OntoENERGY – A Lightweight Ontology for Supporting
Energy-efficiency Tasks
Enabling Generic Evaluation of Energy Efficiency in the Engineering Phase
of Automated Manufacturing Plants
Tobias Linnenberg
1
, Andreas W. Mueller
2
, Lars Christiansen
1
, Christian Seitz
2
and Alexander Fay
1
1
Helmut Schmidt University, Department of Mechanical Engineering, Hamburg, Germany
2
Siemens AG, Industry Sector, Advanced Technologies & Standards, Nuremberg, Germany
Keywords: Energy Domain Knowledge, Energy Efficiency, Energy Conservation, Knowledge Acquisition, Energy
Management, Ontology, Energy Engineering.
Abstract: To facilitate the automated evaluation of energy-efficiency aspects of a system in the early lifecycle phase
of engineering, a consistent semantic definition of the relevant terminology as well as the interrelations
between those terms is required. For this purpose, a lightweight ontology named OntoENERGY has been
developed, which allows for continuous handling of energy-efficiency issues in technical systems
throughout their entire lifecycle. To verify OntoENERGY, a simulation model based on a real test bed of an
automated plant process was modeled, analyzed, and assessed, with a focus on energy consumption and the
related information. This allows optimization potential to be identified and enables a direct assessment, with
the aid of a simulation model.
1 INTRODUCTION
Resource efficiency and energy efficiency are both
intensely discussed topics today, important not only
in the context of the impending climate change, but
also with regard to the turnaround in energy policy
promoted by the German government. This topic
requires new and enhanced methods to support the
development of efficient systems (DECHEMA,
2009).
Energy is a special resource, occurring in
different forms and quantities, and required
throughout industrial systems. In general, energy
efficiency therefore represents a special kind of
resource efficiency. Technical systems, such as
electrical installations, hydraulic systems, air
compressors, and thermal systems all feature
domain-specific characteristics requiring
differentiated approaches in design and analysis to
permit efficient operation. In this paper, we focus on
the area of energy efficiency.
Energy-efficient tasks can be integrated into all
phases of the product lifecycle. In the design and
planning phase of a product, simulation tools are
already used. These tools enable the most
appropriate automation equipment to be selected and
the optimal production process to be defined. In the
operation phase, service tools for diagnostics and
predictive maintenance could reduce the dissipation
of energy and resources. Unfortunately, these tools
are not coordinated with each other. To achieve this,
a common understanding of data is necessary.
When it comes to capturing the required energy-
related information, the fields of energy
(management) systems and automation systems offer
a wide spectrum of perspectives and glossaries with
a great variety of possible interpretations. An
explicit definition of the terms common to these
fields of application as well as the formalization of
their correlations is therefore essential in order to
facilitate a sound analysis and understanding of the
energy efficiency of a system design. It is also
necessary for the identification of optimization
potentials and precise communication about these
aspects. With a view to the field of digital
engineering and the tasks taking place within the
digital factory context (Chryssolouris et al., 2009),
we consider it advisable to enable the required
software tools to handle these aspects in an
automated and integrated way. This becomes even
more important when it comes to the ongoing
337
Linnenberg T., W. Mueller A., Christiansen L., Seitz C. and Fay A..
OntoENERGY A Lightweight Ontology for Supporting Energy-efficiency Tasks - Enabling Generic Evaluation of Energy Efficiency in the Engineering
Phase of Automated Manufacturing Plants.
DOI: 10.5220/0004622903370344
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2013), pages 337-344
ISBN: 978-989-8565-81-5
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
demand for the integration of plant IT systems based
on a homogeneous syntax and semantics (Sauer,
2010).
Here, ontologies depict a suitable tool for
achieving this objective. Using ontologies, it is
possible to structure knowledge in a manner that can
be read by both machines and humans. This allows
automated and distributed processing and analysis of
energy-related information. Thus, defining and
providing the required vocabulary is the first step
toward facilitating engineering tools to perform
automated reasoning on the soundness of system
engineering measures. We therefore developed
OntoENERGY as an easy-to-use vocabulary for the
field of energy efficiency.
