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ThinkHome Energy Efficiency in Future Smart Homes


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Smart homes have been viewed with increasing interest by both home owners and the research community in the past few years. One reason for this development is that the use of modern automation technology in the home or building promises considerable savings of energy, therefore, simultaneously reducing the operational costs of the building over its whole lifecycle. However, the full potential of smart homes still lies fallow, due to the complexity and diversity of the systems, badly engineered and configured installations, as well as the frequent problem of suboptimal control strategies. Summarized, these problems converge to two undesirable conditions in the "not-so-smart" home: energy consumption is still higher than actually necessary and users are unable to yield full comfort in their automated homes. This work puts its focus on alleviating the current problems by proposing a comprehensive system concept, that shall ensure that smart homes can keep their promise in the future. The system operates on an extensive knowledge base that stores all information needed to fulfill the goals of energy efficiency and user comfort. Its intelligence is implemented as and within a multiagent system that also caters for the system's openness to the outside world. As a first evaluation, a profile-based control strategy for thermal comfort is developed and verified by means of simulation.
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Hindawi Publishing Corporation
EURASIP Journal on Embedded Systems
Volume 2011, Article ID 104617, 18 pages
Research Article
ThinkHome Energy Efficiency in Future Smart Homes
Christian Reinisch, Mario J. Kofler, F´
elix Iglesias, and Wolfgang Kastner
Automation System Group, Vienna University of Technology, 1040 Vienna, Austria
Correspondence should be addressed to Christian Reinisch,
Received 1 July 2010; Accepted 15 September 2010
Academic Editor: Peter Palensky
Copyright © 2011 Christian Reinisch et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
Smart homes have been viewed with increasing interest by both home owners and the research community in the past few years.
One reason for this development is that the use of modern automation technology in the home or building promises considerable
savings of energy, therefore, simultaneously reducing the operational costs of the building over its whole lifecycle. However, the
full potential of smart homes still lies fallow, due to the complexity and diversity of the systems, badly engineered and configured
installations, as well as the frequent problem of suboptimal control strategies. Summarized, these problems converge to two
undesirable conditions in the “not-so-smart” home: energy consumption is still higher than actually necessary and users are
unable to yield full comfort in their automated homes. This work puts its focus on alleviating the current problems by proposing
a comprehensive system concept, that shall ensure that smart homes can keep their promise in the future. The system operates
on an extensive knowledge base that stores all information needed to fulfill the goals of energy eciency and user comfort. Its
intelligence is implemented as and within a multiagent system that also caters for the system’s openness to the outside world. As a
first evaluation, a profile-based control strategy for thermal comfort is developed and verified by means of simulation.
1. Introduction
The worldwide energy demand is rising constantly. While
many sectors (e.g., transport, production industry) have
been trying to reduce their energy consumption for several
years, sustainability in the residential domain must still be
considered being in its infancy. This stems at least partly
from the fact that, although awareness and motivation to
save energy are nowadays typically existent among home
owners, adequate technological support for the users is
greatly lacking. This concerns foremost the unavailability of
dedicated, comprehensive systems that support an energy-
ecient operation of a home or building. Considering the
rapidly increasing energy costs, reduced energy consumption
has economic benefits but it also pays on a macroscopic level,
where national and international environmental goals and
laws have to be fulfilled.
Realizing an energy-ecient building operation is closely
tied to the employment of building automation systems
(BAS), which are considered as an almost mandatory con-
dition for the sustainable (low-energy, low-emission) home
or building [1]. Hence, over the past decade, smart homes
as in the residential building sector. The tempting vision of
smart control over environments motivates home owners to
integrate automation technology into their homes with the
promising eects of increased comfort, peace of mind, and
reduced operational costs. Still, the mere installation of such
systems does not automatically constitute a perfect solution.
In fact, much of the potential that would be available through
BAS in the smart home lies fallow. This is for several reasons.
Control strategies that link sensors and actuators are not as
powerful and flexible as they should be. Furthermore, tuning
the control precisely to the requirements and also preferences
of its users is a task reserved to experts with profound system
knowledge. Additionally, it requires to take into account the
characteristics of building structure, building automation
equipment, and other influence factors. Thus, optimizations
of (both new and existing) systems are hardly ever realized in
full due to the large eort encountered. For the same reason,
necessary readjustments to new or changed requirements
(e.g., when a room is remodeled from oce to bedroom)
2EURASIP Journal on Embedded Systems
are foregone almost as a rule once the system has been
Another shortcoming that BAS are facing today is that the
promising integration of household appliances (white goods)
and consumer electronics (brown goods) is not happening
pervasively, if at all. The reason is that the integration of
these devices is not trivial at the physical layer (e.g., wired-
wireless), nor at the network layer (communication), neither
at the application level (data semantics). Additionally, such
an extension of the BAS scope obviously increases the overall
system complexity. However, for full resource conservation,
it is mandatory to include major energy consumers such
as household appliances in novel control strategies of the
automation system.
Apart from the technical reasons that counteract optimal
system performance, also organizational factors are influen-
tial. Due to the complexity of the systems and the underlying
physical processes that shall be controlled (e.g., thermal
comfort control), users are often unable to fully understand
their system and to apprehend the high number of influence
factors that are connected to it (parameters such as building
structure, environmental conditions, system/device capabili-
ties, etc.).
To fully unleash the environmental potential of BAS,
a new approach to the problem that eliminates the afore-
mentioned shortcomings is imperatively needed. Hence, a
novel system concept is required that transparently integrates
all dierent systems of a (smart) home, makes available
all important parameters and information, and enables
advanced use cases that cater equally for both, energy
eciency and user comfort. Most important, the system
needs to support the inhabitants (e.g., to feel comfortable or
to save energy) but it must never patronize them. The system
therefore has to be able to perceive its environment and to be
aware of the users and their actions, thus being able to learn
from and adjust to them.
A novel approach to realize the smart, minimum energy,
green building is taken in this work. The proposed home
system concept is termed ThinkHome. According to its name,
ThinkHome aims at the realization of an intelligent home by
introducing semantic context and artificial intelligence (AI)
in this future home. The advanced intelligence is realized by
means of control strategies that are embedded and cooperate
fairly within the highly interoperable ThinkHome system
structure that provides transparent access to data, users,
building systems, and miscellaneous other services.
In the remainder of the paper, the complete ThinkHome
system concept is presented in greater detail. In Section 2,
the system architecture is described and system building
blocks, related mechanisms, and goals of ThinkHome are
introduced. The potential of the system is then illustrated
by means of use cases in Section 3. The main system parts,
a knowledge base and a multiagent system, are explained
in Sections 4and 5,respectively.InSection 6, an example
of an intelligent ThinkHome control strategy is presented,
evaluated, and compared with other approaches. In the
following section, the ThinkHome approach is set in context
to related work. Finally, the work is concluded and an
outlook on future challenges is given in Section 8.
