=Paper=
{{Paper
|id=None
|storemode=property
|title=Integrated Design of Simulation Models for Passive Houses
|pdfUrl=https://ceur-ws.org/Vol-821/paper3.pdf
|volume=Vol-821
}}
==Integrated Design of Simulation Models for Passive Houses==
https://ceur-ws.org/Vol-821/paper3.pdf
First Workshop on Industrial Automation Tool Integration for Engineering Project Automation
Integrated Design of Simulation Models for Passive Houses
Petr Novák∗ ,† Radek Šindelář∗
∗ Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems,
Vienna University of Technology, A-1040 Vienna, Austria
{novak,sindelar}@ifs.tuwien.ac.at
† Department of Cybernetics, Faculty of Electrical Engineering,
Czech Technical University, Prague, Czech Republic
Abstract—Modern automation systems require both design- Simulation
Floor plan
Library
time and runtime integration of diverse engineering tools. Tra- HouseBuilder
Bldsimlib
Simulation
library file
ditional integration approaches are based on repeating manual
Config.xml Simulation
work, being time-consuming and error-prone. In this paper, file library file
applications of semantic integration, dealing with meaning
House Builder
of objects and their interfaces, is explained and shown on Config File
Expert Assisted
a real industrial use-case. Simulations are useful tools for Simulation Tool
Parser
process optimization or performance testing and the presented
Plant ontology Simulation
methodology makes their design for particular industrial plants individuals library features
flexible. The use-case shows that the design of simulation
Simulation
models for passive houses can be user-friendly and feasible even Plant Ontology Library
for non-experts as it is based on a graphical tool that enables Ontology
to draw a passive house floor plan. Since neither this tool nor OWL OWL
a universal simulation library, comprising atomic simulation Ontology Ontology
blocks, were intended for simulation purposes, the presented
methodology is a typical example of tool integration having Semantic
Engine
heterogeneous data models.
The goal of this paper is to propose an ontology-based Simulation
formalization of knowledge representing structures of real model file
industrial plants and simulation models. The paper also intro-
Simulation
duces the design of simulation models for passive houses from Model
other engineering sources, which can be used by non-experts
for simulation modeling. The practical usage is restricted by
the fact that simulation parameters must be entered manually. Figure 1. Workflow presented in this paper.
The main contributions of the paper are the proposed structure
of an automation ontology and a workflow of simulation model
design that is not common in engineering disciplines.
Keywords-Semantic integration, simulation model, passive simulations. The presented approach is based on Semantic
house, ontology, automation system design phase. Web technologies. Knowledge about the tools under inte-
gration is stored in ontologies. Ontology-based querying and
I. I NTRODUCTION reasoning techniques are used to retrieve the information and
Simulation models emerged as a very efficient way to op- derive new pieces of engineering knowledge. The proposed
timize process operation. They can be used for performance solution realizes an ontology-based middle-ware layer be-
testing of control algorithms or the whole industrial systems tween the tools. A use-case project, dealing with a design
under both normal and extreme conditions, as well as for of simulation models for passive houses, is described in
many other engineering tasks. Nevertheless, several issues this paper. It motivates the research, the examples from this
dealing with simulation models have not been satisfactorily domain are given, and in the final part, this use-case project
solved. The integration of simulation models and the coop- is discussed and evaluated.
eration with other engineering tools still remain problems The goal of this paper is to present a formalization of real
as well as the high requirements on engineering knowledge plant data in a machine-understandable way and to show
and skills to create and configure them. Therefore, simu- the benefits of semantic integration of heterogeneous data
lation models are usually designed and performed only by models on a passive house model use-case.
simulation experts. The workflow of the presented use-case project is depicted
This paper contributes to improve the simulation model in Fig. 1. The entries for the presented methodology are
design phase, and it shows how engineering sources can be a representation of a graphical floor plan of a particular
used to enhance simulation model usage for non-experts in passive house created in “House Builder WPF” software
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First Workshop on Industrial Automation Tool Integration for Engineering Project Automation
and a universal simulation library “Bldsimlib” 1 comprising The usage of ontologies in modeling and simulations is
generic simulation blocks for passive house simulation [1]. introduced e.g. in [6]. In [7] the Ontology-driven Simulation
Both these entries are suitable examples of engineering Tool (ODS) is described. The approach is based on two
sources having heterogeneous data models that must be ontologies: a domain ontology categorizing a knowledge
integrated involving meaning of data and interfaces. Floor including a problem vocabulary and its concepts are mapped
plan of House Builder WPF software originally solves for onto a modeling ontology being used for the simulation
visualizing passive house runtime data in a tool House model description. Our approach distinguishes between plant
Viewer WPF, but in our approach, we also use the same domain and simulation knowledge in a similar way.
