Ontology Development towards Expressive and Reasoning-enabled

Building Information Model for an Intelligent Energy

Management System

Hendro Wicaksono, Preslava Dobreva, Polina Häfner and Sven Rogalski

Institute for Information Management in Engineering, Karlsruhe Institute of Technology,

Zirkel 2, 76131 Karlsruhe, Germany

Keywords: Energy Efficiency in Building, Building Information Model, Ontology Engineering, Ontology Population.

Abstract: In recent years, energy consumption in buildings has been rising and is currently representing a significant

percentage of the whole energy consumption on earth. The EU has responded this trend by requiring zero

energy consumption by 2020 and by supporting innovative research approaches for improving energy

efficiency in buildings without decreasing inhabitants comfort. This paper describes the approach to develop

an intelligent system for building specific energy management that allows occupants and facility managers

to monitor and control the energy consumption and also detects their wasting points. An ontology based

information model for building energy management offering expressive representation and reasoning

capability is also introduced in this paper. We highlight an approach to develop the ontology as the

knowledge base providing the intelligence of the system. Furthermore we demonstrate how the energy

performance analysis is improved using the ontology based approach.

1 INTRODUCTION

A study observing building energy consumption

held in 2007 showed that the public and residential

buildings represent more than 40% of the whole

energy consumption in European Union, of which

residential use represents 63% of total energy

consumption in buildings sector (Balaras et al.,

2007). The energy price has been rising due to high

building operational costs, and shortage of fossil

energetic resources. These reasons force companies

and private persons to organize their behaviour in

more energy-efficient way and to look for intelligent

and long term solutions.

There are several technical possibilities and

products on the market aiming to improve energy

usage efficiency designed for business and public

buildings. European Union has issued the Directive

2002/91/EC about overall energy efficiency of

buildings. The directive aims to improve energy

efficiency by taking into account outdoor climatic

and local conditions, as well as indoor climate

requirements and cost-effectiveness. The building

energy management systems are acknowledged as a

significant source for energy costs reduction up to

30% (Smithson, 2013).

Furthermore, in the future, energy savings in

buildings can be increased by intelligence

improvement of building automation systems. This

kind of method is considered in the literature

important as the conventional thermal insulation of

walls or insulating glazing to improve energy

efficiency in buildings (Lonmark, 2008; Spelsberg,

2006). Recently low cost and low energy consuming

building automation technologies have already been

developed. Recent technologies offer energy

measurement and sensors by using small chips that

consume less than 10 mW (Watteco, 2009). These

chips can be easily installed in building without

modifications.

By using these devices, extra energy

consumption can be avoided. However, the energy

efficiency could be effectively improved, if they are

supported by an intelligent software system. In this

paper we propose an intelligent system for energy

management in buildings by connecting building

automation systems and using intelligent

information model. The existing Building

Information Model (BIM) standards only defines

Wicaksono H., Dobreva P., Häfner P. and Rogalski S..

Ontology Development towards Expressive and Reasoning-enabled Building Information Model for an Intelligent Energy Management System.

DOI: 10.5220/0004540300380047

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2013), pages 38-47

ISBN: 978-989-8565-81-5

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

definitions, dictionary and information structure. In

this paper, we extend the BIM standard to have more

expressiveness and reasoning capabilities. We

incorporate rules and axioms to achieve these.

The information model is used in the knowledge

base that allows intelligent analysis on the relations

between energy consumption, behaviour model

(activities and events in the building), building

related information (geometry, boundary conditions,

etc.) and surrounding factors, such as temperature,

weather condition, occupant habits and behaviour.

The knowledge base is represented using ontologies.

We also introduce the ontology modelling method

that is aligned with existing building information

modelling standard called Industry Foundation Class

(IFC).

This paper is organized as follows. In section 2

we discuss the state of the art and related work.

Next, we introduce the developed system of

intelligent energy management in section 3. Section

4 describes our approach in generation of ontology

as the centre point of our intelligent system. In

section 5 we give overview how the energy analysis

is performed using ontological query. Finally we

make our conclusion in section 6.