2. System Overview
The ThinkHome system is designed under two main
premises: it shall ensure energy eciency and comfort
optimization. While a focus on energy is easily justified with
sustainability and economic considerations, the reason to
prominently feature comfort originates from the fact that
comfort is a main decision criterion for home owners to
employ expensive building automation technology. Thus,
ThinkHome aims at providing a comprehensive system and
architecture for sustainable next-generation buildings. It
can be seen as a digital ecosystem due to its collaborative
characteristic, where advanced methods and algorithms are
applied in order to optimize control decisions as well as
dedicated parts to facilitate information availability and
access. The architecture of the system is designed to provide
important characteristics such as flexibility, modularity, and
compatibility in a native way. The underlying structure
allows a quick extension, works on dierent building con-
trol standards, integrates devices from dierent domains
formerly left out of BAS (e.g., household appliances),
and can handle equipment from dierent manufacturers.
Beyond these features, ThinkHome supports the optimized
application of artificial intelligence methods to the building
environment, focusing on relevant features like ubiquity,
context awareness, conflict resolution, and self-learning
capabilities. In this context, the Artificial Recognition System
(ARS) project shall be mentioned, which covers many of
these aspects and is a major topic in [2]. The works collected
in the book operate on mechanisms originally coming from
neuropsychology and psychoanalysis and have the common
goal to provide computer systems with consciousness (e.g.,
for situation modeling)—an approach also tempting when
thinking of smart homes.
The ThinkHome system moreover considers the build-
ing management from an holistic viewpoint, thus going
far beyond optimizing each service or application inde-
pendently, an integrated view that is also demanded by
Borggaard et al. [3]. Sustainable operation in ThinkHome is
realized by intelligent control strategies that take into con-
sideration a multitude of parameters ranging from building
structure over weather forecast data to personalized user
preferences. The comprehensive system acts autonomously
and automatically towards the system goals and assists the
users to reach their preferred building conditions in the
most energy ecient way. Thereby, all energy consumer in
the home are targeted, that is, the system is not limited
to the traditional BAS domains heating, ventilation and
air-conditioning, and lighting/shading, but it also considers
consumer electronics and household appliances.
In order to implement the previous characteristics, the
ThinkHome architecture features two main parts, a com-
prehensive knowledge base (KB) and a multiagent system
(MAS). As shown in Figure 1, the system is completed by the
global goal component that is symbolically located on top of
the system as well as a historization (data storage) system in
the bottom right corner.
The task of the knowledge base is to intelligently
maintain all relevant concepts that are considered to be
EURASIP Journal on Embedded Systems 3
Global goalsetting
inference agent
Knowledge base
User agents
Global goals
data agent
KB interface
agent User prefs
BAS interface
Intelligent multi-agent system
Figure 1: Overview of the ThinkHome system.
influence factors in a smart home. Thus, it stores details on
users like their preferences and profiles, current occupancy
and activities (i.e., context), as well as schedules. Likewise,
also weather data and building conditions are conceptualized
mainly to enable dynamic optimizations. Furthermore, the
KB keeps information about the building: it integrates data
already collected during the architectural conception and
construction process of a building, in particular comprising
data on the building structure, building orientation, used
materials, and related properties of these items. It also stores
information on all resources (e.g., devices) that are available
within the smart home, including energy-related aspects.
Viewed in a global context, the KB is the foundation for
the MAS and basically supports the system to infer the
most appropriate building control strategies, that is, those
that are most energy ecientandcomfortorientedin
the current situation. Additionally, the KB functions as an
abstraction layer of the underlying BAS. As it is not relevant
for control strategies to be aware of the concrete installations
in the building, but rather of the services they oer, the
KB provides a generic and integrated view of the dierent
devices, networks and related functionalities to the higher
system part. Taken together, this part of the system represents
the shared vocabulary used by the MAS for execution of
advanced control strategies. It is therefore fundamental in
grounding ThinkHome.
Located on top of the KB, the intelligence part of the
system is implemented as a multiagent system. This approach
was chosen for two reasons. First, MAS is a powerful logical
methodology that perfectly complements the previously
identified necessities and requirements, mainly in terms of
distributed intelligence, for providing encapsulation on a
functional level and for natively supporting communication
among dierent system parts. Second, the use of the
agent paradigm also brings along independent evolution,
exchange, and maintenance of the autonomous parts that are
implemented as agents. The use of well-defined interfaces
helps to retain the required autonomy and even permits a
possible local distribution of components.
During operation, the MAS makes use of the data and
knowledge about the system that is stored either explicitly
or that can be inferred from the ontology model in the
KB. This variety of information allows the MAS to execute
advanced control algorithms and strategies that are enriched
by a multitude of influence parameters and mainly rely on
mechanisms from artificial intelligence (AI). These control
strategies are embedded in dierent agents, where each agent
pursues its own task and goals but can cooperate with other
agents to also solve more complex problems. In order to be
aware of the environment, the agents retrieve information
from the knowledge base. The KB always keeps a current
representation of the system state (i.e., a process image),
while historical data are collected in a dedicated back-end
data storage system (cf. Figure 1). Other dedicated agents
realize further interfaces of the overall system to the users, the
BAS, and other miscellaneous services (e.g., remote server
ThinkHome’s structure, based on a smart and vivid
agent information exchange, also facilitates the integration
of context awareness methods and self-learning capabilities.
Agents initiate actions relying on data from the smart home
stored in the knowledge base or the history storage. This data
can later also be analyzed to create profiles or benchmarks,
compute predictions, refine the agent parameters (believes,
goals), select control algorithms, or tune their parameters.
The comprehensive ThinkHome approach also considers
two aspects frequently forgotten in other systems: a usable
interaction between the system and its users and an unob-
trusive yet ubiquitous integration of the smart system in the
daily context. Both promise a higher user acceptance and
satisfaction with the system, but demand that the system
is capable of automatic and mostly autonomous control of
the environment. Unobtrusive action of the system is for
example enforced with the help of learning and context
awareness mechanisms that help the system to transparently
act on behalf of its users without demanding any direct
interaction of them. One example on how these properties
can be implemented within ThinkHome is outlined in
Section 6, where the smart home tries to learn from the users
by just observing them in order to be able to predict their
desires, act ahead autonomously, and finally also assess their
level of satisfaction.
ThinkHome also passively contributes to energy e-
ciency, because users may take part actively in the control
process, if they wish to. With the help of the extensive
amount of data available in the system, users can be provided
with periodical energy consumption reports and hence get
feedback on their actions which can increase their energy
awareness. One possible and particularly unobtrusive way to
deliver this feedback is ambient displays, a technology that
visualizes diverse aspects of energy or water consumption
4EURASIP Journal on Embedded Systems
with the help of, for example, colors that then function as
more abstract consumption indicators [4]. The ThinkHome
system can also provide information on how to conserve
energy by means of practical savings advices, for example,
by recommending to open the shades before turning on
artificial lighting. On a larger scale, it is also envisioned that
multiple ThinkHome systems (installed in dierent homes)
could be linked and exchange data on new control strategies,
compare historic data and trends, or even cooperate to
achieve certain goals (e.g., implement novel demand side
management concepts) [5]. Finally, the combined ontology-
based MAS approach is especially beneficial considering the
complexity and heterogeneity of the involved disciplines:
home automation, knowledge representation, modeling and
processing, AI, machine-learning, and context awareness.
