense to integrate
LCC into urbi+. A big advantage of the introduced method is
that the results are fed back to the CityGML building objects as
generic attributes. This way, the result figures are persistently
stored in the city model linked to the specific real world objects
they belong to and are thus available for subsequent analysis
including other data source or software. One example of this
approach can be found in the work of (Fuchs, 2018), where ref-
erence values for solar heat gains in the heat demand calculation
have been replaced with results of the solar potential analysis
tool described in (Willenborg et al., 2017). By using the more
detailed results of the solar tool including the 3D topography
surrounding the buildings, the accuracy of heat demand calcu-
lation could be improved significantly. Moreover, the results
can easily be visualized using the 3DCityDB WebMapClient.
ACKNOWLEDGEMENTS
The authors would like to thank Ordnance Survey GB
(www.ordnancesurvey.co.uk) and 1Spatial (www.1spatial.com)
for sponsoring the publication of this paper.
REFERENCES
Biljecki, F., Stoter, J., Ledoux, H., Zlatanova, S., C¸ ¨
oltekin, A., 2015.
Applications of 3D City Models: State of the Art Review. ISPRS Interna-
tional Journal of Geo-Information, 4(4), 2842–2889.
Bogenst¨
atter, U., 2007. Fl¨
achen- und Raumkennzahlen.
www.ifbor.eu/3.html. Online; accessed 2020-02-12.
Cabeza, L. F., Rinc´
on, L., Vilari˜
no, V., P´
erez, G., Castell, A., 2014. Life
cycle assessment (LCA) and life cycle energy analysis (LCEA) of build-
ings and the building sector: A review. Renewable and Sustainable En-
ergy Reviews, 29, 394–416.
Chen, Y., Hong, T., Piette, M. A., 2017. Automatic generation and simula-
tion of urban building energy models based on city datasets for city-scale
building retrofit analysis. Applied Energy, 205, 323–335.
Coccolo, S., Mauree, D., K¨
ampf, J., Scartezzini, J.-L., 2016. Integration
of outdoor human comfort in a building energy simulation database using
CityGML Energy Ade. SBE Regional Conference Zurich.
Diefenbach, N., Cischinsky, H., Rodenfels, M., Clausnitzer, K.-D., 2010.
Datenbasis Geb¨
audebestand. Datenerhebung zur energetischen Qualit¨
at
und zu den Modernisierungstrends im deutschen Wohngeb¨
audebestand.
Institut Wohnen und Umwelt (IWU). Darmstadt.
DIN EN 12831-1:2017-09, 2017. Energy performance of buildings -
Method for calculation of the design heat load - Part 1.
DIN EN 15978:2012-10, 2012. Sustainability of construction works - As-
sessment of environmental performance of buildings.
DIN EN ISO 14040:2009-11, 2009. Environmentalmanagement - Life
cycle assessment - Principles and framework.
DIN V 18599-5:2018-09, 2018. Energy efficiency of buildings - Calcu-
lation of the net, final and primary energy demand for heating, cooling,
ventilation, domestic hot water and lighting - Part 5.
DIN V 18599-8:2018-09, 2018. Energy efficiency of buildings - Calcu-
lation of the net, final and primary energy demand for heating, cooling,
ventilation, domestic hot water and lighting - Part 8.
DIN V 4108-6:2003-06, 2003. Thermal protection and energy economy
in buildings – Part 6.
DIN V 4701-10:2003-08, 2003. Energy efficiency of heating and ventila-
tion systems in buildings – Part 10.
DWD, 2020. Deutscher Wetterdienst - Globalstrahlungskarten.
www.dwd.de/DE/leistungen/solarenergie/lstrahlungskarten mi.html.
Online; accessed 2020-04-11.
EQUA Solutions AG, 2020. IDA ICE - Simulation Software — EQUA.
https://www.equa.se/de/ida-ice. Online; accessed 2020-04-02.
EU Commission, 2018. A Clean Planet for all - A European strategic
long-term vision for a prosperous, modern, competitive and climate neut-
ral economy. Technical report, European Commission, Brussels.
Federal Ministry of IBC, 2019. Federal Ministry of Interior Building and
Community (IBC) - Sustainable Building Assessment System (BNB).
https://www.bnb-nachhaltigesbauen.de/. Online; accessed 2019-08-28.
Federal Ministry of IBC, 2020. Federal Ministry of Interior Building
and Community (IBC) - ¨
OKOBAUDAT. www.oekobaudat.de/. Online;
accessed 2020-04-05.
Fuchs, D., 2018. Berechnung des Energiebedarfs von Wohngeb¨
auden auf
Basis von semantischen 3D-Stadtmodellen am Beispiel der Gemeinde Po-
ing. Bachelorarbeit, Technische Universit¨
at M¨
unchen, M¨
unchen.
German Federal Ministry of EAE, 2013. German Federal Ministry of
Economy Affairs and Energy (EAE) - Energy Saving Ordinance (EnEV).
Harter, H., Banihashemi, F., Kierdorf, D., Vollmer, M., Lang, W., 2020.