2 RELATED WORK

In 2009 Electric Power Research Institute USA

conducted research of electricity consumption

feedbacks in household. They categorized feedback

mechanism based on the information availability

into standard billing, enhanced billing, real-time

feedback, real-time plus feedback, etc. (Neenan et

al., 2009) The research showed that real-time plus

feedback leads to the best improvement of energy

conservation comparing to the other feedback

mechanism, despite the higher cost of

implementation. Real-time feedback allows users to

monitor their energy consumption and/or control

appliances in their home through building

automation system (BAS) and home area network

(HAN).

Each building automation technology may offer

different functionalities, and has its own strength

and weakness. For example, the technology

digitalSTROM offers good functionality in energy

metering, but it does not support occupancy

metering. In order to achieve comprehensive energy

management by taking into account as much as

related conditions and factors, an integration of

different building automation systems is required.

Ontology can be used as generic model to facilitate

the integration (Reinisch et al., 2008). The ontology

is not only used to describe functionalities of

building automation systems, but also to represent

states of building, and relations with behaviour

model and surrounding factors. In this paper, we

introduce also method to generate ontology

components semi-automatically based on user events

and building specific information.

An ontology based approach was introduced in

EU funded project ISES based on description logic

ontology containing rules and constraints. The

ontology is represented with OWL-DL combined

with SWRL and is used as the information model for

integrated lifecycle energy management in building.

The approach addressed not only interoperability

issues with other systems, but also allowed quality

control by end user using knowledge-based

management methods (Scherer et al., 2012).

The EU funded project HESMOS developed an

ontology-equipped framework to address the

integration of distributed and heterogeneous data

from ICT building energy systems. The framework

comprises IFC-BIM as a central integration part and

a link model to bind the distributed data together.

The core link model is represented with OWL,

which includes the capabilities of model

management and decision support (Guruz et al.,

2012).

Both EU projects do not strongly consider the

alignment of existing standards, for instance IFC,

with the developed information model. They do not

include the modelling of occupant behaviour as one

of the factors that affects the energy consumption in

the building. This paper introduces an approach that

addresses these points.

3 OVERVIEW OF THE CONCEPT

In this paper, we propose an intelligent system,

which considers different aspects of a building. The

system allows the users to have an integrated view

of energy consumption in their apartment, office, as

well as in entire building. With the help of building

automation system and other metering systems

installed on the site the energy consumption can be

evaluated in different detail levels and quality, for

instance, energy consumption per appliance, per

group of appliances, per zone in the building, or per

user event (Wicaksono et al., 2010). Figure 1 depicts

the designed approach of the energy management

system developed in our recent research project.

As seen in Figure 1, the generic ontology is

OntologyDevelopmenttowardsExpressiveandReasoning-enabledBuildingInformationModelforanIntelligentEnergy

ManagementSystem

Figure 1: Overview of the developed energy management framework.

created by a building information modelling expert.

The generic ontology represents domain knowledge

for building holistic energy management. It contains

definitions, terminologies (T-box), and taxonomies

that are aligned with IFC. The information model

contained in the generic ontology is applicable in

any building. The generic ontology is then

instantiated and enriched with building specific

information resulting building specific ontologies.

The development of ontology will be explained

further in section 4.

The data collector and aggregator module is

developed for collecting energy data and sensor data

from different building automation systems installed

in the building. It contains an interface to

communicate with different building automation

logic control units or gateways via web services. The

module is also responsible to collect occupant

activities or behaviours in the building. For this, a

web-based interface to model occupant activities is

developed. Furthermore, an interface to the calendar

reflecting the schedules and activities of the

occupants is being developed.

The collected data are aggregated and stored in a

database. In order to allow visual representation of

energy consumption data, we perform necessary data

pre-processing such as removing erroneous values,

data transformation, data selection and data

conversion. The data are prepared to enable an

energy consumption analysis in different criteria

based on relation between rooms, appliances, time,

and user events. Therefore it allows a data-driven

analysis that is conducted directly on the collected

data by performing SQL-query, simple calculation,

or visualization, for instance, energy consumption

per time unit and each appliance. The data is

provided in such a form to enable the execution of

data mining algorithm for finding the energy usage

pattern.

The data mining module evaluates energy

consumption data that are collected and aggregated

and extracts the knowledge in forms of patterns and

relationships from the data. Through this module,

energy consumptions can be related to device levels,

room, and time, which in addition to that, can be

combined with relation to occupant behaviours and

surrounding conditions. As seen in Figure 1 the

extracted knowledge is incorporated in the building

energy management knowledge base represented by

building specific ontology. The relationships

between data are modelled as rules and represented

as SWRL.