Mechanisms from all these domains have to be coupled in an
intelligent fashion to implement an advantageous control, a
challenge solved by the ThinkHome system architecture. The
comprehensive system approach is completed by a seamless
integration of the intelligent MAS and the knowledge base
pursuing an open and well-defined interface definition
already from the start.
It can be seen that the wide variety of parameters
harvested by the ThinkHome system can apparently lead to
an energy-optimized building control if used in a sensible
way. This system concept comprises facts that up to now
have rarely been included in any smart home approach,
thus further promoting the benefits that smart homes and
modern automation systems have to oer nowadays. Due
to the diversity of considered information, even alternative
control strategies that consume very few or no energy (e.g.,
opening a window) can be weighted and taken into account
to lessen energy expenditure.
3. Use Cases
To justify a new technology like ThinkHome, it is important
to identify useful applications and scenarios for which the
system can provide substantial improvements. The following
section therefore investigates dierent use case classes which
exhibit a high energy savings potential especially in the
residential sector.
3.1. Thermal Comfort. According to the report [6], space
heating in residential homes makes up about 57% of the total
energy demand in the EU. It is obvious that an intelligent
usage of home appliances can lead to a significant reduction
of energy consumption. One case would be to link the
heating of the rooms with the weather prognosis. This
means, that on a sunny winter’s day, for example, shutters can
be opened in unoccupied parts of the building, to let sunlight
traverse windows and transparent doors (solar radiation).
Depending on the transmission rate of the glazing, it is
possible to achieve a heat gain with this action. Of course
this kind of activity just makes sense in parts of the building
where sunlight can be expected, which leads to the necessity
of having a notion of the building orientation.
The energy consumed for space heating can be further
reduced by knowing the thermal inertia of the building.
If, for example, it is known that one room is adjacent
to two conditioned spaces, bringing this room to comfort
temperature can be achieved faster than if the room is
directly connected to the outside. In addition, how the
room condition follows the outside temperature depends
on the equivalent energy storage mass of the building
material. Therefore, thickness and material of exterior as
well as interior walls and floors are valuable data when, for
example, an optimum start/stop schedule for the heating
system has to be provided. This heating control is closely
related to the occupancy and usage of the building and
dierent areas inside it. For energy eciency, conditioning
of a space has to happen at the latest possible point in time
before occupation will occur. This intelligent control can
be significantly improved if the thermal inertia of a room
are known in advance. Therefore, material, dimensions, and
other building physics parameters have to be stored in the
system, in order to calculate the thermal properties of a
room and with the help of these values influence the heating
Two main exterior influence factors are wind and
temperature: the higher the draught of outside air, the more
pressure is put on the building hull leading to a higher
air exchange rate through small gaps between walls and
openings. This figure can be measured by the so-called blower
door value, which quantifies the rate at which air traverses
the building hull. Also the dierence between outside and
inside temperature is a major influence on how much air
exchange happens. Consequently, it can be used for thermal
The opposite use case in the area of thermal comfort is
cooling of a space during summer season. With an intelligent
control system considering knowledge of weather data as well
as building design and shape, the existing energy savings
potentials in the field of artificial air cooling can be exploited.
If, for example, the weather forecast for the night predicts
cool temperatures, the system could drop an artificial cooling
strategy in favor of ambient air cooling, in order to lower the
temperature in the building. This technique, also known as
night purge, of course has to be performed in accordance to
the occupancy of the building. The temperature of unoccu-
pied rooms can be brought down to a reasonable level while
keeping it on a comfortable value in occupied rooms. Also
in this case the thermal inertia can be considered by cooling
down the room to a lower temperature than necessary and
counterbalance this with stored day-heat in the building
hull. This activity therefore also performs a natural chilling
of the building envelope. If the night WeatherSituation in
addition is calm (e.g., no thunderstorms, wind), also natural
ventilation can be taken into account by opening windows.
Of course an appropriate security policy has to be followed
in order to avert burglary. Another possibility to prevent the
building from summer overheating is intelligent control of
shutters and blinds: closing shutters in unoccupied rooms
can create an additional layer of insulation against sun-rays
and therefore lower the sun’s impact on room temperature.
Directly related with the heating/cooling issue is the
control of air quality and humidity. To keep windows shut
when extreme outside conditions occur (heat or cold) and
EURASIP Journal on Embedded Systems 5
rely on artificial cooling and heating is of course a possibility.
However, a hygienic air change in a building has to be
guaranteed, in order to make users feel comfortable and
keep the share of CO2in the air at a healthy level. Air
quality can be assured by opening windows and doors
or airing the room with the help of ventilation facilities.
For the suggested system, it is important to weigh pros
and cons of the dierent possibilities and to draw the
right conclusion in accordance to energy optimization and
comfort preservation. Again, the action to be taken is
extremely dependant on weather conditions and orientation
of the building. If a wind sensor senses high wind, it will
not be an optimal solution to rely on natural ventilation in
occupied rooms. For unoccupied spaces, on the contrary, it
is of course an option to open windows and doors in order
to perform fast air circulation. On the other hand, natural
ventilation may be counterproductive if, for example, during
summertime direct solar radiation is experienced. Therefore,
a consideration of dierent possibilities again with respect to
energy eciency and comfort is necessary. Another example
is artificial air humidification which is one of the most energy
intensive areas in space conditioning, as the air has to be
cooled down to a low level to humidify it and then has to
be heated up to a comfort level again. In this case, natural
humidification can be taken into account by using ambient
air if the exterior weather conditions currently permit to do
so. The outdoor conditions can thereby be obtained with the
help of rain/humidity sensors or via some weather forecast
3.2. Visual Comfort. For the subjective feeling of comfort,
apart from thermal properties, the visual satisfaction is very
important. A system taking into account exterior conditions
can reduce the lighting necessities for rooms, thus saving
energy. One possibility is to improve the situation by
intelligent blind control. Aligning blinds according to the
position of the sun can lead to an improved lighting situation
inside a room. This condition can be measured by sensors
(e.g., a luxmeter) in order to ensure that a certain luminosity
is provided. The system can, for example, adjust the position
of blind lamellae. If this action does not generate a sucient
light intensity, additional artificial lighting can be used to
compensate the deficiency. However, it is always important
to keep in mind that a user has a need for self-determination.
In other words, the user does not like to be patronized by the
system. Therefore, actions concerning blind control should
preferably be performed when a room is unoccupied. Also
in this use case, the weather condition provided by weather
forecast services can be taken into account to assure visual
comfort. This way reflections can be minimized and a room
can be lightened according to its intended usage.
