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This paper describes a use case specific modification to the CityGML Energy Application Domain Extension (ADE) version 2.0. The application use cases considered for this paper are: (i) district level economic and ecological assessment of energy flows and self-sufficiency, (ii) life-cycle assessments (LCA) of climate related emissions and costs and (iii) validation of energy simulation models and results. For this purpose, the development of an extended schema for the Energy ADE is discussed. A brief explanation of the implementation methodology is also described in this paper.
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Proposed Integration of Utilities in the Energy ADE 2.0
Maximilian Schildt1, Christian Behm1, Avichal Malhotra1, Sebastian Weck-Ponten1,
Jérôme Frisch1, Christoph van Treeck1
1Institute of Energy Efficiency and Sustainable Building (E3D), RWTH Aachen University,
Germany
Abstract
This paper describes a use case specific modification to
the CityGML Energy Application Domain Extension
(ADE) version 2.0. The application use cases considered
for this paper are: (i) district level economic and
ecological assessment of energy flows and self-
sufficiency, (ii) life-cycle assessments (LCA) of climate
related emissions and costs and (iii) validation of energy
simulation models and results. For this purpose, the
development of an extended schema for the Energy ADE
is discussed. A brief explanation of the implementation
methodology is also described in this paper.
Key Innovations
Use case specific extended schema of the Energy ADE
UML based modular development
Practical Implications
The extended schema of the Energy ADE will help
simulation scientists and urban planners to integrate
simplified models of utilities into CityGML data models
for time-efficient district level analyses and simulations
employing a simplified data structure.
Introduction
With ever rising energy demands throughout the globe,
building simulation tools are gaining popularity between
urban planners, simulation scientists as well as in the
scientific research domains. As buildings are one of the
main energy consumers in cities, the amount of
information required for urban scale energetic analysis is
also high (Braun et al. 2018). Moreover, urban scale
energy performance simulations do require virtual data
models to represent cities and districts. However, a lack
of detailed semantic and topological information of these
data models exists within the community. In order to fill
the gap of representing real buildings in an urban context,
City Geographical Markup Language (CityGML)
(Gröger et al. 2012) is being extensively used for
information exchange, analysis and simulations. The
geometrical information available in CityGML datasets
along with the ADEs can be used for different use cases.
For energy performance and network simulations, the
Energy ADE (Agugiaro et al. 2018) and the Utility
Network ADE (Kutzner and Kolbe 2016) can be used
respectively. Within the scope of this paper, the authors
would like to propose an extension of the different classes
of the Energy ADE 2.0 standard with respect to use cases
in the fields of urban-scale energy flow determination, life
cycle analysis and simulation models and results
validation. Furthermore, a simplified integration of the
utilities is also explained in this paper. Comparing to the
computational requirements of using the Energy ADE and
the Utility Network ADE together, the proposed
extension will help the simulation community to reduce
the required resources and facilitate network integration
in urban scale simulations while reducing access time.
Furthermore, the proposed schema will assist in the setup
of urban-scale life cycle analysis for the determination of
a district’s carbon footprint. The extension is written in
the unified modeling language (UML) and the proposed
schema alterations employ its conventional symbols.
(UML Class Diagrams 2017)
This paper is structured as follows: The “CityGML and
ADE” section briefly describes the CityGML standard.
Within the same section, the different CityGML
Application Domain Extensions (ADE) are described.
The “Use Case Description” section introduces the use
cases that are considered for extending the Energy ADE
1.0. Furthermore, the “Implementation of the new ADE
schema” highlights the new classes and additions made to
the previously existing schema before moving to the
“Conclusion”.
