DATABASE DEVELOPMENT WITH “3D-CITYGML”-AND “ENERGYADE”- SCHEMA FOR CITY-DISTRICT-SIMULATION MODELLING.pdf

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DATABASE DEVELOPMENT WITH “3D-CITYGML”-AND

“ENERGYADE”- SCHEMA FOR CITY-DISTRICT-SIMULATION MODELLING

C. Fliegner, M. - A. Brüntjen, Dr. J. Frisch, Prof. Dr. C. van Treeck

Lehrstuhl für Energieeffizientes Bauen (E3d)-RWTH - Aachen, Germany

ABSTRACT

The paper details parts of the work within the Aachen

EnEff:Campus RoadMap project aiming at

developing a systematic road map towards a cost-

effective reduction of the primary energy

consumption of the campus. The project follows a

systematic approach for deriving low-order dynamic

building and distribution network energy

performance models from a geoinformation data

base. The building data aggregation process and the

specific layout of the campus database is presented.

The developed PostgreSQL data base comprises a 3D

city model with application domain extensions

(ADEs) for modeling the energy system, thermal

networks, building physics, occupancy, material and

time series descriptions.

KURZFASSUNG

In diesem Paper wird der Arbeitsstand des

Forschungsprojektes „EnEff:Campus:RoadMap“

vorgestellt. Ziel des Projektes ist einen Plan für

minimalinvestive Sanierungsmaßnahmen zu

entwickeln, die zu nutzflächenspezifischen

Primärenergieeinsparung der Campus Liegenschaften

führen. Die dynamische Gebäude- und

Netzsimulation, die als Werkzeug Anwendung findet,

benötigt dafür Modellparameter die in einer

spezialisierten Datenbank abgelegt werden. Im

Folgenden wird zum einen der Prozess der Erfassung

bzw. Erzeugung von relevanten Daten, andererseits

die Anpassung und Entwicklung der Datenbank

beschrieben. Die entwickelte Struktur fußt auf einer

PostgreSQL-Basis und umfasst die bestehenden

Application Domain Extensions (ADEs) EnergyADE

und UtilityNetworkADE [CityGML Energy ADE.

2016].

INTRODUCTION

The building sector has the largest single potential for

reducing primary energy demands and, thus, for

lowering energy-related greenhouse gas emissions

[UNEP 2009]. Improving the energy efficiency of the

thermal quality performance of the building envelope

and the energy systems is a key element [IEA 2013].

Thereby, buildings cannot be treated as individual

energy systems in an isolated manner. In order to

make use of volatile renewable energy sources, future

buildings need to store both thermal and electrical

energy and need to interact with superordinated

network(s) with respective demand-side management

methods [Müller/Monti2015].

In order to find economically and ecologically viable

solutions for energetic refurbishments and major

savings in primary energy for a university-wide

campus such as the RWTH Aachen campus,

buildings cannot be treated in a passive and isolated

manner. Furthermore, a campus typically comprises

very different typologies of buildings in terms of age,

size, topology, scope and usage. Huge energy

demands for singular test facilities may come along

with large potentials of waste heat and the like with

individual differences in scheduling and availability.

A campus is further subject to rapid changes due to

new test facilities or conversions of buildings in terms

of usage. For optimizing a campus in terms of a

sustainable refurbishment strategy and roadmap, an

integrated solution approach is needed taking into

account both the demand and the suppy sides with

respect to the individual buildings and their dynamics

in terms of energy usage and waste heat potentials,

the distribution networks and the power, heat and

cooling generation side.

The central aim of the project "EnEff: Campus -

RoadMap RWTH Aachen" is to develop a road map

for the reduction of the specific primary energy

consumption with respect to the effective area of

buildings at RWTH Aachen University by 50% until

2025, based on the energy consumption of 2013/14.

This goal will be achieved through an innovative

global concept and will be determined by a newly

developed and implemented comprehensive and

transferable methodology for energy optimization of

university campus buildings. Therefore, the current

“in-situ” condition is systematically recorded in a

database and presented using dynamic urban district

simulation. Together with an integrated data

management, a basis for the most efficient and

economical optimization strategies and measure plans

is created. For these plans of measures, three

refurbishment alternatives for the three campus areas

of the RWTH Aachen University will be developed

C-03-2 Database development with “3D-CityGML”- and

“EnergyADE”- schema for city-district-simulation modelling

235

as element of the road map for the reduction of

primary energy consumption. All relevant data that is

used to describe the thermal and energetic behavior

of the campus areas was collected at the beginning of

the project. This includes but is not limited to

geographical data, geometrical and topological data,

building physics data and occupancy. Furthermore,

the energy performance of buildings and thermal

networks are closely monitored. All collected data is

stored in a common database.