3.3. Energy-Ecient Operation of White Goods. Smart homes
and buildings are no longer focused exclusively on realizing
the direction of additionally integrating all kinds of devices
found in the home, in particular consumer electronics and
household appliances, in the automation networks. Two of
the most important standards that support this integration
are UPnP [7] and DLNA [8]. These electrical devices hold a
major share of the total energy consumption in the house-
hold [9], most obviously already due to the large number
found in present day homes. In fact, they contribute to the
energy balance in multiple ways (e.g., a washing machine
consumes hot water and electrical energy). For this reason, a
smart home system must also deal with a maximized energy-
ecient operation of the major appliances typically found in
the household (i.e., white goods such as washing machines,
dishwashers, refrigerators but also electrical water heaters).
Basically, the system must dierentiate between two major
types of appliances when reviewed under an energy perspec-
tive: devices that run continuously (e.g., the refrigerator) and
those that are active (a)periodically (e.g., a dishwasher). For
devices belonging to the first kind, only their operation may
be optimized, that is, the amount of energy consumed during
their regular use may be reduced. In case of a refrigerator, this
could mean that its cooling power and thus the consumed
energy are automatically adapted with regard to its content.
If, for example, the refrigerator is filled 90%, the cooling will
require more electrical energy than at the beginning of the
week when it is only filled 20%. The amount of food could
be detected automatically and used as an input parameter
for a control strategy. This approach is also applicable to
the latter category of devices, for example, a dishwasher
programme (water temperature and duration) can of course
be tailored to the amount and type of dishes inside. However,
the ThinkHome system oers much more powerful tools for
energy optimization. Once all appliances are integrated in the
smart home system, the system is able to determine the most
ecient starting time for this class of devices. For example,
the start time of a dishwasher can be aligned with the weather
forecast: if there is a high possibility for sunshine around
noon, the energy for the dishwasher can be obtained from the
photovoltaic system installed at the rooftop, which justifies a
delay of the scheduled start (if there are no other constraints
such as people coming home early). Similarly, the hot water
needed for the washing machine can be generated by solar
panels. While these examples represent the most sensible use
of local energy producers, it can easily be extended to interact
with smart grid and demand side management applications,
as these deal with distribution or time adjustments of loads
in general.
3.4. Energy-Ecient Operation of Brown Goods. Consumer
electronics are devices of everyday use that operate with
electrical energy. Often, they are related to user entertain-
ment. Therefore, the comfort aspect plays a significant role
in associated smart home use cases. From a technical point
of view, most devices only oer two modes on how energy
can be saved. One is the widely implemented stand-by mode
which however is highly disputed for its sustainability, as
energy in the order of 2% up to more than half of the
amount of regular operation may still be consumed. The
other option is to completely turn othe device and, in
the best case, to even separate the loads from the electrical
circuit. Unlike household appliances, it is also not possible to
defer the operation of consumer electronics to times when
excess energy is available.
6EURASIP Journal on Embedded Systems
Basically, the task of turning ocurrently unused devices
does not require a sophisticated system like ThinkHome.
However, it shows that a manual intervention is very often
skipped, most likely due to comfort reasons and also not
last due to the sheer number of devices typically found
in the home. In this case, the context awareness of a
smart home comes to help. Through knowledge on room
usage/occupancy, devices of a room can be turned o
automatically if nobody is present. A more advanced use case
features a layered approach, which first puts the devices in a
stand-by mode for a defined time, and only afterwards turns
them ocompletely. For example, leaving the room during
a commercial break on TV will not instantly lead to turning
othe TV, but the intelligent system will wait for some time
(and also watch for other activities, e.g., the user going to
bed) and then re-evaluate the situation. The system also has
to be capable of handling exceptions, for example, the VCR,
which must only be turned oif it is not recording. Likewise,
it can be powered on right in time before a recording event is
3.5. Miscellaneous Services. Apart from the major use cases
described above, there are some additional services that
can be achieved by a smart home automation system. One
application could be a presence simulation performed most
energy eciently by the smart home. Another functionality
is irrigating the garden and surroundings with respect to the
weather forecast. If, for example, a high probability of rain is
predicted for the evening, the irrigation of the garden may be
delayed. Afterwards, rain sensors can be used as confirmation
or denial of the forecast, rescheduling the irrigation task if
necessary. This behavior, apart from it being energy ecient,
leads to an overall resource-ecient operation as also the
water usage of the smart home is reduced. Moreover, the
comfort of the users is increased as they are relieved from
manually performing these optimization tasks. The system
can also be exploited to increase the user’s awareness of
energy consumption by providing tailored feedback through
consumer electronic devices. For example, it is possible
to visualize a user’s electricity consumption on the TV or
to generate detailed reports of the energy demand over a
specified period. It is also imaginable that users can define
a time for regular feedback as well as to select which loads
to monitor. Finally, another savings potential arises from
the fact that computers and all other smart home devices
produce heat. This heat has to be removed from devices but
could subsequently be converted by a heat exchanger and
used as supplementary energy-source in other parts of the
Of course the depicted controls in the white and brown
goods as well as miscellaneous area assume an extensive
integration into a home automation network. Some of the
explained functionalities are not yet readily available as o-
the-shelf products, but it can be expected to reach the desired
level of integration in the near future. Some first approach
can be seen in the technology described in [10] which allows
to intervene in the operation mode of connected electric
consumer goods. This way the stand-by energy demand of
devices can be extensively reduced and also a feedback to
the user about the energy demand of dierent devices can
be realized. Integration of white goods into a home network
such as it is provided by a KNX system is described in [11].
Overall, considerable progress in this area can be expected.
Therefore, the use cases of white and brown goods portrayed
oriented; however, they will not be fictional for long when
observing the prospering market of smart home equipment.
4. Knowledge Base: Ontology
In information systems, the division of a domain into
relevant concepts and its formal representation is known
as ontology [12]. The ThinkHome ontology can be seen as
basis for the proposed system. All data has to be stored and
provided in an intelligent way, supplying the system with
needed knowledge. For the storage of information it was
decided to use the Web Ontology Language (OWL), mainly
because of its formal definition and reasoning capabilities.
Furthermore, OWL is one major technology of the so-called
Semantic Web. This additionally supports the openness of
the ThinkHome knowledge representation.
As already mentioned, an OWL datastore contains dif-
ferent constructs to create a formal representation of knowl-
edge. The model, which is similar to a database scheme in
database design, is constructed by concepts and properties. A
concept defines a general idea of a possible item in the defined
knowledge base. For the suggested ThinkHome ontol-
ogy, such concepts are for example WeatherInformation
including all data concerning immediate exterior circum-
stances or HumanActor describing the group of human sys-
tem users. In most ontologies constructed from scratch, it is
desired to organize the identified concepts in a subsumption
hierarchy, which means in a superclass/subclass connection.