CityGML and ADE
CityGML, an open XML based format, enables
geographical information system (GIS) modellers to
represent urban areas into 3D virtual city models. Capable
of storing semantic and topological information of
individual buildings, CityGML can also be used for the
calculation of energy demands using different simulation
environments (Braun et al. 2018). Remmen et al. 2017
and Coors et al. 2014 demonstrated the application of
CityGML data models for energy demand simulations
using Dymola (Dassault Systems 2016) and EnergyPlus
(NREL 2019), respectively. Several tools such as
CityATB (Malhotra et al. 2020), CityDoctor
(HFTStuttgart 2013), FZK Viewer (KIT 2019), etc. also
facilitate the analysis, repair and visualization of 3D city
models. Based on the contained information or the
detailing of the data models, CityGML datasets are
available in five Levels of Detail (LoD 0-4) (Gröger et al.
2012). Moreover, the definition of the CityGML LoDs
differs from the level of coarseness of a building’s thermal
zone. Starting from a 2D non vertical polygon, LoD0
depicts the building footprint, semantic information and a
parameter indicating the building’s height. LoD1
represents the building in object blocks as generalized
features of the cities. Furthermore, LoD2 also contains the
differentiated roof surfaces of the individual buildings.
LoD3 explicitly defines the windows and exterior
installations for the individual buildings. Lastly, LoD4
also depicts the interior furniture of the buildings. Table 1
gives an overview of the five LoDs in CityGML.
Table 1: An overview of the CityGML LoDs Information
retrieved from Malhotra et al. 2019
Overview of LoD concept in CityGML (LoD0 – LoD4)
LOD0
(2.5D model definition)
Regional and landscape scale representation
Lowest accuracy
No information over building installations
Information about roof representations available
No information about city furniture
LOD1
City and region scale representation
5/5m 3D point accuracy (Low)
Object blocks as generalised features (>6*6m/3m)
Flat roof structures
No information over building installations and c ity
furniture
LOD2
City, districts and project scale representation
2/2m 3D point accuracy (Middle)
Objects as generalised features (>4*4m/2m)
Differentiated roof representations
Information over building installations available
City furniture as prototypes, generalized objects
LoD3
City districts, exterior architectural models, landmark
scale representation
0.5/0.5m 3D point accuracy (High)
Object as real features (>2*2m/1m)
Building installations as exterior features
LOD4
Interior architectural models, landmark
representation
0.2/0.2m 3D point accuracy (Very High)
Constructive elements and openings are represented
Real object forms for building Installations, roof
structures and city furniture
Based on the application use case, CityGML data models
can also be extended using an ADE (Gröger et al. 2012),
which allows the CityGML core geometrical models to
store and exchange application-based information. In
context of urban energy simulations, two prominent
ADEs, the Energy ADE (Agugiaro et al. 2018) and Utility
Network ADE (Kutzner and Kolbe 2016) are generally
used.
The Energy ADE provides a standard to overcome the
data interoperability issues as well as to facilitate single
building and city wide energy simulations (Agugiaro et al.
2018). With the purpose to extend the CityGML
buildings, the modular structure of the Energy ADE
(based on version 1.0) can extend individual buildings
with 6 basic modules.
Energy ADE Core: It defines a number of base classes
and data types. The core module extends the
CityObject and AbstractBuilding of CityGML. It also
contains code lists and some enumerations.
Building Physics (BP): This defines the detailed
physical properties of the individual buildings. Using
the BP classes, the buildings can also be modelled
with multiple zones. Individual thermal zones are
further defined by the thermal boundaries as well as
thermal openings of the buildings.
Energy Systems (ES): This module can be used to
represent the energy distribution, conversion and
storage devices of a building or district. The ES
module also enables the storage of information about
each individual device, its connections, rated power
and model type in a hierarchical network.
Occupant behaviour (OB): The OB module
represents the occupants of individual buildings and
their behaviour. This includes heating/cooling
schedules, occupancy rate, etc.
Material and construction (MC): The MC defines the
construction and optical properties of the buildings
and its components. It includes the attributes such the
U-values, material conductivity, density, etc.
Supporting classes (SC): The SC modules supports
the different time series, schedules and also the
storage of the weather data associated to a building or
an urban area.