Although motivated by industrial needs in the
field of automation systems in the manufacturing
industry, OntoENERGY will be applicable to any
domain with the need for the evaluation of energy-
efficiency issues.
The structure of the paper is as follows. Section 2
provides an overview of related work. Section 3
presents the relevant fundamentals of the energy
domain. In Section 4, the design of OntoENERGY is
introduced, and Section 5 describes a first
application scenario. A short synopsis in Section 6
concludes the paper.
2 RELATED WORK
When approaching knowledge formalization in the
field of energy efficiency, it is essential to consider
existing standards in this domain, especially since
our work is motivated by industrial needs. The EU
directive 2006/32/EG “Energy Service Directive”
(European Parliament, 2006) and, derived from that,
the German energy conservation law
(“Energieeinsparungsgesetz”), as well as the
German energy conservation regulations
(“Energiesparverordnung”), based on the latter, can
be considered as the most important. These provide
mandatory minimum energy-efficiency requirements
for real estate and property owners and other energy
aspects. They apply to residential buildings, office
buildings, and certain industrial facilities, taking into
account not only the installed equipment but the
entire balance of energy creation, storage, and
handling. The energy flows are evaluated using
primary energy factors.
The VDI directive 4661 “Energetic
characteristics” (VDI, 2003) is designed to provide a
broad and fundamental, uniform definition of the
terminology encountered in the energy economy.
In order to capture and process energy-related
information fully automatically, a semantic
definition of the required terminology of the energy
domain is required. However, from the perspective
of the authors, there is no distinct ontological
formalization of the fundamental terminology that is
required for consideration of energy efficiency in
general, and that can be directly applied to both
automation and non-automation application
domains. Nevertheless, several previous works can
be used as reference to energy-related ontologies.
(Borst et al., 1995) defined an extensive ontology for
the modeling of physical systems. Here, energy is
covered as an integral part in a generic but purely
physical view, which does not suffice for capturing
the information in the energy-efficiency context. On
the other hand, (Zeiler et al., 2009) limited their
approach to problems related to energy conversion
processes in the building and infrastructure domains.
Coming from this domain as well, the more
extensive ontology by (Wicaksono et al., 2012)
offers the possibility of capturing knowledge about
(discrete) manufacturing processes as they relate to
energy management. However, their work is on the
level of domain ontology. Thus, these approaches
have two drawbacks in common: They are only
scalable within the designated range of applications,
and their generalization, i.e. transfer and application
to other domains, is difficult.
Comprehensive upper ontologies like SUMO
(Niles and Pease, 2001) or SWEET (NASA, 2012)
also define energy-related terms. Although their
definitions are intended for generic use, both lack
important aspects required in the energy-efficiency
context. Also, their implementations do not permit
easy subsetting for special application purposes.
3 FUNDAMENTALS
OF THE ENERGY DOMAIN
Regardless of the respective field of application,
there are many recurring aspects within the energy
domain, which are considered as equal for all basic
use cases, e.g. analyses. Therefore, the
understanding of the fundamental terminology in the
context of power engineering and energy economy,
required for energy-efficiency support, forms the
basis of OntoENERGY. Hence, this section provides
the most important definitions, extending the
industry-related VDI directive (VDI, 2003), which
was chosen because of its maturity. Furthermore,
these definitions can be based on fundamental
mathematical relations.
KEOD2013-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
338
3.1 Basic Definitions
Energy constitutes a fundamental physical variable,
denoted by the SI unit of Joule [J] or alternatively
Watt seconds [Ws]. According to the first law of
thermodynamics – the law of energy conversion –
energy can neither be created nor annihilated. Thus,
energy cannot be “consumed” but only converted.
Furthermore, a complete conversion of one form of
energy into another is impossible according to the
second law of thermodynamics. It states that energy
conversion processes are always connected with
thermal losses and the generation of entropy. In this
context, we distinguish between three major
interpretations of the term energy.