A building plan is usually drawn in 2D using

CAD software applications, such as AutoCAD.

KEOD2013-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

Unfortunately, 2D-drawings only contains

geometrical information, for instance, lines, points,

curves, circles, etc. the CAD layouts cannot describe

any semantics of building components contained in

the drawing. We can still understand semantic of the

drawing because we already know the symbols

representing certain building components or

appliances, such as doors, walls, fridge, etc.

(Wicaksono et al., 2010). However different

AutoCAD versions provide different representations

of the geometrical and object-related information

which makes difficult an automated extraction of

data. In the frame of the FP7 KnoHolEM project a

method to interpret the semantic from 2D-drawings

and populate the ontology has been developed. The

final aim is to allow a semi-automated extraction of

semantic information

The developed tool combines user input with

pattern matching methods. The user interprets the

CAD layout and the tool maps the CAD layouts to

ontology classes and facilitates the creation of

ontology individual on the corresponding classes.

Thus the ontology will be populated with building

specific information coming from CAD layouts.

The resulted ontology allows a knowledge-

driven analysis. It means the analysis is not

conducted directly on the data, but by utilizing

ontology that represents the knowledge.

The visualization and analysis tool facilitates the

visual interaction between the user and the system.

The building geometry is visualized in 3D. The

visualization and analysis tool is an instrument for

the end users to query the ontology. By using the

tool the facility manager is able to identify energy

wasting and anomalies, to examine the building

states, e.g. which windows or doors are currently

open, etc. The tool also allows the building

occupants to have better understanding of the energy

performance in their building and it also empower

their engagement in balancing comfort and energy

efficiency. The occupant can have an overview of

the energy efficiency of the zones, where he is

responsible for, thus it increases his awareness to

avoid energy wasting and achieve more energy

efficiency.

4 ONTOLOGY DEVELOPMENT

The knowledge base as the centre point of the

developed energy management system is represented

in OWL (Web Ontology Language), a W3C

specified knowledge representation language (Smith

et al., 2004). Basically there are two types of

ontologies that we develop. We develop a generic

ontology representing a common information model

for building energy management, and then it is

populated and extended with building specific

information resulting more building specific

ontologies corresponding to the specific buildings. It

is illustrated in Figure 2.

Kitchen

Corridor

Bathroom

Livingroom

Bedroom

Kitchen

Corridor

Bathroom

Livingroom

Bedroom

Kitchen

Corridor

Bathroom

Livingroom

Bedroom

Figure 2: Generic ontology and building specific ontology.

In our work, there are six main steps to develop the

ontology resulting a building specific ontology. The

steps are illustrated in Figure 3. The following

subsections explain each step to develop the

ontology.

4.1 Definition of Ontology Main

Resources

The ontological classes as well as their attributes and

relations representing the resources needed for the

energy management in buildings are created

manually by experts. It is depicted as step 1 in

Figure 3. The ontology containing these hand-

crafted elements is called generic ontology. It only

contains the ontological classes or Tbox components

that describe the knowledge structure, definitions

and terminology. It does not contain any ontological

individuals or Abox components and contains no

building specific information. Figure 4 depicts the

ontology main classes representing the different

resources needed for the energy management in

building.

The class BuildingElement models the

building structures that are observed, examined and

analyzed in energy management activities. The

building elements are passive entities which have

state, but do not have capabilities to measure or to

observe their own states. The class

BuildingElement and its sub classes represent

the fundamental of Building Information Model

(BIM). It is aligned with the domain layer in

IFC2x4.

The class BuildingControl indicates the

entities related to building automation system

elements in the building. It represents the sensors,

OntologyDevelopmenttowardsExpressiveandReasoning-enabledBuildingInformationModelforanIntelligentEnergy

ManagementSystem

Figure 3: Ontology development process.

actuators, controller, alarm, etc., which are elements

of a building automation system. It has capabilities

to measure, to observe, and to control the state of

BuildingElement. It aligns with entities in the

IFC2x4 domain IfcBuildingControlsDomain.

The class Actor represents the human actors

having bahaviour that can affect the states of

BuildingElement. The Actor can be organizations

or persons, who have name, postal address, telecom

address, etc. It is aligned with the IfcActorResource

in IFC2x4.