The usage of the CityGML Energy ADE has been
demonstrated in some of the European and Non-European
pilot projects (Agugiaro et al. 2018). Furthermore, for
research and simulations as well, the usage of the Energy
ADE can also be seen in Braun et al. 2018, Geiger et al.
2018, Nouvel et al. 2015 and Malhotra et al. 2019. While
the Energy ADE 1.0 enables the extension of city models
with energy related attributes, the Utility Network ADE
focusses on topographically and semantically
representing the aggregation of network features in a city
(Kutzner and Kolbe 2016). As stated in (Agugiaro et al.
2018), there exists some overlaps in the area of interests
between the Energy ADE and the Utility Network ADE
working groups which can be seen e.g. in the connection
of the individual buildings with the grid or the power
generation plants. Figure 1 gives a schematic composition
and structuring of networks in the context of power supply
used in the Utility Network ADE.
Figure 1: Composition and hierarchical structuring of
networks in the context of power supply. Image source:
(Kutzner and Kolbe 2016)
In the last years, the development of the Energy ADE has
undergone many modifications and additions. In the latest
version of the Energy ADE 2.0, the energy systems
module has been removed due to missing support (OGC
and Sig3D 2019). However, within the scope of this
paper, the authors would like to propose the integration of
the ES module along with use case based extensions to
some other classes of the Energy ADE 2.0. This will,
therefore, increase the time efficiency and usability.
Use Case Description
This section describes the different use cases based on the
economic and ecological assessments of energy flows
along with district level self-sufficiency, LCA of climate-
related emissions and costs, and the validation of energy
simulation models and results at an urban scale.
Economic and Ecological Assessment of Energy
Flows and Autarky in Districts
One of the major changes in energy supply for districts is
the transmission from a centralized, unidirectional to a
decentralized, bidirectional structure. As buildings
become prosumers, i.e. producers and consumers of
energy, the simulation of energy flows in districts is
becoming more and more complicated. Especially in
mixed-use districts with bidirectional low temperature
district heating systems (Boesten et al. 2019), the role of
energy generation facilities is decreasing as buildings
exchange energy flows between themselves. Therefore, it
is crucial for a data structure to provide information about
central and decentral generation facilities and networks.
Furthermore, the hierarchical structure of the networks
must be defined along with the buildings, facilities and
storage systems in each network. The energy flows
between and within these networks should also be
quantified. Based on the amount of energy to and from
each building and the connection to the utility systems, it
must be possible to assess the self-sufficiency of
individual buildings, groups of buildings, or the entire
district. Additionally, it should be possible to evaluate the
energy flows in terms of cost, ecological impact, and
absolute emissions. This enables the creation of a carbon
footprint for the energy production and consumption of
any given district object in all LoD.
District-Scale LCA of Climate Impact-Related
Emissions and Costs
The determination of a district’s climate impact and the
related costs require a meticulous amount of data for
individual district objects (buildings, utilities and grids),
its components and the energy demand. More specifically,
the energy consumed within the life cycle phases of
production, operation, and dismantling/recycling and
respective costs need to be assigned to each building,
utility and grid. Material-related LCA data are available
in abundance, i.e. publicly available databases for
environmental impacts are being set up and maintained in
part even at state level (Ciroth and Di Noi 2019; Martínez
Rocamora et al. 2016; Pagnon et al. 2020). Yet,
comprehensive data models for the respective district
objects currently seem to be limited to specific
applications, e.g. in the building sector (Sinha et al. 2016;
Wolf et al. 2017). Moreover, a mechanism for the
validation of a district’s reduced model in terms of LCA
is yet to be developed. In order to enable the development
of a validated toolchain for the district-scale LCA of
climate impact-related emissions and costs, the authors
propose an extension of the Energy ADE with LCA-
related classes. In accordance with the LoD concept of
CityGML (Table 1) the extension shall enable the
development of validated low-order district object models
in order to conduct time-efficient LCA simulations. In
alignment with the general availability of LoD 1 and 2
building models distributed by German federal services
(ldbv Bayern 2021), it should be an objective to attribute
such low-order models with LCA information. In
particular, the integration of utilities on a district level is
to ensure the consideration of all district objects for
conducting district-scale LCA mainly focusing on climate
impacts.