The physical interpretation, which serves as a
generic clause for the electrical, chemical,
thermal, and mechanical forms of energy.
The industrial interpretation, which categorizes the
different forms of energy according to their
appearance as primary energy and secondary
energy.
The automation-related interpretation, which aids
in the understanding of manufacturing processes
and hence requires a qualitative differentiation of
the energy involved. Thus, we distinguish between
the following four forms of energy:
Product energy, which is contained in the
product itself. In a production process, it
describes the energy contained in a work piece,
e.g. thermal energy after a heating period or
potential energy in high-rise storage.
Process energy, which is brought into the
process and therefore affects the work piece. The
energy content of a thermodynamic or
mechanical system is controlled through process
energy. Hence, any instance of process energy is
accompanied by a change in product energy.
Resource energy, which depicts the “base load”
of all consumers involved in the process. Such
consumers support the process at least through
subsidiary actions or the supply of energy.
Examples are air compressors, transformers,
automatic control systems, lighting systems,
ancillary units, and heating, ventilation, and air
conditioning systems (HVAC).
System energy, which represents the overall
amount of energy of the entire automated system
considered in the analysis. It embodies the total
of all energy amounts and energy flows in the
system.
Energy demand is a goal, based on the
possibilities to satisfy it. Thus, it is defined as the
amount of energy required to satisfy a goal or to
produce a good with the aid of an appropriate
technology under defined circumstances. For
purposes of comparison, this is the quantification of
an energy demand projected onto forms of primary
energy carriers like coal, oil, or gas – the so-called
primary energy factor.
The cumulated total energy demand of a system
is determined by the total of all energy demands as
required by all elements of the system (resource
energy) and the process-related energy demand
(process energy) (Verl et al., 2011). A detailed
analysis of the total energy demand allows for the
distinction between the steady use of resource
energy and the fluctuating consumption of process
energy, and consequently for the differentiation of
fixed and variable elements of the energy demand.
Additionally, processes and components that are not
directly related to the main process, e.g. air
compressors, lighting, or HVAC systems (Dietmair
and Verl, 2008), still contribute to the total energy
demand. Such energy-consuming processes cannot
be allocated to a single process and thus need to be
treated as a common energy demand. Finally, the
actual energy demand is determined by the
respective mode of plant operation (Dietmair and
Verl, 2008).
Since energy cannot actually be consumed in the
first place, there is no consistent definition for the
commonly used term energy consumption in general
literature. In (VDI, 2003), the term “delivered
energy” is used, which describes the total energy
content of primary and secondary energy carriers
delivered to the consumer. From an external point of
view, the “energy consumption” can thus be
regarded as the energy delivered to a system. Aside
from that, (VDI, 2003) also defines energy
consumption as the “quantity of particular forms of
energy consumed in order to cover energy demands
under real conditions.” Essentially, energy
consumption refers to the amount of actually
converted energy that has to be applied in order to
reach a given goal. This depends heavily on different
external and internal factors. According to (Dietmair
and Verl, 2008), the current system state (e.g. idle,
active, shut-down, etc.) can be regarded as the most
important influence. Thus we can differentiate
between fixed and variable parts of the amount of
consumed energy – similarly to the energy demand.
3.2 Mathematical Correlations
In the following, the basic definitions depicted
above are extended by fundamental mathematical
correlations. These illustrate physical and
OntoENERGY-ALightweightOntologyforSupportingEnergy-efficiencyTasks-EnablingGenericEvaluationofEnergy
EfficiencyintheEngineeringPhaseofAutomatedManufacturingPlants
339
economical correlations necessary to understand and
analyze system performance with regard to energy
efficiency.