Properties are the relations between these concepts and can
be dierentiated in two kinds: object properties which estab-
lish connections between dierent concepts and datatype
properties which connect concepts with values of a specified
datatype. The last basic elements which represent the data
are individuals. These are distinct from the conceptual model
and act as concrete instantiations. For example, in the field
of building information this would be a particular wall
separating two defined rooms or a specific window type.
In addition to defining simple relations, several logical
restrictions can be put on these basic elements as to create
more complex dependencies. One example would be an
anonymous superclass restriction, which allows membership
in a class to be defined through logically combined properties
of a set of individuals.
OWL, in the majority of the cases, is restricted to some
form of logic such as description logics (DL) in order to make
it decidable. This means when DL is enforced, a so-called DL-
reasoner (e.g., Pellet [13]) can infer new information from
the ontology. As OWL is an open standard, ontology reuse as
well as integration into other projects is possible.
The vision of ThinkHome is to create a comprehensive
knowledge base which includes all the dierent concepts
needed to realize energy ecient, intelligent control mech-
anisms. The information base brings together dierent
EURASIP Journal on Embedded Systems 7
(e.g., layout, spaces,
walls, materials)
(e.g., schedules,
preferences, contexts)
(e.g., system processes,
user activities)
(e.g., white goods, brown goods,
building automation services)
(e.g., environmental impact,
energy providers)
(e.g., thermal comfort,
visual comfort)
(e.g., weather, climate)
Figure 2:Knowledgebasetoplevelconcepts.
branches of control information which all can be seen as
universe of discourse for the intelligent multiagent system.
The multiagent society can subsequently query the facts
stored in the ontology, thus enabling intelligent decision
Figure 2 shows the main branches of the ontology. This
division may not be seen as physical separation of knowledge,
but merely as logical segmentation of core concepts. First
and foremost the storage of building information is of great
importance. As already discussed in Section 3, the storage
of building characteristics can support optimized control
strategies striving for energy-ecient operation of the smart
home. It is not feasible for a user to enter all these values
manually due to the huge eort and lack of knowledge.
Thus, an automatic approach is favored. Therefore, for the
ThinkHome system, the inclusion of data stored in a building
information model (BIM) was considered.
A BIM is a data exchange format used by architects,
construction engineers, and building physicists among other
parties involved in the construction process of a building.
Each of these stakeholders adds domain knowledge to a com-
mon model which keeps information of the whole building
lifecycle (except the operational phase). As a consequence,
the model serves as a valuable source of information. There
exist several open formats of BIM, where the Industry
Foundation Classes (IFC) and the Green Building XML
(gbXML) can be seen as the most popular ones today
[14]. gbXML was chosen for application in ThinkHome,
because the format focuses on the exchange of information
for energy simulation and calculation, and therefore stores
facts that are helpful for the focal point of the proposed
system. Through the information retrieved from the BIM,
we obtain enough concepts to model the whole building
including wall layers, window sizes and types, door sizes
and positions, room area and volume as well as assigned
room purpose and orientation of the building. Subsequently,
exact calculation of the building behavior with respect to
thermal mass and room arrangement becomes possible. This
is especially beneficial for an energy-ecient provision of
thermal comfort (cf. Section 3).
In the ThinkHome project, a transformation from
gbXML to the OWL language format was carried out by
Extensible Stylesheet Language Transformation (XSLT) doc-
uments. This straightforward approach allows to integrate
all data already collected by former engineering parties and
store it in an intelligent way as OWL document. The Web
Ontology Language allows to classify the concepts retrieved
from gbXML and, due to the formal definition of the
language, also reasoning on the data becomes possible.
Apart from concepts relating to the building, also
actor information about the users of the system has to be
considered. Users in this case can be either human users,
but also system agents. The reason for this is that the
ontology builds the foundation of a multiagent system in
which intelligent actors can take autonomous actions on
behalf of the users. For humans, the knowledge base must
know dierent characteristics (e.g., age, gender) and also
keep a user profile (cf. Sections 5and 6). In the user profile,
the preferences of the users are stored. These profiles are
an aggregation of atomic actions residing in the ontology as
Aprocess is a concept containing elementary operations
that are used to describe the users’ activities. Certainly also
basic system processes are kept in this part of the ontology.
Very important, with respect to the use cases depicted earlier,
is to consider exterior influences. These weather and climate
data can be used to infer the proper action and perform tasks
most energy eciently. In addition, this information can be
exploited in order to guarantee user comfort, for example,
by natural lighting through sunlight (cf. Section 3). Comfort
information is a smaller part of the ontology which neverthe-
of elementary measurement units (e.g., temperature, humid-
ity, luminosity) and therefore provides a notion of comfort
to the system. Most of the measures can be retrieved from
the building information unit, as the data imported from
gbXML includes a vast amount of measurement units of
any kind. In the energy information branch reside dierent
available energy providers and their trading conditions.
This information is especially valuable when envisioning the
8EURASIP Journal on Embedded Systems
integration of the ThinkHome system into a smart grid, as
the ontology can provide the momentarily best option for
energy consumption or recovery. This part of the ontology
also keeps energy schedules for dierent occupancy states
and scenarios (e.g., day, night, weekends, holidays) and this
way allows to anticipate consumption peaks. Furthermore,
it is important to have an idea of the provided building
automation services, as well as equipment available in the
smart home. This resource information branch includes white
goods, brown goods, and automation networks hosting
lighting, shading as well as heating, ventilation, and air
conditioning (HVAC) devices. As the automation networks
can be of dierent types, protocols, and manufacturers, it
is valuable to represent them as concepts in an ontology.
This way, their definition can be generalized, which in turn
supports the transparent integration and communication
across the dierent networks. In addition, energy producers
like solar collectors or a thermal heat pump are stored in this
section. Hence, a complete model of the energy consuming
and producing landscape available in the building is depicted
in the knowledge base [15].
Especially for the last core section, approaches dealing
with dynamic data and historization of information have to
be kept in mind. A recording of historic sensor data can be
valuable for performing trend analysis or generating updated
occupancy profiles as pointed out in Section 6. As the
described knowledge base can only provide an instantaneous
reflection of the system’s state, a proper transition into a
historical permanent storage becomes necessary. Obviously,
not all of the information needs to be represented as
historical data as large amounts of information are known to
be highly static (e.g., building information). Therefore, just
a subpart of the global knowledge base has to be considered
for historization. Possible comprehensive environments for
managing large-scale ontologies as RDF triple store are the
Virtuoso Universal Server Project [16], as well as the JENA
Semantic Web Framework [17].
4.1. Benefits of Using OWL
4.1.1. Query Language. Additionally to an intelligent storage
of building and process information, it is of course important
to be able to question the knowledge store for these data.
Just like SQL being the query language of relational database
systems, SPARQL [18] is the interrogation mechanism of the
Resource Description Framework (RDF). Furthermore, as
RDF is the foundation of OWL, the SPARQL language can
subsequently be used to query the ThinkHome knowledge
base. RDF stores data as triples in a labeled-directed graph.
