Ontology Driven Approach for Intelligent Energy Management in
Discrete Manufacturing
Hendro Wicaksono
, Sven Rogalski
and Jivka Ovtcharova
Intelligent Systems and Production Engineering/ Process and Data Management in Engineering,
FZI Research Center for Information Technology, Haid-und-Neu Str. 10-14, D-76131, Karlsruhe, Germany
Institute for Information Management in Engineering, Karlsruhe Institute of Technology,
Zirkel 2 Building 20.20, D-76131, Karlsruhe, Germany
Keywords: Energy Efficiency, Energy Management, Knowledge Acquisition, Machine Learning, Manufacturing,
Abstract: In recent years ontologies have been used for knowledge representation in different domains, such as energy
management and manufacturing. Researchers have developed approaches in applying ontologies for
intelligent energy management in households. In the manufacturing domain, ontologies have been used for
knowledge management in order to provide a common formal understanding between the stakeholders, who
have different background knowledge. Energy management in a manufacturing company involves different
organizational entities and technical processes. This paper proposes an approach to applying ontology for
intelligent energy management in discrete manufacturing companies. The ontology provides a formal
knowledge representation that is accessible by different human stakeholders as well as machines in the
company. This paper also demonstrates the methods used to construct and to process the ontology.
Facing a politically challenging future - determined
by environmental targets, the finite nature of fossil
energy and a constant population increase - energy
and resource efficiency has developed into one of
the most crucial issues of the 21st century.
Following industrialisation, especially in countries
with emerging markets, there has been a significant
increase of energy demands in the past decade.
Besides the ecological and social motivation,
costs also play a decisive role. Products with many
variants have made processes more complex. These
are often very energy intensive and therefore
expensive. This implies that on one hand,
manufacturers need to be flexible in order to satisfy
these diverse demands. On the other hand, customers
demand high quality and often more precise
products (Kinkel, 2005). In addition, increasing
energy prices reduce the revenue span. Hence,
energy efficiency in accordance with the
economization of production costs is an important
competitive factor in the energy-intensive industries,
such as manufacturing.
A suitable solution to address this problem is the
introduction of corporate energy management. This
can be established depending on the size and energy
intensity of the company. Energy management
defines the sum of all processes and measures which
are developed and implemented to ensure minimal
energy consumption with the given demand. An
energy management system (EnMS) is a systematic
way to define the energy flows and serves as a basis
for decisions to improve energy efficiency. An
EnMS includes the implementation of organization
and information structures that are necessary for
energy management, including the required tools for
this purpose (Kahlenborn et al., 2010). The standard
ISO 50001 describes the requirements for energy
management systems for industrial companies.
Most manufacturing companies face problems in
implementing the energy management standards.
There are often no standardizations in their operation
portfolios (plants, sites, etc.). The energy consuming
production processes, building infrastructures, as
well as power plants are documented and managed
separately and in an unstructured manner.
Management has little insight into the usage of
energy in the operation, due to the knowledge gap
between managers and operators. Managers do not
Wicaksono H., Rogalski S. and Ovtcharova J..
Ontology Driven Approach for Intelligent Energy Management in Discrete Manufacturing.
DOI: 10.5220/0004141601080114
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2012), pages 108-114
ISBN: 978-989-8565-30-3
2012 SCITEPRESS (Science and Technology Publications, Lda.)
have the tools to manage the energy usage over
different vertical and horizontal levels of the
organization. Operators often do not realise whether
their activities and decisions create excessive energy
usage or demand. Best practices to avoid energy
wastage are only known by some employees, but the
other employees do not have access to this
An important step in energy management is to
measure the energy consumption at different levels
of granularities. This can be done using data
acquisition systems. The framework of a data
acquisition system contains signals, sensors, signal
conditioning, hardware and a computer with
software. Due to the various technologies of data
acquisition systems, connectivity and
standardization are important issues to establish an
automated energy management system. Most of the
meters have to be read manually and thus it presents
problems in accuracy.
The energy data acquisition systems are not
integrated with other IT systems, such as Enterprise
Resource Planning (ERP) or Material Execution
System (MES). It is difficult to relate the energy
consumption and demand with manufacturing
operations, employee activities and business
processes. It causes an inaccuracy of energy cost
allocation to the produced goods or services.
This paper proposes an ontology based approach
to solve the problems mentioned above and at the
same time providing a knowledge base that can be
accessed by intelligent systems.