Validation of Urban Scale Energy Simulation Models
and Results
The results of energy performance analysis and
simulations highly depend on the simulation
environments that are being used to compute the energy
demands. Quite often there are different techniques and
processes that are used, within the applied simulation
environment, to simplify the geometry of the input
building models. In some cases, however, the models
might even lose the overall geometrical characteristics as
they are simplified for faster and efficient urban scale
computations and analysis. Moreover, some national and
international standards, such as (ASHRAE) benchmark
the modeling requirements. These standards can be used
to validate simulation models and results. However, the
standards are sometimes based on individual simulation
environments (Henninger and Witte 2004). For the
homogeneous energetic analysis of individual urban
areas, it is important to perform the validation on the
model (before and after the simulation) as well as the
simulations. For validating and authenticating the
simulations at an urban scale, it is necessary to include the
heat and power network, connection and distribution links
between the individual buildings. Therefore, the authors
propose an extension to some of the classes of the Energy
ADE with respect to the integration of the utilities.
Common Requirements for an Extended Energy
ADE
Each of the previously described use cases requires
certain extensions to the Energy ADE 1.0 with regard to
utilities. Though, the Utility Network ADE, can also be
used for achieving the desired use cases, it does, along
with the Energy ADE, drastically increase the
computational requirements and calculations for urban
energy simulations. Moreover, certain modules such as
material properties and building physics which are
required for building energy simulations are present in
detail in the Energy ADE. Therefore, in this paper, the
authors mainly focus on the use case specific extensions
to the Energy ADE by integrating utilities for
building/urban energy performance simulations.
Furthermore, to ensure the development of a succinct
update, the common denominators of all the use cases
were identified and structured.
The clustering process should be illustrated with an
example of a simplified energy distribution model for
district level representations. To make the economic and
ecological assessments of energy flows and self-
sufficiency levels in districts, a basic representation of
energy networks and flow directions is required. A similar
representation is also needed for the overall determination
of the CO2 emissions of a district and the allocation of
emissions to specific district objects. The logical
representation of bidirectional energy network links also
helps to validate the energy flows in the buildings. This
could also be used for comparing simulation results to
some national and international standards such as
ASHRAE Building Energy Simulation Test
(ANSI/ASHRAE 2017). The flows of energy can also be
an important factor for simulating and further comparing
to other simulation tools for urban-scale energy demand.
Figure 2: Example illustration of the separate use cases
and synergy between their required functions.
Information retrieved from (Behm et al. 2020).
Therefore, the authors propose to implement a logical,
bidirectional network representation that includes energy
converters and storages to cover the described use cases.
In the next section, the implementation of the
modifications made to the Energy ADE 1.0 are explained.
Implementation of the New ADE Schema
The presented requirements highlight the need of a
hierarchical structure to facilitate the storage of energy
flows from centralized and decentralized energy suppliers
to the buildings. It is also important to demonstrate the
existing links between individual buildings and networks.
The presented approach in Figure 3 illustrates the
proposed structure centered around a generic network,
considered to be a central hub in which every connection
of the energy utility in buildings and districts is stored.
Moreover, a network is connected to a specific building
or district and contains the information about all energy
converters (EnergyConverterLink) and storage systems
(EnergyStorageLink) in the network via links. It also
allows the connection to other networks to facilitate
energy flows between hierarchical layers, i.e. central
networks in a district and decentral networks in buildings.
These links derive from “AbstractLink” and store two
TimeSeries for the energy flows into and out of a network.