As a consequence, SPARQL works on graphs and triples
which can be combined using variables. For the ThinkHome
system, it becomes possible to retrieve selected information
about the building and ongoing processes with the help
of this query language. For example, with the information
retrieved from gbXML and stored in the ontology, it becomes
possible to find out specific information of a room or the
whole building. A simple SPARQL query can extract areas
and volumes as well as the appropriate measurement units of
the dierent rooms in the building (cf. Listing 1).
SELECT ?id ?name ?a ?aunit ?vol ?volunit
{?gbXML gbOWL:hasAreaUnitValue ?aunit.
?gbXML gbOWL:hasVolumeUnitValue ?volunit.
?area gbOWL:hasNativeValue ?a.
?volume gbOWL:hasNativeValue ?vol.
?spc gbOWL:containsArea ?area.
?spc gbOWL:containsVolume ?volume.
?spc gbOWL:hasIdValue ?id.
?spc gbOWL:hasNameValue ?name }
Listing 1: SPARQL Query: Room Areas and Volumes.
This information alone can already be used to optimize
the on/oheating schedule according to the space that has to
be heated. Similar queries can be created to determine which
rooms are adjacent to each other and to obtain the thickness
as well as material of interior and exterior walls. With the
data retrieved from the gbXML model, it is also possible to
exactly determine the position of windows and doors and
therefore take sunlight into account to reach thermal and
visual comfort as previously discussed in Section 3.
An update of specific data triples in the ontology can
be accomplished by SPARQL/Update queries (SPARUL).
With the help of this extension of the SPARQL language, it
becomes possible to delete and insert triples in RDF data
models. Although this addition is not yet a standard for the
World Wide Web Consortium (W3C), it is already supported
by major Semantic Web technologies like the JENA Semantic
Web Framework and the Virtuoso server.
4.1.2. Inference. One of the main concepts of OWL ontolo-
gies is inference. This ability can be used to perform
subsumption reasoning as well as inferring new information
out of the stored data. An example is considering weather
conditions when choosing an appropriate cooling method.
Not every cooling technique is to be allowed for all dierent
weather situations, as it is obviously not desired to rely
on natural ventilation when a thunderstorm with heavy
rain and wind is currently taking place outside. Therefore,
possible weather situations are classified and stored in the
ThinkHome ontology as can be seen in Figure 3.The
concepts shown are general classifications, as the particular
weather conditions in OWL are stored as individuals. As
already mentioned, it is possible to reason upon the stored
data with the help of a reasoner and subsequently infer new
For example, if currently a badweather condition is
experienced and an agent pursues a cooling task for a specific
room, it is beneficial to know which cooling methods are
possible with respect to the current WeatherSituation. Some
concept in the ontology can model exactly this situation
(cf. Figure 4). In this case, a class CoolingBadCold is
provided, which members are defined to be in the class
EURASIP Journal on Embedded Systems 9
Humidit y
Figure 3: Weather and process information in the ThinkHome
which permits a bad and cold WeatherSituation and are
not heating processes (as the agent is searching for current
possibilities to cool the room). Therefore, all individuals of
this anonymous superclass are to be members of the defined
class CoolingBadCold. As can be seen in the members
section of Figure 4, the reasoning mechanism of the ontol-
ogy can automatically infer two individuals, which denote
processes to be possible in this situation: AirCondition
and VentilationExteriorAir. Another cooling process
defined in the ontology, namely, OpenWindow, is not inferred
to be a member as this action should just be performed in a
calm WeatherSituation.
This use case shall underline the manifold possibilities
that emerge with the application of an OWL ontology.
SPARQL queries, as described before, tend to become inher-
ently easier when ontology reasoning capabilities are used
and properly defined concepts are provided. Besides, the
described model allows to integrate new weather situations
or system processes into the model, which can subsequently
Figure 4: Cooling options during a bad weather situation.
be included in the result set according to the logical
dependencies between the OWL classes and properties.
This makes the ThinkHome system highly flexible, as, for
example, dierent climates and weather conditions can easily
be added.
5. Agent Framework
To realize optimized control strategies that allow maximizing
energy eciency and user comfort simultaneously and
automatically, methods from AI need to be employed.
An excellent means are multiagent systems, that are not
only a software engineering paradigm, but a method that
inherently supports distributed intelligence, interaction and
cooperation to act towards defined goals [19]. Agent-based
systems are further characterized by cooperative problem
solving in which some or all agents may take part. Moreover,
MAS is designed to encapsulate software parts in agents that
can be maintained or exchanged independently and easily.
In ThinkHome, the MAS has the main task to realize
advanced control strategies. Thus, it bears the artificial
intelligence part in it, which decides on the control strategies
and their parameters. Furthermore, it integrates auxiliary
data sources and implements context inference as well as
conflict resolution services. The MAS is inhabited by a
number of specialized agents that are responsible of solving
dierent problem aspects. These agents follow the Belief-
Desire-Intention (BDI) architecture model [20]. The overall
solution is obtained by cooperation among the agents to
solve some problem where some or all agents may take part.
The set of dierent agents is called agent society. All agents are
interconnected by means of an agent-based framework that
hosts the agents and provides services for communication
and data exchange among them. A prominent example of
such a framework is the Java Agent DEvelopment Framework
(JADE) [21].
The sustainable operation of ThinkHome is achieved
by the system constantly striving to perform an optimal
mapping between the current smart home state, the given
user goals (i.e., user comfort), and energy eciency. To
obtain these data, access to the knowledge base is required.
10 EURASIP Journal on Embedded Systems
Therefore, the agent-based system implements interfaces to
the underlying ontology. For interaction with the physical
environment, also an interface to the building automation
systems of the smart home is designed.
The ThinkHome MAS is specified following the
Prometheus methodology [22]. Prometheus provides formal
guidelines and a formal notation for a detailed agent and
system architecture specification. It proposes an iterative
process, during which several design artifacts are created.
Prometheus accompanies the specification process from the
begin of the design until the implementation. Throughout
the specification process, support by a specific design tool
named Prometheus Design Tool (PDT (Available at: http:// is available. At the end, a
formal specification of the multiagent system is obtained,
that can now be transformed into programming concepts of
dierent agent-oriented programming languages.
The procedure of the Prometheus methodology is well
summarized by Gascuena and Fernandez-Caballero in [23].
Following the methodology, the first step is a (informal)
description of the system purpose and functionality called
“system specification phase.” The main goal is to first sketch
the system functionality and purpose, and afterwards to
refine it with the help of use case scenarios. In this work, the
system description can be found in Section 2 and a selection
of use case scenarios is presented in Section 3. Based on the
system overview, the major system goals are derived and
hierarchically grouped in the next step. This leads to the
goal overview diagram shown in Figure 5, which presents
a hierarchical goal decomposition of the system. Goals are
indicate further subgoals. Below a goal, the key words AND
or OR are shown that indicate whether all subgoals must be
fulfilled to achieve the root goal (AND)orifitissucient that
one (or more) subgoals are achieved (OR). During this design
stage, Prometheus puts the focus more on completeness (i.e.,
to cover all system goals) than on full correctness of the
hierarchy or the decomposition, respectively.