file without any modification for defining so-called plant An ontology-driven simulation model design is presented
ontology individuals, i.e. ontology-based representation of in [8]. The paper is focused on generating MATLAB-
the passive house structure. Simulation blocks and their Simulink blocks and defining them via DAVE-ML according
features are formalized in a so-called simulation ontology. to the domain ontology. Connection of these blocks is
Consequently, plant and simulation ontologies are used to done manually, thus this approach is complementary to the
semantically create a simulation model for a passive house. methodology explained in this paper.
The remainder of this paper is structured as follows: the The methodology presented in this paper uses so-called
second section summarizes a related work. The third section power bonds and signal bonds to classify a type of device
formulates research issues that are addressed in the fourth interconnection. These terms originate from a bond graph
section, summarizing a methodology for formalization of theory, that is introduced e.g. in [9]. Power bonds are
plant, simulation and other engineering knowledge in gen- common in real physical systems where the flow of energy
eral, and in the fifth section, describing the use-case project defines power transmissions, whereas signal bonds refer to
and its results. The sixth section concludes and proposes interconnections where energetic interactions can be ignored.
further work topics. For example, there is usually assumed that sensors have no
impact on measured variables, thus this kind of relationship
II. R ELATED W ORK
is called signal, whereas e.g. tanks and outlet pipe interact
Semantic integration is a perspective way to integrate by power bonds.
diverse systems and tools, which is based on data meaning.
Semantic level stands on top of a technical integration level,
that is concerned with data transfers. Further explanation can III. R ESEARCH I SSUES
be found e.g. in [2]. Although the semantic integration can
be implemented in many ways, the wide-spread approach is The problems, which are discussed in this paper, can be
based on Semantic Web technologies, especially representa- summarized into following research issues.
tion of knowledge in ontologies. RI-1: Formalization of plant and simulation knowl-
The term ontology, originating from philosophy, is in edge in general. As real plants have diverse structures and
engineering defined in many ways [3]. One of the most devices, there is a need for formalizing their description.
cited definitions is by Gruber: ”An ontology is an explicit Such a formalization is useful for reusability, flexibility in
specification of a conceptualization” [4]. In the presented terms of process redesign, and automatic or semi-automatic
approach, ontologies are represented in OWL DL2 format methodologies for supporting both design and run-time
that provides a suitable compromise between expressive phases of the automation system lifecycle.
power and performance of reasoning. We use ontology RI-2: Applications of the formalization for design
querying language SPARQL3 and ontologies are managed of simulation model for a particular passive house.
from Java code via framework ARQ4 , providing query The particular use-case project integrates the two stand-
engine on top of Jena API5 . alone engineering tools. A universal library Bldsimlib is
Although for simulation integration general-purpose tech- implemented in MATLAB-Simulink6 and it comprises so-
niques such as DCOM, CORBA, J233 could be used [5], called generic simulation blocks. They approximate building
there exist frameworks including standard vocabulary for elements, such as windows, walls, doors, or rooms. Usually,
simulation integration, such as DIS, SEDRIS or HLA [6]. only simulation experts are able to create and perform the
Especially HLA framework is widely cited, but it does not simulation model. We try to overcome this shortcoming by
support integration on semantic level. Therefore, we focused integrating the second tool, the graphical House Builder
on ontology-based approaches. WPF application. The XML config file, being its output,
is used to recognize a structure of the house. It is stored in
1 Acronym of “Building Simulation Library”.
a machine-understandable form and consequently, a simula-
2 http://www.w3.org/TR/owl-features/
3 http://www.w3.org/TR/rdf-sparql-query/
tion model is generated semi-automatically.