For effectively organizing the data and as basis for

the transition to simulation, an appropriate and

comprehensive database structure is necessary such

as the GML-profile “CityGML” [Häfele 2013] and its

Application Domain Extensions “CityGML

EnergyADE” [Special Interest Group 3D 2015].

Based on the collected data and a structured database,

it is possible to visualize the current state of all

RWTH campus areas in a geographic information

system (GIS). In the settings of this project, the

specific software QGIS Vienna is used [QGIS 2015].

To build up the current state of the energy supply

system, several thermal dynamic simulation models

are created based on the collected dataset. The

simulation models include the buildings with their

building services systems, the distribution and

generation systems. All of those previously

mentioned systems have to be described by simplified

models in order to capture the scale of the whole

campus within a single simulation scenario. These

buildings and system models are then coupled with

thermal network models. Thus, the entire energy

system can be detected and simulated with its

dependent interactions. Consecutively, these coupled

models will help to find an optimized solution for

efficient measures and refurbishment options.

To ensure that the simulation results obtained by the

simplified models correlate with the current energy

consumption, important parameters, such as the

consumption data of the buildings are recorded

properly. Further monitored parameters represent

produced and transmitted energy flows or energy

quantities in the thermal network, for example. These

are used for indicating irregularities in daily

operation modes of energy systems and represent a

potential for improvement.

Based on the analysis of monitoring data and

simulation results, individual improvement measures

are derived and verified iteratively, through which the

primary energy demand and greenhouse gas

emissions can be reduced. The measures consist of

retrofitting the building stock, energy- and conversion

system technology, the thermal network typology, or

a combination of those. As well, savings resulting

from conversion of existing buildings or strategic

demolition and new construction of buildings can be

considered in the model. For all these optimization

approaches, a cost estimate is performed using the

present value method, assessing their impact,

feasibility and cost effectiveness, respectively. After

the evaluation of individual measures conclusive

refurbishment variants are developed. Various

measures and packages of measures are combined,

simulated and evaluated in the light of their

respective interactions. By simulating several

scenarios, refurbishment options can be tested under

different conditions and checked according to their

practical feasibility. This ensures that undesirable

consequences are excluded and detrimental effects

can be minimized. Hence, the simulated dynamic

energy flows are represented by a three-dimensional

GIS-based graphic model of the Aachen campus. In

the following, the mentioned tools like QGIS,

TEASER, and the developed PostgreSQL database

structure are described in detail.

METHODOLOGY

In order to use the entire mentioned tools in an

automated manner, the development of a central

database is essential. Through this, relevant data is

retrieved, which describes the current thermal

energetic state of the campus buildings. As it is the

aim of the project to develop a roadmap of measures

towards reducing heating and cooling energy

consumption, the data base necessarily needs to

reflect the building geometry, thermal building

properties (i.e., the building physics), usage, etc. in

the database. A viable and existing database scheme

is the "3D City Database for CityGML" [Kolbe et.al

2015]. It enables to store three-dimensional

geometries with respect of predefined levels of

geometric detailing (LOD) of a city in the database.

Building up on this database, in the EnEff:Campus

project the data base is extended by thermal and

energy-related characteristics of the buildings. These

characteristics become availabe in the CityGML

Energy Application Domain Extensions (ADE). It is

an extension of the CityGML standard and has the

intention “to define a standard for exchanging

information for energy simulation on urban level”

[Energ ADE 2016]. This extension is currently under

development; the currently available state is

integrated within the 3D city database. With this

extended database, an automated tool chain is created

for district simulation. In the following, the tools are

presented which are used for building performance

simulation in the EnEff:Campus project.