Once the system specification exists, the next step of the
methodology, the “architectural design phase,” starts. Now it
is important to derive the agents out of the previous artifacts,
and to model their interaction. An important outcome of
this phase is the data coupling diagram which prepares the
aggregation of system functions into dierent agents. The
intention is to identify functionalities that logically belong
together (i.e., that use the same data and are coupled) and
that thus can be modeled and implemented as one agent
type. The outcome is a set of agent roles of the system. Among
the agent society, a very loose coupling is targeted (e.g., to
allow their distribution to dierent devices), while within a
single agent a high cohesion is sought which indicates that
the related functionalities have been grouped (e.g., beneficial
for the data flow in the system). In ThinkHome, several
dierent agent roles can be dierentiated. The following list
gives an overview of the main roles (Note, that a single agent
type may represent a set of agents that together solve the
problem indicated by the name.) that are mandatory for a
successful operation of our system. The dierent agent tasks
are described in natural language.
(i) Control Agent. The Control Agent is the core point
for the sustainable, energy-ecient operation of the
smart home. It is responsible for execution of the
intelligent control strategies that control the building
state. For this purpose, the agent takes into consider-
ation the global goals, user preferences, the current
system state, and auxiliary data (e.g., current solar
radiation) to compute appropriate actions for the
underlying building automation system. The control
decisions will be made upon both simple control
algorithms as well as using artificially intelligent ones,
for example, artificial neural networks or fuzzy logic
[24]. To master this crucial task, the Control Agent
acquires information from several other agents in the
system, striving to get a global view of the whole
system state.
For example, the agent could be informed that a user
will come home in one hour (cf. Section 6,where
one possibility to generate this information, namely,
profile generation, is presented). The control agent
then obtains user comfort values, current sensor
values from the building automation system, and
additional semantic information that is contained in
the KB. The latter is used to enrich the available
data and hence get a more complete model of the
system state (e.g., request a list of current cooling
possibilities for the living room). After computation
of an appropriate control strategy, it can be executed
by the automation system.
(ii) User Agent. The User Agent acts on behalf of users
and has the goal to enforce comfortable environ-
mental conditions for its owner. Hence, each system
user has its own user agent which advocates the
preferences of its user within the system. The design
of the user agent follows the notion that to control
the indoor conditions of a building in an energy-
ecient way, it is most important to reduce the
control eorts to the lowest amount possible so
that the users still feel comfortable. Therefore, it is
mandatory to be aware of the presence, preferences,
and habits of all residents, and also to predict future
user actions (e.g., computing an occupancy profile
for a user). In ThinkHome, this information is kept
in the User Agent. This agent further embeds a
learning component that is responsible for learning
the preferred environmental conditions, habits as
well as typical situations and scenarios of its owner
during operation. In this task, it is supported by
the Context Inference Agent. Additionally, the agent
manages a user profile which mainly covers comfort
and other preferences, schedules as well as global
parameters (e.g., the importance of comfort versus
energy eciency to this user). It also accepts user
feedback and provides this feedback to the control
agent which can incorporate it in its control strategy.
Since not all possible users are known to the system a
priori, persons that are not registered in ThinkHome
(e.g., guests) are assigned an anonymous, temporary
EURASIP Journal on Embedded Systems 11
Use solar energy
Energy-ecient operation
Use outside air
Find ecient schedule for hot water generation
Ecient schedule for heating/cooling/ventilation
Ecient operation of white ware
Foresee when user goes out and comes home
Get preferred tempe rature
Goal of detect entering a room Goal of detect leaving a room
Foresee preferred light level
Get preferred light intensity
Appropriate light level
Assure that enough hot water is available
Visual comfort
User comfort
Goal of treat
Tra ck pr ob lem s wi th
User can overrule system
Thermal comfort
Goal of appropriate temperature
Goal of appropriate air quality
Foresee preferred room tem perature
for other sub-systems
Present in
Set light intensity
Utilize good weather
Foresee user’sbehavior
Ecient operation of brown ware
Perceiveuser’s reactions to system decisions
Figure 5: ThinkHome goal overview diagram (partly shown).
User Agent that assumes default values and is dis-
patched to cater for his/her needs during the visit.
(iii) Global Goals Agent. Similar to the User Agent,
this agent advocates the global goals when control
decisions shall be made in the MAS. It is a key
component for the realization of energy-ecient
building operation. While the whole MAS is designed
to work collectively towards the global goals, this
agent strives to enforce certain global goals policies.
For example, if energy eciency is given a very
high priority by the user, the agent could insist
to give the comfort parameters less importance (or
inform the user if the deviation of both goals exceeds
a threshold). It is therefore also concerned with,
for example, the calculation of the energy impact
of certain measures, so that it can recommend or
(iv) Context Inference Agent. The agent can set actions in
context with users, location, and time, that is, it can
identify activities and build a model of the current
situation. This context inference is required for an
adaptive, intelligent building control. For example,
persons can be identified when entering the building,
tracked within the building, and their location is
continuously reported to other agents. These can
then act upon this information, for example, turn o
the lights when all persons left a room. Furthermore,
it is important to put user actions in context with
the current building state in order to build a better
user profile. For example, someone may not like
to have the window tilted during nighttime and
therefore close it manually. The system can recognize
this action and relate it to a control decision that
was executed automatically just before and can thus
adapt the control strategy to comply to this user’s
(v) Auxiliary Data Agent. This agent provides an inter-
face to integrate additional data from miscellaneous
sources, for example, from Internet-based web ser-
vices. A typical example is the retrieval of weather
forecasts and also severe weather warnings which
can be obtained from a local weather station or
over the Internet. Another possibility is the access of
current energy prices, the announcement of current
excess energy to other ThinkHome houses, or the
implementation of demand response mechanisms
(vi) KB Interface Agent. The agent interfaces to the
knowledge base and handles all data exchange across
the system parts. If initiated by other agents, it
uses SPARQL queries to extract information from
the knowledge base. The obtained information is
parsed, optimized for the use by the other agents, and
communicated back to them. In the other direction,
information may also flow from the MAS into the
knowledge base. In this case, the process is simply
reversed, that is, the information is received from
other agents and transformed to comply with the
knowledge base. For updating the ontology, SPARUL
(SPARQL/Update) queries are used.
(vii) BAS Interface Agent. The BAS Interface Agent acts
as interface between the agent society and the
underlying automation system of the smart home.
On one hand, this concerns the execution of the
control strategies computed by the Control Agent.
It therefore sends data to the BAS controllers to
achieve or keep the desired environmental conditions
in the building. On the other hand, it functions
as a feedback interface from the building back to
the ThinkHome system. This includes the sensing of
initiating updates in the knowledge base, and gener-
ally collecting all information that is requested by the
MAS from the automation devices.