The network has a certain “energyCarrierType” and
every connecting network has to be of a similar type. It
can have an approximated length, status and a year of
construction. The network is derived from the CityObject
and has a connection to “SolidMaterial” to model the
pipe or wire material, density and other material
properties. In contrast to the existing Utility Networks
ADE, the proposed schema vastly omits the geographical
and physical representation of networks with the
exception of length and material properties for the sake of
heuristic LCA conduction.
The groups of buildings can be mapped by
CityObjectGroups which in itself are CityObjects. The
featureType “District” is derived from this
CityObjectGroup and is used as a filter to support this
district-based approach. As Figure 3 only illustrates the
connection of the links to the network, the connections to
the storage and converter systems are presented in Figure
4. Every storage system is connected to exactly one
storage link. The energy flowing from and to the storage
is stored in the two existing time series of the same link.
Figure 3: Hierarchical network system including links to other networks, energy and storage systems
«featureType»
District
name: CharacterStr ing
«featureType»
SolidMaterial
«CodeList»
StatusValue
+ inUse
+ tempOutOfService
+ outOfService
+ blocked
+ underConstr uction
+ planned
+ destroyed
+ unknown
EnergyStorageLin kEnergyConverter LinkNetworkLink
«featureType»
AbstractLink
energyAmountInputNetwork: AbstractTimeSeriesGroup
energyAmountOutputNetwork: AbstractTimeSeriesGroup
constraints
_cityObject has to be
a building or a district
«featureType»
Network
+ energyCarr ierType: energyCarrierTypeValue
+ status: statusValue
+ length: decimal
+ yearOfConstru ction: Integer
«Feature»
CityGML::_CityObject
1
0..* 0..*0..*0..*
1
0..*
Grey Energy
Allocation methods
Energy demand
Sources
Conversion
Monitoring
Degree of
self
-
sufficiency
Flexibilities
Emissions
Storage
Energy distribution
Utilities
CO2 - Optimization
LifeCycle Assessment
Self-sufficiency
Validation
This is possible, as the energy flow always remains in the
same network using the same energyCarrierType. In
contrast, the energyConverterLink only connects to either
input or output of an energy converter since the
energyCarrierType changes. Considering an example of a
heat pump with an electric and a hot water network, one
energyConverterLink connects from the electric network
to the electric input of the heat pump. Another
EnergyConverterLink is used to create the link from the
hot water energy output from the heat pump to the
respective network. In the case of the energy converter,
usually only one of the two time-series is utilized.
As every converter can have an arbitrary number of
energy inputs and outputs (converters can have one input
and output (e.g. boiler), two inputs (e.g. heat pump) or two
outputs (e.g. CHP)), the energy input and output is also
generalized. The energy related data of every device is
then stored in those energy inputs and outputs. This
enables the storage of efficiency and rated power for
every energyCarrierType, i.e. separate heat and electricity
power ratings for a CHP device. Additionally, every
AbstractEnergyConverter can have a use type value, e.g.
heating or electricityProduction. If no information of a
used energy converter is available, it is possible to use the
AbstractEnergyConverter with generic input and output.
If the type of the device is known, its type can be stored
in the enumeration energyConverterType. It also contains
a Boolean that is True if the converted energy results from
a renewable source. If type-specific information like rotor
length and height of a wind turbine is available, it is
possible to have an instance of the specific device derived
from the abstract energy converter, e.g. the WindTurbine
in Figure 4. The available converter types are given in the
enumeration energyConverterTypeValue”. Another
minor modification here is the addition of environmental
energy to the “EnergyCarrierTypeValue”. This is done to
have an input to the renewable energy systems. Moreover,
by contrast to Energy ADE 1.0 the proposed abstract
energy system in Figure 4 is a generalized approach that
omits classes related to specific utilities and their
respective parameters.