Anergy represents the operationally unusable part
of the energy, which may therefore be called “lost
energy.” At the other end of the spectrum is exergy,
which is the usable part of energy because it is
convertible to usable work (Rudolph and Wagner,
2008). Exergy and anergy together form the total
amount of energy, describing the working capacity
of a system.
energy = exergy + anergy (1)
The term efficiency, especially energy efficiency, is
defined by various norms and directives (VDI,
2003), (European Parliament, 2006), which can be
subsumed by equation (2).
effort
revenue
efficiency
(2)
Efficiency is the ratio of an energy-equivalent system
output (the revenue) to the supplied energy input
(effort) within a discrete time- or state space.
Obeying the first and second law of
thermodynamics, stating that technical processes are
always subject to energy losses, this ratio ranges
between [0, 1[. Efficiency is the most important
factor when evaluating the performance of a system
in terms of energy. This means that energy
efficiency is the realization of an energy-related
(conservation) goal met by a predefined effort.
Consequently, energy dissipation can be
expressed as the difference between the actual
demand and the target demand in the current system
state.
dissipation = actual demandtarget demand (3)
Regarding the use of the value of dissipation for
simulation purposes, for example, it is important to
note that deviations of these two values may be
induced by modeling errors or required
simplifications. These abstractions must be regarded
in detail when faults need to be identified by means
of simulation.
4 DESIGN OF THE OntoENERGY
ONTOLOGY
Having presented the terminological prerequisites,
we will now describe the basic requirements for
OntoENERGY, followed by the design decisions
and their implementation.
4.1 Requirements
The main objective of OntoENERGY is to define
semantics of the fundamental physical quantities and
their interrelations as found in the energy domain.
Although the use case initially addressed was the
energy-efficiency evaluation of automated processes
in order to identify related potential shortcomings
and pitfalls already in the early plant engineering
phase, we envision that it is applicable to any
application or domain in which such energy analyses
are needed. From this, we derived the following
basic requirements, each of which has equal
importance:
Applicability: It must be possible to directly apply
the ontology to energy-efficiency related analyses
on factory automation machinery and their
operation.
Extensibility: It must be possible to consistently
upgrade the ontology in subsequent usage
scenarios.
Portability: It must be possible to directly apply
the ontology to different domains or port it into
proprietary software tools with various use cases
and conditions in the field of energy efficiency.
Hence, our requirement was that OntoENERGY
must act as a lightweight upper ontology that
application- or domain-specific ontologies can easily
build upon. Furthermore, it must be easily
integratable into various software tools, such as
plant design tools or energy management systems.
4.2 Design Decisions
With the goal of creating an upper ontology, the
focus of our work was on the TBox level, reaching a
high degree of abstraction and support of domain-
independent use cases. Several basic decisions were
made in order to achieve a clear and understandable
hierarchization of the terminology introduced in
Section 3.
First, OntoENERGY should, insofar as possible,
be usable as a single, small, stand-alone ontology
without external dependencies, in order to be easily
portable and integratable. The resulting
hierarchization can be found in Figure 1.
Second, the distinction of the three main
interpretations of energy (physical, industrial, and
automation) should be retained. These are used to
sub-classify the associated forms of energy.
Third, emphasizing OntoENERGY’s objective of
supporting energy-efficiency analysis, the quantity
of energy dissipation is regarded in this
context as the most important result of a s ystem’s
KEOD2013-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
340
Figure 1: Concept hierarchy of OntoENERGY.
energy- efficiency evaluation. In OntoENERGY, it
shall be classified as “non-productive consumption”
of energy. This is due to the nature of energy
dissipation, which stands for “consumption” of
energy without increasing the value of the absorbing
system.
Fourth, mathematical correlations are treated as
terminological and modeled in the ontology in a way
similar to that in SWEET (NASA, 2012), but in a
compact and simplified form using explicitly
defined roles, for example divisor and dividend as
allocated operands of a division. The elaborated
mathematical operations may be found on the
bottom of Figure 1. Although full-fledged
representations of mathematical concepts that allow
for arbitrarily complex expressions exist, for
example MathML (Carlisle et al., 2010), using these
was considered unsuitable, since it would not meet
the goal of easy applicability, and furthermore
would exceed the scope of this work.