The class Behaviour represents behavior

performed by Actor. The Behaviour can affect

the state of BuildingElement. There are two

methods to model the behavior in the building, i.e.

bottom up and top down. This will be described

further in section 4.4.

The class State represents the state of

BuildingElements. It can be divided as

ComplexState and SimpleState. Examples

of ComplexState are ComfortState and

EnergyEfficiencyState, whereas examples

of SimpleState are WindowState,

DoorState, etc.

4.2 Explicit IFC-OWL Mapping

The modern building drawing already contains

semantic information represented using IFC entities.

To support the ontology population from IFC

drawing containing semantic information, we

develop a method to map the IFC entity to OWL

class explicitly. We use class annotation to perform

the mapping. As seen in Figure 5, the class

annotation correspondToIfcEntrity maps

the IFC entity, for instance IfcWall to OWL class

Wall, and the class annotation

correspondToIfcEnumerationElement

maps the IFC enumeration value STANDARD to

OWL class StandardWall.

The explicit IFC-OWL class mapping

accelerates the ontology population process from

IFC drawing containing semantic information. If we

have an entity in our IFC drawing, by querying the

ontology using SPARQL, we can find the

corresponding OWL class. For example, the

following SPARQL statement finds the OWL class

Wall, if we have the IFC entity IfcWall.

SELECT ?class ?ifc

WHERE {

?class

knoholem:correspondToIFcEntity ?ifc

?class

knoholem:correspondToIFcEntity

“IfcWall”

}

KEOD2013-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

Figure 4: Ontology main classes.

4.3 Population of BuildingElement

using Developed Tool OntoCAD

A layout of a building is commonly created as a two

dimensional drawing or sketch using CAD software

applications, such as AutoCAD. Further AutoCAD-

based software tools are used to plan and model

many domains of a building, such as ventilation,

heating, access controls and photovoltaic (Krahtov et

al., 2009). The number of elements in a sketch and

its complexity may vary (Donath, 2008). CAD is

one of the easiest and oldest technologies used in the

industry. At the same time CAD is the least effective

technology when it comes to accomplishing building

information modeling because it demands a great

amount of effort. Recent research shows evidence

that it does not ensure high quality, reliable, and

coordinated information that the higher level of BIM

produces (Vanlande et al., 2008).

We develop a tool to extract the semantic

information from CAD drawing and populate the

ontology using the extracted semantic information.

The tool is called OntoCAD. First, A CAD drawing

is exported to DXF. Then we import the primitives

in our tool OntoCAD from the exported construction

layout files. The primitives are extracted and

clustered in layers like they were in AutoCAD. This

vector based data representation is the basis for the

viewer and the pattern matching algorithms. An

important user input at the beginning is the mapping

of the ontology specific data and object properties

with the OntoCAD functions, for instance the

computation of the object position. The implemented

pattern matching and classification algorithms

recognize building elements based on user defined

templates. The user selects an object that can

directly be populated in the ontology or he can

choose to search for similar objects and then

populate all of them at once. He has the possibility

to directly validate the result and if necessary apply

some corrections to the results. The results are

continuously and automatically saved to the

ontology.

Figure 5: IFC-OWL explicit mapping.

OntologyDevelopmenttowardsExpressiveandReasoning-enabledBuildingInformationModelforanIntelligentEnergy

ManagementSystem

4.4 Behaviour Modelling

The next step to develop the ontology is the

behaviour modeling as seen in step 4 of Figure 3. In

our work, we develop two approaches to model the

behaviour. The first approach is bottom up. In

bottom up approach, behaviours are modeled based

on building states. In other words, it is based on the

relationships between sensor output values. A

simplified example of a bottom up behaviour is as

follow:

 ⊓ 

⊑ 

(1)

The system can infer that the activity

CoffeeBreak currently occurs, if the occupancy

sensor in the kitchen shows that the kitchen is

occupied, and the coffee machine there is turned on.

The drawback of this approach is that we need a

lot of different sensors to be able to give statement

about the activities. To model this kind of behaviour

is an extensive task. A machine learning method can

be helpful to extract the relations representing

behaviour from different sensor data.

The second method is the top down approach.