For the last ten years researchers have been applying
ontologies as knowledge representation in various
domains, such as medicine, agriculture, biology,
software engineering, etc. Ontology has proven to be
a solution to the shared understanding problems
among people and even software. It is used to
harmonize the knowledge gap between customers
and manufacturers during the requirement elicitation
in the pre-contract phase of the product lifecycle
(Wicaksono et al., 2012). An ontology driven
approach is also used to detect semantic ambiguities,
uncertainties and contradictions in business and IT
service management, therefore it overcomes the
business gap between many IT service providers
(Valiente et al., 2012).
In the energy management domain, ontology has
been utilized as a representation of the knowledge
base for an intelligent system that monitors and
controls the energy consumption in a household
(Wicaksono and Rogalski, 2010). The rules
represented in SWRL are integrated in the OWL-
Ontology. Wicaksono, Rogalski, and Kusnady
(2010) developed an approach to allow the semi-
automatic generation and instantiation of ontology
elements using a machine learning algorithm
(Wicaksono et al., 2010). Ontology is also used to
classify home electrical appliances produced by
various home appliance vendors and manufacturers
to allow a comparative analysis of their energy
consumptions (Shah et al., 2011). Furthermore it is
also used to represent functionalities of
heterogeneous devices from different technologies
used for home energy management (Rossello-
Busquet et al., 2011).
In the manufacturing domain several studies
have already been conducted on the application of
ontology to structures and the integration of
knowledge from the different systems or
stakeholders. It begins with an approach from
Lemaignan et al., (2006), which proposed an upper
OWL ontology for manufacturing domain and
presented two applications in cost estimation and
multi-agent systems (Lemaignan et al., 2006).
Lin and Harding developed an approach to
support information autonomy that allows the multi-
disciplinary, inter-enterprise stakeholders to use
their own terminology and information model and
simultaneously facilitate the communication and
information exchange among them (Lin and
Harding, 2007).
In chemical industries, ontologies are also used
to model different types of work processes. It allows
the formal representation of operational processes
throughout the plant life cycle (Hai et al., 2011).
Panetto et al., (2012) proposed an approach for
facilitating a system’s inter-operability in a
manufacturing environment based on an ontological
model for inter-operating, all-application software
that shares information during a product lifecycle.
The approach tried to address the difficulties in
managing heterogeneous information scattered
within organizations. It focused on the concept that a
product should embed the information about itself
(Panetto et al., 2012).
Until today, there have been no researchers who
have developed approaches in the use of ontology to
support energy management in manufacturing. In
this paper we propose an approach that adapts the
Figure 1: Architecture overview of intelligent energy management in manufacturing using ontology.
home energy management ontology to the
manufacturing domain, resulting in manufacturing
energy management ontology. We present some
methods to generate the ontology as well.
In our work, we use OWL – Web Ontology
Language to express the knowledge model. OWL is
originally a mark-up language for publishing and
sharing ontologies on the web (Bechhofer et al.,
The ontological classes as well as their attributes
and relation definitions representing automation
devices; for instance sensors, energy meters,
building environments, production facilities,
products and resources; are created manually by
experts. The ontology containing these hand-crafted
elements builds the knowledge base corresponding
to the manufacturing energy management domain
knowledge. It contains only the ontological classes
or Tbox elements that describe the knowledge
structurally and terminologically. It provides a
common conceptual vocabulary in the
manufacturing energy management domain. It does
not contain any ontological individuals or Abox
The domain knowledge represents the meta
model of a manufacturing energy management
system, therefore it owns the validity for any
manufacturing company and does not contain any
instance-specific information. It also includes
SWRL rules corresponding to common practices of
energy management in manufacturing. SWRL is a
mark-up language that combines OWL and RuleML
(Rule Markup Language) and enables the integration
of rules in OWL ontology (Horrocks et al., 2004).
The domain knowledge is added with company-
specific rules. Some rules are created by the
knowledge engineers in the company. Other rules
are created semi-automatically by applying machine
learning algorithms. The algorithms generate
association and classification rules from the hidden
Figure 2: Class hierarchy of manufacturing energy management domain ontology.
knowledge that is extracted from the collection of
energy, production and energy-related infrastructure
data. The rules are converted to SWRL format and
integrated into the ontology.