For implementing OntoENERGY, the Web
Ontology Language (OWL) (Bechhofer et al., 2004)
was chosen for practical reasons, especially because
of its inherent support for automated reasoning and
the tools available for coupling to third-party
software.
4.3 Implementation
The definition of the overall structural and
conceptual hierarchy of OntoENERGY followed the
terminology and its relational structure as described
in Section 3 (see Figure 1).
The concept Energy subsumes the subconcepts
Physical, Industrial and Automation, representing
the three interpretations of energy. These in turn
subsume concepts representing the respective forms
of energy. With their aid, tools can perform detailed
energy analyses. Additionally, the concepts of
Exergy and Anergy allow for assessments of energy
quality.
The concepts Supply (as the “effort”), Demand,
and Consumption (as the “claim”) are used for
describing the different processes of energy
exchange and for subsuming various quantities
while providing key performance indicators. Here,
Supply is of particular importance in order to
identify and categorize the sources and sinks of
energy. Further, Dissipation_Of_Energy, as stated
previously, is allocated to the Consumption
superconcept.
Mathematical correlations involved in
calculating different target values are subsumed
under the mathematicalOperator concept. Due to the
missing support of ternary relations in OWL (W3C,
OntoENERGY-ALightweightOntologyforSupportingEnergy-efficiencyTasks-EnablingGenericEvaluationofEnergy
EfficiencyintheEngineeringPhaseofAutomatedManufacturingPlants
341
2006), their respective semantics have been modeled
using a hierarchy of object properties (see Figure 2),
with adequately defined domains and ranges, for
representing the roles the operands play in applying
the operators.
Figure 2: Object property hierarchy for explicit
mathematical correlations.
Hence, for instance, the calculation of energy
efficiency is represented as
efficiency_division division
(=1 efficiency_has_dividend.Consumption)
(=1 efficiency_has_divisor.Supply)
Energy_Efficiency (=1
efficiency_is_division_of.
efficiency_division)
with the roles of all entities and also the formula
involved being specified.
In order to analyze courses of energy
consumption that occur for example during plant
operation in general or execution of certain
processes in particular, it is necessary to capture
basic temporal information about the occurrences of
values. Therefore, the basic concepts of Duration
and Timepoint were borrowed from the Process
Specification Language (PSL) (ISO, 2006).
For representing the quantities’ units,
units.owl of SWEET V1.1 (NASA, 2006) was
included as the sole external dependency. This is
justifiable, since this was also designed as a stand-
alone ontology, and thus no further external
references would be needed (in contrast to newer
SWEET versions).
5 APPLICATION
OF OntoENERGY
For application and proof of concept of
OntoENERGY, the authors used a Siemens research
facility in Nuremberg (see Figure 3), providing
detailed energy usage information and employing a
hybrid process in combination with a discrete
simulation model, and for which a semantic model
describing the plant structure and the process exists.
Figure 3: Siemens research facility.
The facility consists of four modules B1-B4, each
consisting of a conveyer belt, a drive system
(electrical engine and frequency converter), and
various sensors. It is controlled by one
Programmable Logic Controller (PLC). Moreover,
there are energy meters for measuring the actual
energy consumption of each module. The purpose of
this facility is to generate an arbitrary left-to-right
text, produced from small plastic disks, comparable
to an electronic continuous text. Therefore, this
process employs combinations of moving and
stopping the electrical engines of B1 and B2 to
create columns, resulting in the desired letters. By
transporting the columns from B2 to B3 and discrete
movement of B3, the letters are generated, which
eventually results in a complete text.
As described in Section 3, the actual energy
consumption depends on the modules’ different
modes and corresponding states during operation.
Here, the demonstrator defines three different states.
First, the state stand-by denotes the fact that the
whole system is supplied with energy but no actual
process is carried out. The second state, idle,
represents that only the cooling system of the
frequency converter is running; again no process is
carried out. The third state describes the actual
execution of the process that properly moves the
conveyer belts. For assigning the states of the
demonstrator to the energy concepts of
OntoENERGY, both the stand-by and the idle states
KEOD2013-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
342
refer to the concept Resource Energy. The concept
Process Energy is assigned to the state process.