The behaviours are modelled using common

modelling approaches, such as UML or BPMN

diagram. Behaviours are defined as sequences of

different sub activities. The drawback of the

approach is the impossibility to identify the

occurring activities automatically. The system

cannot know what kinds of activities are currently

occurring in the building. Therefore it needs a

manual activity instantiation from the occupants.

This kind of manual activity logging causes

extensive work to the occupants.

4.5 SWRL Generation using Data

Mining

In our work, data mining algorithms are used to

identify energy consumption patterns and their

dependencies. Data mining is defined as the entire

method-based computer application process with the

purpose to extract hidden knowledge from data

(Kantardzic, 2003). In our work, we use different

data mining procedures to generate knowledge for

recognition of energy usage anomalies, energy

wasting and also to predict the energy consumption.

In this work, we relate the energy consumption

with the behavioural occupant’s pattern in the

building.

We aim to recognize energy consumptions that

do not occur normally in the building. To perform

this task, we have to know how energies are

consumed normally regarding occupant events and

surroundings. For example, normally when an

occupant is currently working and the outside

temperature is comfortable, let us say greater than 20

degrees Celsius, total energy consumption in the

building is low, for instance lower than 10 kWh. If

in the same pre condition total energy consumption

in building is more than 10 kWh, then it is

considered as a usage anomaly.

It is difficult for users if they always have to log

their activities. In our work we use simple sensors to

recognize user activities automatically. Simple

sensor can provide important hint about user

activity. For instance, an occupancy sensor in a

kitchen can strongly give a clue whether somebody

is currently cooking. Of course it should be

combined with information of appliance states in the

kitchen.

The rules representing normal energy

consumptions are obtained through data mining

classification rules algorithm. The algorithm is based

on a divide-and-conquer approach. The created rule

(2) shows the probability of 67% about how often it

could happen if the activity is working while outside

temperature is greater than 20 degree with total

energy consumption is lower than 10. This value is

called confidence. The rules described in (2)

represent a condition that normally occurs. The rule

is transformed to (3), in order to represent an

anomaly condition, by negating the consequent part

of the rule (Wicaksono et al., 2012).

Event=”Working” ˄ OutsideTemperature_

≥20→TotalEnergyConsumption<10

(conf:0.67)

(2)

Event=”Working” ˄ OutsideTemperature_

≥20→TotalEnergyConsumption≥10

(3)

The rules created by data mining algorithm are

stored in ontology as SWRL. SWRL rule (4)

represents the transformed rule (3), which is stored

in ontology. The class UsageAnomaly is a sub

class of ComplexState.

UserBehaviour(?e)˄

hasName(?e,“Working”)˄

OutsideThermometer (?ot)˄

hasValue(?ot,?otv)˄

swrlb:greaterThanOrEqual(?otv,20)˄

SmartMeter(?sm)˄ hasValue(?sm,?smv)˄

swrlb:greaterThanOrEqual(?smv,10) →

UsageAnomaly(?e)

(4)

The rules resulted from data mining algorithm do

not always have 100% confidence. Therefore we

represent the rules as SWRL in order to enable

KEOD2013-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

verification by using SWRL editor such as Protégé.

The editor enables users to add, modify and delete

the resulted rules.

4.6 Modeling States

In our work we divide the state to SimpleState

and ComplexState. To model both kinds of

states, we formulate a set of competency questions.

Table 1 gives examples of competency questions in

order to model a complex state

EnergyInefficient

Table 1: Competency questions to model states.

Question-

Competency questions

Example of

answers

Which heaters are

currently in energy

inefficient state in the

building?

Heater2,

Heater3

Q1.1

Which heaters are

currently switched on?

Heater1,

Heater2,

Heater3

Q1.2

Which openings are

currently open?

Window2,

Door3,

Window4

Q1.3

Which heaters and

openings are currently

located in a same

closed zone?

Heater1 and

Door1,

Heater2 and

Window2,

Heater3 and

Door3,

Heater4 and

Window4

The SWRL illustrated in (5) implies a complex state

of energy inefficiency, if a window is opened, a

heater is turned on, and they both are located in a

closed zone. The necessary classes are created based

on the formulated competency questions. For

example, as seen in (5), the classes

HeaterSwitchOn and OpeningOpen are

created based on competency questions Q1.1 and

Q1.2.