4 http://jena.sourceforge.net/ARQ/
5 http://jena.sourceforge.net/ontology/ 6 http://www.mathworks.com/products/
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First Workshop on Industrial Automation Tool Integration for Engineering Project Automation
Domain of real industrial plants
controls
Device “A” Domain of control
Device “B” and measuring
systems
signal_bond
measures Room Wall Room
Device “C” power_bond
room1 wall1 room2
simulates affects
Domain of Figure 3. Comparison of interconnections in terms of plant description
Domain of simulations and signal-oriented simulators.
disturbances
Figure 2. Formalization of problem domains and relationships of their
elements. of the bonds is realized while assembling the simulation
model in Java code, performing series of SPARQL queries.
As we defined several domain ontologies that describe the
IV. F ORMALIZATION OF P LANT, S IMULATION , AND real plant and simulation, the relationship between these on-
OTHER E NGINEERING K NOWLEDGE tologies are realized via relations summarized in the previous
paragraph. All of the ontologies and their relationships build
Our approach is based on explicit distinction between a so-called automation ontology, being depicted in Fig. 4.
plant, simulation, and other engineering knowledge. Plant The rectangular blocks represent ontology classes, whereas
knowledge is related to existing devices and elements, the rounded blocks are ontology individuals. For better read-
whereas simulation knowledge comprises features of avail- ability, some individuals are omitted, but the fundamental
able simulation blocks, e.g., their interfaces. We store plant ideas are covered by this figure. We can see the classes filled
knowledge in a so-called plant ontology, and simulation in blue color that represent an upper layer, shared within
knowledge in a so-called simulation ontology. Further, we diverse automation systems. The plant ontology comprises
define supplemental ontologies such as a signal ontology. real industrial plant devices, which are categorized into
Specification of fundamental domains and their relation- five classes. “Actuator” class involves controllable devices,
ships is introduced in Fig. 2. The upper left set represents “Passive Element” represents uncontrollable devices. “Dis-
a real plant domain. The figure depicts that real plants turbance” affects real plant, it defines boundary conditions
comprise devices, that are connected by two basic domain- and it is often non-measurable and random. “Measure point”
specific terms: “signal bond” and “power bond”, that we defines sensors or softsensors that are software algorithms
use in compliance with bond graph theory to define the calculating a value from other variables. Blocks filled in
real device interconnections. We consider a measuring and yellow color depend on a type of a particular plant, in
control system as one domain, which interacts with the real our case an example passive house classes are depicted
plant domain via properties “measures” and “controls”. We (further classes were omitted for better readability). The
explicitly define disturbances, i.e. factors that influence the simulation ontology comprises the description of available
real plant and that are usually not desired from a control simulation libraries and final simulation models including
point of view. For example, when controlling a temperature their interfaces. Since industrial devices and tools usually
in a house, disturbances are the sun, humans, or opening and requires information about not only the connections, but
closing doors as they impact on the controlled variable. Sim- port numbers as well, the individuals of “Port” are depicted.
ulation models are interconnected with a real plant via the They represent all available ports and specify their signal
relation “simulates”, expressing that some real plant device types. Last but not least, “Power Bond Decomposition”
is approximated by the particular generic simulation block. labels doubles of ports that decompose power bonds, i.e.
According to our industrial experiences, this relationship is they define signal routing in signal-oriented tools.
not usually 1:1 but 1:n, i.e. one plant device can be simulated Although a structure of the automation ontology could
by more than one simulation blocks. seem complicated at first, it provides powerful support for
An important issue for simulation model design is a de- diverse engineering tasks. Furthermore, it is not expected to
composition of power bonds for signal-oriented simulators, be modified by hand in a general-purpose ontology editor,
such as MATLAB-Simulink. Figure 3 depicts a description but either in specialized editors implemented for automation
in the plant ontology and the corresponding schema in purposes or via specialized tools such that a user does not
MATLAB-Simulink. We can see that each power bond is interact with this ontology at all. The latter case is used in
decomposed into two signal bonds, i.e. two interconnections the passive house use-case presented in this paper, as the
in the signal-oriented simulator. In our approach, translation plant ontology is created semi-automatically by the parser
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First Workshop on Industrial Automation Tool Integration for Engineering Project Automation
owl:Thing
Plant Simulation Port Signal
Passive Measure Simulation Power Port User-
Actuator Disturbance Controller
Element Point Library Decomposition defined
PB Wall 1 PB Wall 2
State Room Room
HVAC Unit Envelope State Flow
Elements
Bldsimlib Room input Port Wall Input 1 signal
input Port Wall Input 2 signal
Wall Room Exterior
Bldsimlib Wall output Port Wall Output 1 signal
room1 simulates output Port Wall Output 2 signal
simulates
Figure 4. Automation ontology: Machine-understandable knowledge-base formalizing industrial plant, simulation and other engineering knowledge.