For energy evaluation, a reference process was
examined in which all three states are addressed and
the values described by OntoENERGY are
delivered. If the reference process is started, the
evaluation module in the simulation tool identifies
the currently active energy state and the energy
consumption associated with this state. The
evaluation system can determine the upper and
lower bounds for the energy states. The information
is then passed on to consumer applications with
energy-related tasks.
Figure 4: Detail of TBox extension with OntoENERGY.
Figure 5: Detail of process knowledge annotation.
Since the reference process was executed several
times with different rates, a corresponding power
consumption curve exists for each velocity. With
this evaluation mechanism, a complete energy report
can be created that contains all information about the
energetic behavior of each supervised component.
This can be used to optimize or configure the
system.
Because the primary intended use of
OntoENERGY is the integration of different
software tools within the field of industrial
applications, a system setup with OntoENERGY
resembles that in Figure 6. The arrows denote
information flow (black: “raw” energy information,
grey: semantically enriched information, which can
be utilized for information interchange and also
directly used by tools already based on semantic
technologies), while the grey boxes denote
components realized by the authors and the white
box depicting an outlook for use of OntoENERGY-
based information.
Using such setup, OntoENERGY enables the
vertical integration of measured energy data, while
at the same time permitting energy-related
interactions between applications (horizontal
integration).
Figure 6: General system setup using OntoENERGY as
common vocabulary (outline).
6 CONCLUSIONS
AND OUTLOOK
The application of a simulation model in the
engineering phase features two distinct advantages:
In the context of long-term simulations, it can be
used to predict future energy usage scenarios of
single processes or entire plants on the one hand,
and to evaluate planned process updates or the
deployment of energy-efficient components and
drives on the other hand. These energy-efficiency
related analyses can be performed without changing
the real-life test bed or disturbing the manufacturing
process. Furthermore, the simulation model,
reflecting the facility and process, helps to identify
potentials for optimization and for developing and
testing new engineering concepts as they relate to
energetic aspects.
To facilitate the integration of process simulation
tools and their energy-related results into the digital
factory as well as a differentiated analysis and
communication of energy consumption processes
therein, a distinct terminological foundation and a
definition of the energy-engineering related
coherences is needed.
Requirements derived from these use cases were
taken as the basic objectives when designing
OntoENERGY. Thus, it features a precise semantic
definition of the terminology as found in the energy
domain. This allows for qualitative and quantitative
allocation of different forms of energy throughout all
OntoENERGY-ALightweightOntologyforSupportingEnergy-efficiencyTasks-EnablingGenericEvaluationofEnergy
EfficiencyintheEngineeringPhaseofAutomatedManufacturingPlants
343
engineering phases, using a consistent information
model.
In order to cover a wide spectrum of different
domains, the following three goals were regarded as
equally important: 1. Applicability on factory
automation machinery and its operation.
2. Extensibility and upgradeability in subsequent
usage scenarios. 3. Portability and applicability to
different domains or proprietary software tools.
Consequently, OntoENERGY has been realized
as a universal lightweight upper ontology, allowing
for individual adaptations while providing all
necessary means for deploying it right out of the
box. It can be easily integrated into future software
tools and methodologies. The applicability of
OntoENERGY to a process has been demonstrated
on a real-life manufacturing domain test bed. Further
steps will include the linking to third-party software
tools and adaptations to specific domain
requirements.
REFERENCES
Bechhofer, S., Van Harmelen, F., Hendler, J., Horrocks, I.,
McGuiness, D. L., Patel-Schneider, P. F., Stein, L. A.,
2004: OWL Web Ontology Language Reference. URL
http://www.w3.org/TR/2004/REC-owl-ref-20040210/.
– accessed 19.04.2013.