The Abox knowledge elements are created semi-
automatically based on factory configuration and
layout. For this purpose, we have developed a
method for interpreting the semantic information
from building and factory construction drawings
(Wicaksono and Rogalski, 2010). This will result in
an instance of the manufacturing energy
management domain ontology containing company-
specific knowledge elements. Figure 1 depicts the
architectural overview of our approach for
developing the intelligent energy management
Raza and Harrison (2011) have developed an
ontological knowledge model for Product Lifecycle
Management (PLM) in the automotive industry,
based on relationships among products, processes
and resources (Raza and Harrison, 2011). In our
approach, we propose a similar method and add
knowledge elements representing energy
management related knowledge such as ancillary,
transport or intra-logistics and energy conversion
Figure 2 depicts the class hierarchy of the
manufacturing energy management domain
ontology. At the highest level of ontology under the
Thing, we put the classes Product,
Process, ManufacturingResource,
BuildingElement, EnergySource, and
EnergyEfficiencyState. Product represents
the discrete products, such as the purchased
materials or products, the intermediate products that
are manufactured within the factory and the end
products that will be sold to the customers. We
define energy relevant properties on the concept
Product, for instance
hasVolume, hasWeight,
hasMaterialType. Based on these properties, it
can be decided which machine the product should be
manufactured with and how much energy it
We develop the process hierarchy similar to the
one in the MASON approach (Lemaignan et al.,
2006) consisting of
HumanOperation and LogisticOperation. All
of those processes can affect the energy
consumption in the company. For the sake of
simplicity, in this paper we present only several
main manufacturing operations classes, for instance,
Forming, Joining, Assembly, Machining,
Molding, and Casting. The Process class has the
hasEnergyConsumption. We put the
object property
produces in
ManufacturingOperation, to relate it to
Product and operatedOn to
ProductionFacility. The concept
LogisticOperation represents the intra-logistic
operation or transport between production facilities.
It has the properties
hasOrigin and
hasDestination with ProductionFacility
being the range of both properties.
The class
ManufacturingResource consists
of the subclasses
TransportFacility, and HumanResource. It has
a relationship to
Process through the object
EnergySource represents the energy generation
sources that are possibly used by the company. They
can be from a utility company or internally
generated, such as photovoltaic or thermal co-
generation. We add the object property
with the classes
ProductionFacility and
EnergyConsumingAncillaryFacility as the
ranges, in order to allow the modelling of the energy
flow within the company.
The class
corresponds to energy efficiency that should be
achieved and energy wasting or peak loads that have
to be avoided within energy management activities.
Through this class we can classify which practices
or constellations could improve the energy
efficiency, or which cause energy inefficiency. We
consider a peak load as a state that should be
prevented, since it can instantaneously cause high
energy allocation, thus causing higher energy costs.
The class
BuildingElements models the
building structures, such as rooms, production halls,
canteen, offices, as well as the energy consuming
facilities that indirectly affect the production
processes, such as lighting and HVAC systems. By
using the approach in interpreting the factory layout
drawing, the ontological elements of the classes can
be generated semi-automatically (Wicaksono and
Rogalski, 2010).
The class
IndustrialAutomation represents
the integrated application model for different
industrial automation technologies. The actuator for
controlling the production or ancillary facilities is
modelled as a sub class. The energy consumption
meter, temperature sensor, occupancy sensor and
other sensors are also included as subclasses. The
object property
isAttachedOn is created to relate
them to production and ancillary facilities.
As shown in Figure 1, there are three ways to
generate a company-specific knowledge base. The
first is through the manual generation by knowledge
engineers in the company. If necessary, the
knowledge engineers can enrich the domain
ontology with sector-specific ontological sub
classes. For example, for a metal or stainless steel
industry, the sub classes
and Milling are added as the sub
classes of
Forming. The energy efficient or
inefficient practices are modelled with SWRL. An
example of a condition, which describes energy
wasting conditions of ancillary facility, is if an oven
is active during a heat treatment process and the
heating system located in the same zone or hall is
still turned on, then it is considered as an energy
inefficiency condition. Equation (1) illustrates the
SWRL representation of such a condition.
Oven(?o)˄ HeatTreatment(?ht) ˄
operates(?o,?ht)˄ isActive(?ht,
true)˄ HeatingSystem(?hs) ˄
hasState(?hs, true) ˄ Zone(?z) ˄
isLocatedIn(?o,?z) ˄
The knowledge inferred from the SWRL rules in
equation (1) can be retrieved using Semantic Query-
Enhanced Web Rule Language (SQWRL). SQWRL
is a SWRL-based query language that supports SQL-
like operations like negation, disjunction, counting,
and aggregation (O’Connor and Das, 2009). The
SQWRL shown in equation (2) can be used to
retrieve all the ancillary facilities that are in under
energy wasting conditions.