Q1.1

HeaterSwitchedOn(?h) ˄

Q1.2

OpeningOpen(?o) ˄ Inside(?z) ˄

Q1.3

isLocatedIn (?o, ?z) ˄

Q.1.3

isLocatedIn

(?h, ?z) →

EnergyInefficient(?h)

(5)

The simple state class OpeningOpen is

represented as axiom (6). It implies that if an

opening sensor gives the value true, and it is

installed on a certain opening, it can be inferred that

the opening is currently open.

Opening and (hasSensor some

(OpeningSensor and (hasBinaryValue

value true)))

⊑

OpeningOpen

(6)

Analogue to the OpeningOpen the simple state

HeaterSwitchedOn is represented using the

axiom (7).

Heater and (hasSensor some

(EnergyMeter) and (hasBinaryValue value

true))

⊑

HeaterSwitchedOn

(7)

5 ENERGY ANALYSIS

THROUGH ONTOLOGY

QUERY

In knowledge base represented in ontology, all

conditions of energy wasting and anomalies are

represented as SWRL. Periodically data acquisition

module requests real-time data from building

automation gateway. These data contain states given

by all installed building automation devices. SWRL

rules are used to decide whether these incoming data

correspond to complex states, e.g. energy

inefficiency and anomaly condition. We develop a

rule engine based on SWRLJessBridge to support

the execution of SWRL rules combined with Protégé

API that provides functionality in managing OWL

ontology.

First the attribute values of relevant ontological

instance are set to values corresponding to incoming

data. For example, if opening sensor attached to

window gives a state “Open”, then the attribute

hasState of corresponding ontology instance of

concept OpeningSensor is set to “Open”. After

that the rule engine executes the SWRL rules and

automatically assigns individuals to the ontology

classes defined in the rule’s consequent. For

example for rule (5) the instance of class Heater is

additionally assigned to EnergyInefficient

class and for rule (4) the instance “Working” of class

UserBehaviour to class UsageAnomaly.

SPARQL is used to evaluate whether energy

inefficient condition or energy usage anomaly

occurs. Which appliances cause the energy wasting

can be retrieved as well. It is performed by querying

all individuals of EnergyInefficient or

UsageAnomaly class. If individuals of these

classes are found, the affected individuals are

visualized and marked in the visualization and

analysis tool. With this mechanism, user can have

more awareness in order to avoid more energy

wasting. SPARQL is also used to perform further

OntologyDevelopmenttowardsExpressiveandReasoning-enabledBuildingInformationModelforanIntelligentEnergy

ManagementSystem

analysis for example to retrieve all windows or

doors that are currently open. The SPARQL

interface is part of visualization and analysis tool.

6 CONCLUSIONS

In this paper we have presented a system of

comprehensive intelligent energy analysis in

building. In the developed system, we combined

classical data-driven energy analysis with novel

knowledge-driven energy analysis that supported by

ontology. The analysis is performed on information

collected from building automation devices. The

ontology supported analysis approach provides

intelligent assistance to improve energy efficiency in

households or public buildings, by strongly

considering individual user behavior and current

states in the building. Users do not have to read the

whole energy consumption data or energy usage

profile curves in order to understand their energy

usage pattern. The system will understand the

energy usage pattern, and notify user when energy

inefficient conditions occur.

We have presented also an approach to develop

the ontology as the knowledge base of the intelligent

energy management system. There are different

methods and steps to generate the ontology. We

differentiated between generic ontology as generic

information model and building specific ontology

containing the building specific information. The

generic ontology is aligned with IFC to allow

interoperability of our system with existing industry

standards. We introduced the main resources of the

ontology representing the main elements in energy

management in building. We presented briefly a tool

called OntoCAD to perform semi-automatic

extraction of semantic information and population of

building elements in the ontology from CAD

drawings. We also introduced our approach to model

occupant behaviour and building states that affect

the energy performance of the building. In this work,

we also integrated SWRL rules that are extracted

from different data, i.e. energy consumption, sensor

data, and behaviour using data mining algorithms.

ACKNOWLEDGEMENTS

Research activities presented in this paper have been

partially funded by the German government

(BMBF) through the research project KEHL within

the program KMU-Innovativ and the European

Commission trough the FP7 research project

KnoHolEM.

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OntologyDevelopmenttowardsExpressiveandReasoning-enabledBuildingInformationModelforanIntelligentEnergy

ManagementSystem