Borst, P., Akkermans, J.M., Pos, A., Top, J., 1995. The
PhysSys ontology for physical systems. In Working
Papers of the Ninth International Workshop on
Qualitative Reasoning QR, pp. 11–21.
Carlisle, D., Ion, P., Miner, R., 2010. Mathematical
Markup Language (MathML) Version 3.0. URL
http://www.w3.org/TR/MathML3/. - accessed
23.04.2013.
Chryssolouris, G., Mavrikios, D., Papakostas, N.,
Mourtzis, D., Michalos, G., Georgoulias, K., 2009.
Digital manufacturing: history, perspectives, and
outlook. In Proceedings of the Institution of
Mechanical Engineers, Part B: Journal of
Engineering Manufacture. Vol. 223, pp. 451–462.
DECHEMA e.V., 2009. Chemieanlagen: ‚Operational
Excellence‘ ist das Ziel: Trendbericht Nr. 3:
Chemieanlagen-Konzepte.
Dietmair, A., Verl, A., 2008. Energy Consumption
Modeling and Optimization for Production Machines.
In Proceedings “International Conference on
Sustainable Energy Technologies (ICSET)”, pp. 574-
579.
European Parliament, 2006. Directive 2006/32/EC on
energy end-use efficiency and energy services and
repealing Council Directive 93/76/EEC.
ISO, 2006. ISO 18629: Industrial automation systems and
integration - Process specification language - Part 1:
Overview and basic principles.
NASA, 2012. Semantic Web for Earth and Environmental
Terminology (SWEET). URL http://sweet.jpl.nasa.gov.
- accessed 21.04.2013.
NASA, 2006. Semantic Web for Earth and Environmental
Terminology (SWEET) V1.1: units.owl. URL
http://sweet.jpl.nasa.gov/1.1/units.owl. - accessed
21.04.2013.
Niles, I., and Pease, A., 2001. Towards a Standard Upper
Ontology. In Proceedings of the 2nd International
Conference on Formal Ontology in Information
Systems (FOIS-2001). Ogunquit, Maine, October 17-
19, 2001.
Rudolph, M. and Wagner, U., 2008.
Energieanwendungstechnik. Wege und Möglichkeiten
zur Optimierung der Energietechnik. Springer. Berlin.
Berlin 2008
Sauer, O., 2010. Trends in manufacturing execution
systems. In Proceedings of the 6th CIRP-Sponsored
International Conference on Digital Enterprise
Technology, pp. 685–693.
VDI, 2003. VDI-Directive 4661: Energetic characteristics
Definitions – terms – methodology.
Wicaksono, H., Rogalski, S., Ovtcharova, J., 2012.
Ontology Driven Approach for Intelligent Energy
Management in Discrete Manufacturing. In
Proceedings of 4th International Conference on
Knowledge Engineering and Ontology Development.
Barcelona, Spain, pp. 108-114.
World Wide Web Consortium (W3C), 2006. Defining N-
ary Relations on the Semantic Web. URL
http://www.w3.org/TR/swbp-n-aryRelations/. -
accessed 05.03.2013.
Verl, A., Westkämper, E., Abele, E., Dietmair, A.,
Schlechtendahl, J. F.: Architecture for Multilevel
Monitoring and Control of Energy Consumption. In
Hesselbach, J., Hermann, C. (ed.): Glocalized
Solutions for Sustainability in Manufacturing.
Proceedings of the 18th CIRP International
Conference on Life Cycle Engineering, Technische
Universität Braunschweig, Braunschweig, Germany,
May 2nd - 4th, 2011, pp. 347-352.
Zeiler, W., van Houten, R., Boxem, G., Savanovic, P., van
der Velden, J., Wortel, W., Haan, J.-F., Kamphuis, R.,
Hommelberg, M., Broekhuizen, H., 2009. Flexergy.
An ontology to couple decentralised sustainable
comfort systems with centralized energy
infrastructure. In Proceedings of 3rd International
Conference on Smart and Sustainable Built
Environments (SASBE).
KEOD2013-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment
344