Comparison of different energy demand calculation models on urban
scale. (Accepted at BauSim2020 Conference in Graz).
Heinrich, M. A., 2019. Erfassung und Steuerung von Stoffstr¨
omen im
urbanen Wohnungsbau. Dissertation, Technical University of Munich.
Kaden, R., 2014. Berechnung der Energiebedarfe von Wohngeb¨
auden
und Modellierung energiebezogener Kennwerte auf der Basis se-
mantischer 3D-Stadtmodelle. Dissertation, TU M¨
unchen.
Kolbe, T. H., 2009. Representing and Exchanging 3D City Models with
CityGML. J. Lee, S. Zlatanova (eds), 3D Geo-Information Sciences, Lec-
ture Notes in Geoinformation and Cartography, Springer Berlin Heidel-
berg, Berlin, Heidelberg, 15–31.
Loga, T., Diefenbach, N., Knissel, J., Born, R., 2005. Entwicklung
eines vereinfachten, statistisch abgesicherten Verfahrens zur Erhebung
von Geb¨
audedaten f¨
ur die Erstellung des Energieprofils von Geb¨
auden.
Kurzverfahren Energieprofil.
Loga, T., Stein, B., Diefenbach, N., Born, R., 2015. Deutsche
Wohngeb¨
audetypologie - Beispielhafte Maßnahmen zur Verbesserung der
Energieeffizienz von typischen Wohngeb¨
auden. IWU, Darmstadt.
Nouvel, R., Bahu, J.-M., Kaden, R., Kaempf, J., Cipriano, P., Lauster, M.,
H¨
afele, K.-H., Munoz, E., Tournaire, O., Casper, E., 2015a. Development
of the citygml application domain extension energy for urban energy sim-
ulation. Building Simulation 2015 - 14th IBPSA Conference, India.
Nouvel, R., Brassel, K.-H., Bruse, M., Duminil, E., Coors, V., Eicker,
U., Robinson, D., 2015b. SimStadt, a new workflow-driven urban energy
simulation platform for CityGML city models. Proceedings of Interna-
tional Conference CISBAT 2015, LESO-PB, EPFL, 889–894.
Open Geospatial Consortium, 2012. OGC City Geography Markup Lan-
guage (CityGML) Encoding Standard 2.0.
Reinhart, C., Dogan, T., Jakubiec, A., Rakha, T., Sang, A., 2013. UMI
- an urban simulation environment for building energy use, daylight and
walkability. 13th IBPSA Conference, Chamb´
ery, France.
Remmen, P., Lauster, M., Mans, M., Osterhage, T., M¨
uller, D., 2016.
CityGML import and export for dynamic building performance simula-
tion in modelica. Proceedings of BSO Conference, Newcastle University.
SBZ Monteur, 2020. Wirkungsgrad von Solaranlagen. www.sbz-
monteur.de/gut-zu-wissen/wirkungsgrad-von-solaranlagen. Online; ac-
cessed 2020-04-11.
Sindram, M., Machl, T., Steuer, H., P¨
ultz, M., Kolbe, T. H., 2016. Volu-
minator 2.0 - Speeding up the Approximation of the Volume of Defective
3D Building Models. ISPRS Annals, III-2.
Statista, 2019. Wohngeb¨
aude in M¨
unchen - Bestand bis 2018.
https://de.statista.com/statistik/daten/studie/260455/umfrage/bestand-an-
wohngebaeuden-in-muenchen. Online; accessed 2020-04-15.
UN Environment and IAE, 2017. Towards a zero-emission, efficient, and
resilient buildings and construction sector.
VDI, 2019. Verein Deutscher Ingenieure (VDI) - Thermal use of the un-
derground Ground source heat pump systems VDI 4640 Blatt 2.
Walter, E., K ¨
ampf, J., 2015. A verification of CitySim results using the
BESTEST and monitored consumption values. Proceedings of 2nd BSA
Conference.
Weiler, V., Harter, H., Eicker, U., 2017. Life cycle assessment of build-
ings and city quarters comparing demolition and reconstruction with re-
furbishment. Energy and Buildings, 134.
Willenborg, B., Sindram, M., Kolbe, T. H., 2017. Applications of 3d city
models for a better understanding of the built environment. M. Behnisch,
G. Meinel (eds), Trends in Spatial Analysis and Modelling, Geotechno-
logies and the Environment, Springer International Publishing, Berlin,
Heidelberg, 167–191.
Yao, Z., Nagel, C., Kunde, F., Hudra, G., Willkomm, P., Donaubauer, A.,
Adolphi, T., Kolbe, T. H., 2018. 3DCityDB - a 3D geodatabase solution
for the management, analysis, and visualization of semantic 3D city mod-
els based on CityGML. Open Geospatial Data, Software and Standards.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume VI-4/W1-2020, 2020
3rd BIM/GIS Integration Workshop and 15th 3D GeoInfo Conference, 7–11 September 2020, London, UK
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-85-2020 | © Authors 2020. CC BY 4.0 License.
92