Using the scheme presented in Figure 3 and Figure 4, the
requirements of allocating the energy supply devices and
storage systems to their respective buildings and districts
can be fulfilled. The other central requirement to facilitate
simulations of the associated costs, emissions of devices
and the energy flows is presented in Figure 5. The
proposed structure of cost and emission classes is
intended to unify and to extend the sporadic
implementation of LCA-related in Energy ADE 1.0, e.g.
the attribute co2EmissionFactor in EnergySource, to
enable the consideration of all life cycle phases. Every
network can be associated with a data block containing
emission costs in the form of CO2 -taxes and certificate
prices. An additional information container stores cost
and emission data of the associated energy carrier in the
network. Depending on the implementation, this data can
be logically allocated in the root of the hierarchical
network, allowing for different energy carrier prices in
every household or building.
Furthermore, an LCA data container can be associated
with every city object, allowing the description of not
only energy systems but also buildings and other objects.
The LCA data container stores information about the
actual life cycle costs of objects in its production and
recycling phase. It also has the cost for operation and
maintenance, giving the possibility to differentiate the
sums of all costs and CO2-related costs, respectively.
Additionally, the actual CO2-emissions can also be stored
in the respective cycles, including the allocation method
used to derive these numbers. Lastly, the embodied
energy, i.e. the energy used within the production phase,
can be stored including the share of renewable energy
thereof. Technically, the energy converter costs with its
associated levelized cost of energy is also part of this unit
but for the reason of simplicity it is shown in Figure 4.
Another possibility to use this structure is to implement a
bottom up approach. In the Material section of the
existing Energy ADE 2.0, it is possible to describe the
walls and materials of a building. In Figure 5, every solid
material can be linked to life cycle data as well. This
allows a bottom up description of the CO2-emissions and
incorporated energy of every wall and construction part
the building consists of. This approach describes the
structure of the building by using its construction parts.
For the sake of consistency, the values stored with the city
object of the building should be the sum of its
subcomponents if the used materials have an associated
emission data set. The value stored in the CityObject can
be larger if the emissions stored in interior furniture are
considered (LoD4). The proposed changes to the time
series segment of the Energy ADE 2.0 are presented in
Figure 6. The main differences are the inclusion of a time
series group and aggregate function. Firstly, the aggregate
function is included to store single values that are
associated to a certain time series, e.g. the average,
number of values, minimum and maximum, or the sum.
These values might be needed multiple times so that it is
faster to store them than to recalculate them every time
they are needed. Secondly, the time series group is used
to describe an existing time series via different methods.
An example to this is the energy flow from a central to a
decentral network described by the network links shown
in Figure 3. This time series, while describing the same
energy flow, can be obtained by different methods, i.e.
simulations, measurements or other. These time series can
be grouped by a TimeSeriesGroup to have all relevant
time series associated to its link. This consideration is also
used for the application of the simulation validation. To
enable these assessments, the additional acquisition
method of validation and deviation between simulation
and measurement are implemented. The time series group
can then be used to cluster e.g. simulation, measurement,
deviation and validation time series of the analyzed
energy flow to support the assessment and implement an
efficient storage system.
Figure 4: Proposed abstract energy system with storage (from Energy ADE 1.0) and energy converters
including a generic input and output system
Figure 5: Cost and emission classes to enable LCA analysis
Figure 6: Additions to existing TimeSeries classes with a TimeSeriesGroup and AggregateFunctions
«featureType»
SolidMaterial «codeList»
CO2AllocationMethod
+ Method 1
+ Method 2
...