Since ontology is both human and machine readable,
the knowledge base can be connected to an alerting
or messaging system. If the SQWRL in Equation (2)
returns some elements, a message can be generated.
This can sharpen the awareness of employees in
order to improve the energy usage efficiency within
the company. The energy efficient/inefficient
conditions that are formalized and stored in the
ontological company’s knowledge base, allow
simple access and query from any employee as well.
This accelerates the knowledge transfer among the
employees in practicing energy management.
The second method is to semi-automatically
generate the SWRL rules using machine learning
algorithms. In the manufacturing sector, companies
have to deal with large amounts of data from
different systems. There is often knowledge hidden
in the data that cannot be directly identified due to
the complexity of the data. The Knowledge
Discovery in Database (KDD) process extracts the
knowledge from the data using machine learning
techniques. As shown in Figure 1, the product,
process and resource data from different IT systems,
such as Enterprise Resource Planning (ERP) and
Manufacturing Execution System (MES) are
accumulated in a software module for data
aggregation. The module is also responsible for
performing the data pre-processing, such as data
cleaning, selection and transformation. The energy
related data from the Energy Monitoring and Data
Acquisition (EMDA) system are incorporated by the
module and they are finally stored in a relational
Figure 3: A result example of machine learning algorithm.
In our work, we develop a learning engine
module containing machine learning algorithms to
generate rules from the database. Figure 3 shows an
example of a result of a machine learning algorithm
that determines on which machine a product with the
particular characteristics, i.e. weight and material
type, should be heat treated and how high the energy
consumption is. We develop a classification
algorithm to generate rules for this purpose. Rules
generated by the algorithm are then transformed into
SWRL rules. Equation (3) gives an example of a
SWRL rule arising from the algorithm results
illustrated in Figure 3.
Based on the algorithm result shown in Figure 3,
it can be concluded that if the weight of the product
is less than or equal to 50 kg, the heat treatment
process has to be performed in an electrical oven. It
will consume energy between 200 and 400. That
means, if it consumes more energy than 400, it can
be considered an energy inefficient condition or
Product(?p)˄ hasMaterialType(?p,
“Type-A”) ˄ hasWeight(?p,?w) ˄
swrlb:greaterThan(?w,50) ˄
(?h) ˄ operates(?o,?h) ˄
produces(?h,?p) ˄
hasEnergyConsumption(?o,?e) ˄
By querying the ontology using SQWRL, it is
possible to ascertain which machines or production
facilities currently operate energy-inefficiently.
Therefore a quick operative action can be performed
to solve the problem. Since the ontology represents
the semantics of the manufacturing energy
management and allows a shared understanding
among stakeholders, the management can have an
overview of the state of their factory with respect to
energy efficiency. An alarm system can be built
based on the knowledge base.
The ontological individuals of the class
are generated based on the data from the order
management system and ERP (see Figure 1). The
energy consumptions are assigned with data from
EMDA system.
The third method is to generate ontological
elements from building construction drawings and
factory layouts. This method is not further presented
in this paper. This paper focuses only on the
knowledge representation using ontology and how
the knowledge is generated.
This paper presented an approach to an intelligent
energy management system in discrete
manufacturing using ontology as the knowledge
representation. We developed domain ontology for
manufacturing energy-management. It consists of
OWL classes and SWRL rules representing common
constellations of energy (in) efficiency. It is further
instantiated and enriched, resulting in the company-
specific knowledge. This paper explained three ways
to generate the company-specific knowledge, i.e.
manual generation by a knowledge engineer, semi-
automatic SWRL rule generation using machine
learning, and semi-automatic ontological element
instantiation from building construction drawings
and factory layouts.
The relationships among different energy related
elements such as products, processes, resources,
building elements and energy sources built in the
ontology, enable a holistic analysis for any
stakeholder on which factors can affect the energy
(in) efficiency in the company. The knowledge gap
among employees or between operative employees
and management can thus be overcome. The
approach allows the integration and standardization
of different IT and EMDA systems within the
context of a corporate energy management system.
However, to implement the approach, the role of
the knowledge engineer and domain expert to
generate the knowledge is still vital. Knowledge
modelling could be a time-consuming, error-prone
and inefficient task. The approach proposed in this
paper could be further developed by applying
ontology learning from text documents to
automatically generate the ontological classes.
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