3D City database and 3D City Database Importer-

Exporter

The 3DCityDB is a PostgreSQL database based on

the CityGML schema [Kolbe et.al 2015]. It allows for

depicting roads, bridges or areas. Buildings can be

mapped up to LOD4. In addition to the database, the

”3D City Database Importer / Exporter v1.6 -

postgis“ tool [Kolbe et.al 2015] can be used to import

CityGML data. Thus, for example, the campus

C-03-2 Database development with “3D-CityGML”- and

“EnergyADE”- schema for city-district-simulation modelling

236

DATABASE DEVELOPMENT WITH “3D-CITYGML”-AND

“ENERGYADE”- SCHEMA FOR CITY-DISTRICT-SIMULATION MODELLING

C. Fliegner, M. - A. Brüntjen, Dr. J. Frisch, Prof. Dr. C. van Treeck

Lehrstuhl für Energieeffizientes Bauen (E3d)-RWTH - Aachen, Germany

ABSTRACT

The paper details parts of the work within the Aachen

EnEff:Campus RoadMap project aiming at

developing a systematic road map towards a cost-

effective reduction of the primary energy

consumption of the campus. The project follows a

systematic approach for deriving low-order dynamic

building and distribution network energy

performance models from a geoinformation data

base. The building data aggregation process and the

specific layout of the campus database is presented.

The developed PostgreSQL data base comprises a 3D

city model with application domain extensions

(ADEs) for modeling the energy system, thermal

networks, building physics, occupancy, material and

time series descriptions.

KURZFASSUNG

In diesem Paper wird der Arbeitsstand des

Forschungsprojektes „EnEff:Campus:RoadMap“

vorgestellt. Ziel des Projektes ist einen Plan für

minimalinvestive Sanierungsmaßnahmen zu

entwickeln, die zu nutzflächenspezifischen

Primärenergieeinsparung der Campus Liegenschaften

führen. Die dynamische Gebäude- und

Netzsimulation, die als Werkzeug Anwendung findet,

benötigt dafür Modellparameter die in einer

spezialisierten Datenbank abgelegt werden. Im

Folgenden wird zum einen der Prozess der Erfassung

bzw. Erzeugung von relevanten Daten, andererseits

die Anpassung und Entwicklung der Datenbank

beschrieben. Die entwickelte Struktur fußt auf einer

PostgreSQL-Basis und umfasst die bestehenden

Application Domain Extensions (ADEs) EnergyADE

und UtilityNetworkADE [CityGML Energy ADE.

2016].

INTRODUCTION

The building sector has the largest single potential for

reducing primary energy demands and, thus, for

lowering energy-related greenhouse gas emissions

[UNEP 2009]. Improving the energy efficiency of the

thermal quality performance of the building envelope

and the energy systems is a key element [IEA 2013].

Thereby, buildings cannot be treated as individual

energy systems in an isolated manner. In order to

make use of volatile renewable energy sources, future

buildings need to store both thermal and electrical

energy and need to interact with superordinated

network(s) with respective demand-side management

methods [Müller/Monti2015].

In order to find economically and ecologically viable

solutions for energetic refurbishments and major

savings in primary energy for a university-wide

campus such as the RWTH Aachen campus,

buildings cannot be treated in a passive and isolated

manner. Furthermore, a campus typically comprises

very different typologies of buildings in terms of age,

size, topology, scope and usage. Huge energy

demands for singular test facilities may come along

with large potentials of waste heat and the like with

individual differences in scheduling and availability.

A campus is further subject to rapid changes due to

new test facilities or conversions of buildings in terms

of usage. For optimizing a campus in terms of a

sustainable refurbishment strategy and roadmap, an

integrated solution approach is needed taking into

account both the demand and the suppy sides with

respect to the individual buildings and their dynamics

in terms of energy usage and waste heat potentials,

the distribution networks and the power, heat and

cooling generation side.

The central aim of the project "EnEff: Campus -

RoadMap RWTH Aachen" is to develop a road map

for the reduction of the specific primary energy

consumption with respect to the effective area of

buildings at RWTH Aachen University by 50% until

2025, based on the energy consumption of 2013/14.

This goal will be achieved through an innovative

global concept and will be determined by a newly

developed and implemented comprehensive and

transferable methodology for energy optimization of

university campus buildings. Therefore, the current

“in-situ” condition is systematically recorded in a

database and presented using dynamic urban district

simulation. Together with an integrated data

management, a basis for the most efficient and

economical optimization strategies and measure plans

is created. For these plans of measures, three

refurbishment alternatives for the three campus areas

of the RWTH Aachen University will be developed

C-03-2 Database development with “3D-CityGML”- and

“EnergyADE”- schema for city-district-simulation modelling

235

as element of the road map for the reduction of

primary energy consumption. All relevant data that is

used to describe the thermal and energetic behavior

of the campus areas was collected at the beginning of

the project. This includes but is not limited to

geographical data, geometrical and topological data,

building physics data and occupancy. Furthermore,

the energy performance of buildings and thermal

networks are closely monitored. All collected data is

stored in a common database.