«feature Type»
Network
informationEnergyCar rier
+ purchasePrice: de cimal
+ sellingPrice: decimal
+ tCo2PerTonne: decimal
+ globalWarmingPotential: decimal
+ primaryEnergyFacto r: decimal
emissionCost
+ certificatePrices: de cimal
+ co2Tax: decimal
EmbodiedEnergyTotal
+ renewableEnergyEmbodied: decimal
+ nonRenewableEmbodiedEnergy: decimal
+ totalEmbodiedEnergy: decimal
+ percentageO fInteriorEmbodiedEnergy: decimal
EmissionsTotal
+ co2Allocation: CO2AllocationMethod
+ co2Production: d ecimal
+ co2Operatio n: decimal
+ co2Endoflife: decimal
+ co2Total: decimal
LifeCycleCost
+ productionCo st: decimal
+ operationCos t: decimal
+ maintenanceCost: decimal
+ endOfLifeCost: d ecimal
+ co2Cost: decimal
+ totalCost: decimal
«featureType»
CityGML::_CityObject
«featureType»
lifeCycleAssessment
«dataType»
TimeValuesPro perties
+ acquisitionMeth od: AcquisitionMetho dValue
[...]
«enumerat ion»
functionType Value
average
count
maximum
minimum
mode
range
sum
other
«type»
Aggrega teFunction
+ value: Decimal
+ functionType: functionTypeValue
group of time series, which
refer to the same val ue
«enumerat ion»
AcquisitionMethod Value
[...]
deviationSimulationMeas urement
validation
«type»
TimeSeriesGroup
«type»
AbstractTimeSeries
+ variablePro perties: TimeValuesPropert ies
10..*
«Featur
CityGML::_CityObject
«enumeration»
endUseTypeValue
+ spaceHeating
+ spaceCooling
+ heating
+ domesticHotWater
+ electricalProduction
+ other
«enumeration»
EnergyCarrierTypeValue
[..]
solarIrradiance
wind
hydropower
geothermal
«featureType»
PowerStorage
batteryTechnology: Char acterString [0..1]
powerCapacity: measure [0..1 ]
«featureType»
ThermalStorage
+ prepara tionTemperature: Measure [0..1]
+ medium: MediumTypeValue [0..1]
+ thermalLossesFacto r: Measure [0..1]
+ volume: Volume [0..1]
EnergyConverter Cost
+ levelizedCostOfEnergy: decimal
WindTurbine
height: decimal
rotorLeng th: decimal
«featureType»
EnergyStorageL ink
«featureType»
EnergyConverter Link
«featureType»
AbstractEnergyStorage
+ energyCarr ierType: e nergyCarrierTypeValue
+ amount: decimal
«featureType»
AbstractEnergySystem
+ numberOfDevices :Integer
+ model :CharacterString
+ serviceLife :ServiceL ife
+ yearOfManufactur e :Year
endUseType
+ endUseType: endUseTypeValue
«enumeration»
energyConver terTypeValue
biomassCogenerationPlant
gasFiredPowerPlant
blockHeatAndPowerPlant
solarEnergySystem
windTurbine
electricHeatingSystem
Photovoltaik
[...]
«featureType»
EnergyInput
+ energyCarrie rType: ene rgyCarrierTypeValue
+ powerRating
«featureType»
EnergyOutput
+ energyCarrie rType: ene rgyCarrierTypeValue
+ powerRating
+ energyConver sionEfficiency
«featureType»
AbstractEnergyConver ter
+ energyConver terType:
energyConver terTypeValue
+ comment: characterstring
+ isRenewable: boolean
1
1supplies energ y to ►
1
0..1
receives ener gy from ►
1
0..1
fulfills ►
1..*
1
1..*1
1..*1
Conclusion
This paper proposes a use case-based extension to the
Energy ADE 2.0. Several UML-diagrams are given to
explain the addition of energy systems and building
utilities. The new EnergySystems package describes a
hierarchical network to store energy flows between
supply plants and buildings. It enables LCA in terms of
carbon emissions and costs for utilities, buildings,
districts and networks. In addition, it allows validations of
energy performance simulations on urban scale. All
changes are implemented to support the access speed in
data bases to accelerate the work with large data sets in an
urban context.
The proposed use cases and the derived Energy ADE
Extension are part of an ongoing research project. In a
future work, this UML scheme is implemented into the
3DCityDB as an urban scale database and each use case
is researched and applied in detail to assess the viability
of the proposed structure and to make alterations in case
its usage does not prove to significantly reduce the access
speed in data bases or prove to be impractical.
