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

, Andreas W. Mueller

, Lars Christiansen

, Christian Seitz

and Alexander Fay

Helmut Schmidt University, Department of Mechanical Engineering, Hamburg, Germany

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

 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.

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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

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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 demand – target 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

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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

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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

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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

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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).

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