For effectively organizing the data and as basis for

the transition to simulation, an appropriate and

comprehensive database structure is necessary such

as the GML-profile “CityGML” [Häfele 2013] and its

Application Domain Extensions “CityGML

EnergyADE” [Special Interest Group 3D 2015].

Based on the collected data and a structured database,

it is possible to visualize the current state of all

RWTH campus areas in a geographic information

system (GIS). In the settings of this project, the

specific software QGIS Vienna is used [QGIS 2015].

To build up the current state of the energy supply

system, several thermal dynamic simulation models

are created based on the collected dataset. The

simulation models include the buildings with their

building services systems, the distribution and

generation systems. All of those previously

mentioned systems have to be described by simplified

models in order to capture the scale of the whole

campus within a single simulation scenario. These

buildings and system models are then coupled with

thermal network models. Thus, the entire energy

system can be detected and simulated with its

dependent interactions. Consecutively, these coupled

models will help to find an optimized solution for

efficient measures and refurbishment options.

To ensure that the simulation results obtained by the

simplified models correlate with the current energy

consumption, important parameters, such as the

consumption data of the buildings are recorded

properly. Further monitored parameters represent

produced and transmitted energy flows or energy

quantities in the thermal network, for example. These

are used for indicating irregularities in daily

operation modes of energy systems and represent a

potential for improvement.

Based on the analysis of monitoring data and

simulation results, individual improvement measures

are derived and verified iteratively, through which the

primary energy demand and greenhouse gas

emissions can be reduced. The measures consist of

retrofitting the building stock, energy- and conversion

system technology, the thermal network typology, or

a combination of those. As well, savings resulting

from conversion of existing buildings or strategic

demolition and new construction of buildings can be

considered in the model. For all these optimization

approaches, a cost estimate is performed using the

present value method, assessing their impact,

feasibility and cost effectiveness, respectively. After

the evaluation of individual measures conclusive

refurbishment variants are developed. Various

measures and packages of measures are combined,

simulated and evaluated in the light of their

respective interactions. By simulating several

scenarios, refurbishment options can be tested under

different conditions and checked according to their

practical feasibility. This ensures that undesirable

consequences are excluded and detrimental effects

can be minimized. Hence, the simulated dynamic

energy flows are represented by a three-dimensional

GIS-based graphic model of the Aachen campus. In

the following, the mentioned tools like QGIS,

TEASER, and the developed PostgreSQL database

structure are described in detail.

METHODOLOGY

In order to use the entire mentioned tools in an

automated manner, the development of a central

database is essential. Through this, relevant data is

retrieved, which describes the current thermal

energetic state of the campus buildings. As it is the

aim of the project to develop a roadmap of measures

towards reducing heating and cooling energy

consumption, the data base necessarily needs to

reflect the building geometry, thermal building

properties (i.e., the building physics), usage, etc. in

the database. A viable and existing database scheme

is the "3D City Database for CityGML" [Kolbe et.al

2015]. It enables to store three-dimensional

geometries with respect of predefined levels of

geometric detailing (LOD) of a city in the database.

Building up on this database, in the EnEff:Campus

project the data base is extended by thermal and

energy-related characteristics of the buildings. These

characteristics become availabe in the CityGML

Energy Application Domain Extensions (ADE). It is

an extension of the CityGML standard and has the

intention “to define a standard for exchanging

information for energy simulation on urban level”

[Energ ADE 2016]. This extension is currently under

development; the currently available state is

integrated within the 3D city database. With this

extended database, an automated tool chain is created

for district simulation. In the following, the tools are

presented which are used for building performance

simulation in the EnEff:Campus project.

3D City database and 3D City Database Importer-

Exporter

The 3DCityDB is a PostgreSQL database based on

the CityGML schema [Kolbe et.al 2015]. It allows for

depicting roads, bridges or areas. Buildings can be

mapped up to LOD4. In addition to the database, the

”3D City Database Importer / Exporter v1.6 -

postgis“ tool [Kolbe et.al 2015] can be used to import

CityGML data. Thus, for example, the campus

C-03-2 Database development with “3D-CityGML”- and

“EnergyADE”- schema for city-district-simulation modelling

236

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