Figure 5. An example of room description in a House Builder WPF configuration file config.xml.
of a passive house floor plan. Although the description of in a Java loop taking the free ports and decomposing the
simulation model blocks, i.e. a simulation ontology, a port power signals by doubles of signal interconnections in case
ontology and a power bond ontology must be entered by of signal-oriented tools.
humans, we are implementing a tool that supports this work
and makes it easy and user-friendly. V. U SE - CASE P ROJECT: I NTEGRATED D ESIGN OF
Since simulation parameters, i.e. parameters required for PASSIVE H OUSE S IMULATION M ODEL
the parametrization of simulation blocks, can differ from The goal of this section is to provide a solution for
parameters of real plant devices in both count and scale, the research issue RI-2, i.e. to create a simulation model
every generic simulation block has the parameters described for a passive house, whose floor plan is drawn in House
in the simulation ontology. A special kind of parameters Builder WPF software. The simulation model is created by
are initial conditions that are managed in a similar way selecting and interconnecting generic simulation blocks from
as parameters in our approach. Known parameters of real a universal library Bldsimlib, implemented in MATLAB-
plant devices are stored in a plant ontology. Nowadays, the Simulink. This task is challenging as House Builder WPF
translation of plant parameters to simulation parameters have and the universal simulation library have heterogeneous data
not been satisfactorily solved in the presented prototype. models.
Therefore, the semantic engine and the parser are referred Figure 6 depicts a use-case passive house floor plan in
as semi-automatic, meaning that structural issues are solved House Builder WPF tool. The use-case passive house is
automatically but the parameters are managed manually. a simplified house, consisting of two rooms, walls and
While an automation ontology is created, the seman- interior equipment. As it is very simplified, it enables to
tic engine can semi-automatically assemble the simulation show our approach in a clear way and it does not pose
model for a particular plant. It finds all real plant devices any restriction in generality. An exemplary piece of the
(i.e. individuals of the plant ontology) and according to the House Builder config.xml file is shown in Fig. 5. The
ontology property “simulates”, a set of simulation candidates figure depicts the House Builder representation for the left
and their interfaces are retrieved for each plant device via room of the passive house floor plan. Rooms can be found
SPARQL queries. Consequently, the interconnections are set via keyword RoomPolygon, whereas walls via keyword
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First Workshop on Industrial Automation Tool Integration for Engineering Project Automation
Universal library for environmental quantities modeling of residential buildings
Objects in rooms Zones (rooms, exterior, ground) Interactions between zones and ventilation
N_man out 1 1 1 1
N_woman Interaction Exhaust fan
Human out t (deg C) 2 2 2 2
N_child
interaction exhaust
K_activity ROOM CO2 (ppm)
human 1 1
RH (%) Extender 1 1
2 O/C Window 2 Ventilator
p (kPa) {0;1} 2 <0;1> 2
out
Heat source ocwindow ventilator
heat (W) bldsimlib_room
heat 1
out 1 1 1
Window 2 Damper
t (deg C) extender 2 2 2
Universal source <0;1>
CO2 (ppm) bldsimlib_window damper
source EXTERIOR RH (%)
1 1
p (kPa) 2 Interior DOOR Exhaust
Gas cooker sun {0;1} 2 Exhaust
bldsimlib_door Outlet
cooker wind HVAC unit
bldsimlib_exterior 1 Source
1
Plant WALL 2 Source
2
t (deg C) Inlet
Figure 6. Floor plan of the use-case passive house created in House plant
out bldsimlib_wall
hvac
t (deg C)
Builder WPF software. Bath/shower Ground CO2 (ppm)
1
Leakage
1
1
RH (%) 2 (ext) (ext) 2 Ext
bathroom leakage
p (kPa) p_1 Blower door tester 1
1 1 p_2
bldsimlib_ground
Leakage (simple) p_3
2 2
blower_door_tester
LinePoint. First step of our solution is a parsing of leakage_s
House Builder config.xml file that gives information
consequently used to create individuals of passive house Figure 7. High-level overview of a universal simulation library Bldsimlib
in MATLAB-Simulink.