Acknowledgement
We gratefully acknowledge the financial support by
BMWi (German Federal Ministry of Economic Affairs
and Energy), promotional reference 03EWR010B.
References
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The importance of life cycle sustainability in the construction sector is increasing in the light of rising awareness on sustainability issues in society. A means to identify more sustainable options is to assess and compare their sustainability performance. The standards ISO 21931-2 and EN 15643-1 to 5 establish the framework and requirements for sustainability assessment of buildings and civil engineering works. The standards require life cycle assessment (LCA) to be the basis for the environmental part of the sustainability assessment. LCA is a powerful evidence-based method but it requires extensive data. Access to free, easily available and preferably machine-readable LCA data is essential to increase the use of LCA in the construction sector and to make competition fair for all tenderers. This paper aims to compile existing online sources for open-access LCA data of interest for the construction sector. The purpose is to provide a reference document that facilitates the use of LCA in construction. An in-depth search of publications and internet resources was performed, focusing on European sources of Environmental Product Declarations (EPD) and process-based LCA datasets. A comprehensive overview of the European data sources available online and relevant to the construction sector is presented. This research work reveals the existence of numerous sources, often difficult and time-consuming to find. The overview in the paper facilitates finding online data needed for LCA, in many cases in a machine-readable format. This can contribute to increasing the use of LCA in the construction sector, which is important when developing buildings or civil engineering works that are more sustainable over their whole life cycle. A greater use and better integration of LCA in the design process contributes to evidence-based life cycle sustainability of our built environment.
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The energy supply of buildings in urban contexts is undergoing significant changes. The increase of renewable sources for electrical and thermal energy generation will require flexible and secure supply systems. To reflect and consider these changes in energy systems and buildings, dynamic simulation is one key element. Sparse and limited access to detailed building information as well as computing time are challenges for building simulation on urban-scale. In addition, data acquisition and modelling for building performance simulation (BPS) are time-consuming and error-prone. To enable the use of BPS on urban-scale, this paper presents TEASER, an open framework for urban energy modelling of building stocks (open-source at https://github.com/RWTH-EBC/TEASER). TEASER provides an interface for multiple data sources, data enrichment and export of ready-to-run Modelica simulation models. The paper presents TEASER's methodology and package structure. Three use cases show TEASER's capabilities on the building, neighbourhood and urban scales. © 2017 International Building Performance Simulation Association (IBPSA)
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Evaluation of the environmental impact caused by construction materials frequently presents such obstacles as the mismatch between the construction project location and where the LCA database was made, lack of transparency, and/or the unsuitability of the data to the building project conditions, thereby making it necessary to establish a state-of-the-art review for researchers in order to facilitate selection between the wide variety of databases available. A review of existent LCA databases containing data for building materials has been performed. A list of features and criteria for their evaluation is developed, and subsequently applied in order to compare the various databases. Their methodology, documentation, data quality and comprehensiveness are thereby analysed. Despite the existence of a considerable number of databases, only a few contain data on construction materials. Some projects have been abandoned and several more can be considered incomplete. However, GaBi Database and Ecoinvent stand out for their integrity, usability and dedicated resources. A starting point in the selection of an LCA database for construction materials is provided. With all the information gathered herein, researchers are equipped to make a well-founded choice, and the selection process is certainly improved.
Use Case-basierte Anwendungen der Energy ADE : Anforderungen an ein Schema zur
  • C Behm
  • A Malhotra
  • M Schildt
  • S Weck-Ponten
  • J Frisch
  • C A Van Treeck
Behm C, Malhotra A, Schildt M, Weck-Ponten S, Frisch J, van Treeck CA (2020). Use Case-basierte Anwendungen der Energy ADE : Anforderungen an ein Schema zur Ergänzung der Energy ADE um Anlagen-und Gebäudetechnik. RWTH Aachen University.