ontology, i.e. the instances of rooms and walls are created
and their interconnections are inserted as object ontology
properties. The presented version of the algorithm supports
rooms and walls only, but the functionality is planned to be
extended for further elements support. Some elements must exceeds the size of this paper, we will address it in future
be recognized in a non-intuitive way, as House Builder does work. The high-level overview of the used Bldsimlib library
not express them. For example, there is no graphical element is depicted in Fig. 7. The emphasized generic simulation
representing doors or windows, but there are so-called blocks are supported in the current version, i.e., simulation
sensors measuring and visualizing position of sun-blinds blocks of exterior, room, and wall are used in generated
or position of door. This is done by intended functionality model.
of House Builder WPF oriented on passive houses, where
windows are expected usually equipped with sun-blinds and The semi-automatically generated model of the passive
furthermore, the floor plan need not to be absolutely exact house is depicted in Fig. 8. The upper left block represents
in all details as it monitors the operation of the whole an exterior, i.e., it defines borderline conditions for the
automation system of the house. simulated house. Simulation blocks in the central column
The parser reads the config.xml file and for known represent rooms and the blocks in the right part of the figure
keywords creates plant ontology individuals. The parsing is approximate walls. The depicted simulation model does not
done via Java code and creating individuals is realized by export its outputs into a file or MATLAB Workspace and
ARQ/Jena methods. Another alternative would be to express although it would be possible to set all variables as outputs
House Builder WPF elements in a specialized ontology and automatically, we expect either the user of a simulation
map its concepts onto plant ontology, but the first approach model would define the outputs or in the planned extended
was used for this prototype as it is easier and reaches version, the outputs will be defined by ontology property
satisfactory results. “measures” as well as the inputs will be entered via ontology
While having the passive house representation in the property “controls”. In this use-case simulation model, a
plant ontology, that is a tool-independent representation of limitation of the current implementation is apparent: The
the object, the semantic engine generates the simulation walls that separate two same areas are not merged into one
model. The simulator-independent part of the engine is wall. In other words, each room in the generated model has
implemented in Java. It is called from supporting MATLAB four walls, but they could be merged into two walls without
script, where simulation blocks and signal interconnections changing the functionality - one wall to exterior and one to
are entered via MATLAB API using functions add_block the other room. This issue could be solved on the level of the
and add_line. As we assume that there is available a configuration file parser, but we consider this problem being
simulation library comprising generic simulation blocks, general. As it occurs in many real-life systems, we plan to
the creating of models means selecting appropriate library handle this situation on a plant ontology level. The problem
blocks, entering them into a simulation model file, setting exceeds this paper; it is planned for future work. Last but not
their name uniquely and interconnecting them according least, the positions of blocks are done by a rectangular matrix
to the real plant structure. To run the simulation model, and signal wires use auto-routing available in MATLAB; the
also simulation parameters must be added, but as this issue positioning is planned to improve.
17
First Workshop on Industrial Automation Tool Integration for Engineering Project Automation
that is the Excel script widely used in civil engineering
out 1
t (deg C)
out 1
WALL 2
to calculate and evaluate thermal and other properties of
2
CO2 (ppm)
t (deg C) t (deg C)
wall101
passive houses. Other future work issues are the support for
EXTERIOR RH (%)
ROOM CO2 (ppm)
1
1 simulation model input and output management, that will
WALL 2
p (kPa) RH (%)
2 t (deg C) work not only on the simulation model and visualization
sun wall102
wind
p (kPa)
1
level but as well on the run-time level. Further future work
m101 1
ext
2
WALL 2 topics are a positioning of simulation blocks and signal
out t (deg C)
wall104 wires to obtain better readability for humans, and automatic
t (deg C)
1
1
WALL 2
merging simulation blocks on the ontology level.
ROOM CO2 (ppm) 2
t (deg C)
RH (%) wall105 ACKNOWLEDGMENTS
1
p (kPa) 1
m102
2
WALL 2
t (deg C)
The authors would like to thank to the partners from
wall106 the Christian Doppler Laboratory for Software Engineering
1
1
WALL 2 Integration for Flexible Automation Systems for the discus-
2 t (deg C)
wall107
sions and feedbacks. This work has been supported by the
1 1 Christian Doppler Forschungsgesellschaft and the BMWFJ,
WALL 2
2 t (deg C) Austria.
wall103
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