The system specification phase ends with a detailed
description of the agents, which also marks the end of
the agent design. In the following “detailed design phase,
12 EURASIP Journal on Embedded Systems
Prometheus provides an approach on how to transform
the design artifacts into concepts of the JACK agent
programming language [26]. This step is obviously very
implementation related and furthermore JACK technology
specific. However, for the ThinkHome system, JACK does
not constitute the first choice for programming the agents.
This is mainly due to the fact that JACK is a commercial
product, for which considerable license fees apply and the
implementation of the framework is not openly available.
Fortunately, the implementation of Prometheus agents is also
possible in other agent frameworks, in fact the specification
obtained from following the Prometheus methodology is
generic enough to be implemented in most common agent
frameworks. Therefore, the ThinkHome MAS will rely on
other established technologies such as JADE or an improved
version of JADE called AMES, that specifically targets
automation systems [27].
6. Control Strategies
The control strategies are the core part of the intelligent
operation of a ThinkHome building. They are responsible
for the calculation of all actions (switching commands,
start/stop times and many other parameters) that are
executed by the underlying building automation systems.
The control strategies are implemented in a dedicated agent
(cf. Section 5). Hence, they are embedded in the agent
framework and can access all information that is available
in the system, either directly, by communication, or even by
cooperation with other agents. In this section, an example
of a control strategy that provides increased comfort and
simultaneously reduce the energy consumption is presented.
For this purpose, an important aspect of the thermal comfort
use case is taken up again, namely, the calculation of
the setpoint temperature. The setpoint temperature defines
the ideal temperature of some space, when heating or
cooling is required. Normally, the setpoint temperature is
a parameter defined by manual control. However, some
buildings require a low level of heating during unoccupied
periods to avoid condensation/frost damage or to prevent
the building from becoming too cold while for others it may
be more important to reduce peak heating requirements at
startup. This lower temperature is referred to as set-back
temperature. Setpoint temperature schedules then operate
the heating equipment according to a (user) defined schedule
at night-time, weekends, or holidays during the heating
season. This self-regulation of heating and cooling systems is
an interesting possibility that can be exploited to improve the
energy performance. However, in a smart home system such
as ThinkHome, a realization can be even more ambitious.
The proposed setpoint temperature strategy is based on
the concept of profiles. The control strategy is implemented
and tested within the ThinkHome framework. For evaluation
purposes, a comparison by simulation of the following
very common strategies is performed: activation of the
heating system depending on simple occupancy data (On/o
Strategy), controlling the heating system based on a schedule
(Scheduled Strategy), and a combination of both of them
(Combined Strategy). To obtain a quantitative assessment,
also several energy and comfort performance indices are
6.1. Profiles and Profile Generation. A profile is a set of
characteristics or qualities that identify a type of behavior,
thing, or person. As far as control theory is concerned,
profiles help the control system to be aware of changes
in a particular scenario in which it has to take decisions.
Thus, profiles oer the control system abilities for better
prediction and more context awareness. The use of profiles
is based on the fact that inhabitants keep certain habits
and trends. Once a control system is aware of users’ habits
at home, optimized strategies for reaching a good balance
between comfort, and energy savings can be designed and
implemented. The process of getting significant profiles is
best supported by ubiquitous environments. A user’s desire
must be well understood and resulting patterns of specific
behaviors must be thoroughly analyzed [28]. It is important
to be aware that control algorithms that rely too much on
learning from a user’s behavior may run the risk of learning
bad control strategies [29]. It is, however, also known, that
most users behave in a conscious and consistent way [30].
In this approach, two kind of profiles are used: comfort
temperature profiles and occupancy profiles. While the first
class aims at representing the normal desired setpoint
temperatures of the inhabitants, the second class pursues
comfort as well as energy saving goals at the same time.
Using occupancy profiles, the control strategy allows the
smart system to predict occupancy and hence better adjust
the heating or cooling status with respect to this additional
Within the MAS structure, the control agent who
establishes the setpoint temperature embeds also the profile
management (while the flexibility of the structure would
allow a separation in a dedicated control agent and indepen-
is concerned, two distinct parts can be dierentiated: the
profile generator and a profile optimizer.Figure 6 depicts
the dierent actors that take part in the whole process.
It can be summarized as follows. Profiles or patterns are
obtained from real data monitored for long time spans. The
ThinkHome system may observe the energy usage by smart
metering and user behavior by means of the information
collected from sensors and with the help of context inference
mechanisms. This information is stored in the history
storage system (sensor database). At the end of each day,
the profile generator takes the data of the whole day and
generates a daily profile (corresponding to the passed day)
which is also kept within the history storage system (profile
database). As time passes, the daily profile count stored in
the databases rises. Therefore, in a second step, the profile
optimizer retrieves the profiles accumulated in the respective
database and processes them as inputs of the clustering
tool. As output, the clustering tool creates a representative
profile or pattern (in general an improved pattern), which is
subsequently transferred to the ThinkHome ontology. This
representative profile now defines the control strategy of
the next day, meaning that the control agent will use it
to compute its decisions. Apart from setpoint temperature
EURASIP Journal on Embedded Systems 13
Tab le 1: Controller strategy.
Case Occ OccPtatbsetpoint temperature
II 0 1 <twset-back temperature
III 0 1 two
IV 0 0 <tpComfortP
VI 0 0<twset-back temperature
VII 1 0 — — ComfortP
Sensor data
BAS interface
Control agent Profile
KB interface
User nt
Profile data
History storageKnowledge baseHistory storage
Global goals
agent Auxiliary
data agent
Figure 6: Setpoint temperature control within ThinkHome.
control, this process is also applicable for all other kinds of
minimum and nonessential variations.
The clustering tool works on self-organizing maps
(SOMs)—also known as Kohonen networks—that are com-
monly applied to obtain a pattern on input databases. SOMs
have the capability to classify input samples in groups, as
well as to generate a representative sample or model of each
classification group. Provided that users keep certain habits,
the clustering algorithm will calculate an output element or
profile which represents most of the input samples (daily
profiles). Thus, the tool is able to assess if the output profile is
suciently representative in a quantitative way. It is useful for
the control system to know the level of reliability of the pro-
file as it will take strategic decisions depending on this assess-
ment. Since tools based on SOMs have shown problems with
outliers [29], enhanced SOMs have been introduced [31].
6.2. Strategies. Based on the comfort temperature profile and
the occupancy profile, the strategies the controller executes
are summarized in Tabl e 1 . Occ refers to instantaneous
occupancy detected by sensors whereas OccPis related to
the current occupancy profile prediction. twis the waiting
period and defines the time when set-back temperature has
to be applied. tpis the preparation time and defines how
long it takes a specific room or space to be in fine climatic
conditions. It mainly depends on the layout and structure
of the building, the heating system, weather conditions, the
resulting inertia of the heating system, and other parameters.
tastands for the time passed since the last occupancy change
took place, while tbrefers to the amount of time that will still
pass until the next change in the corresponding occupancy
profile is expected. The notation marks a falling edge.
The controller not only generates output for the underlying
HVAC system (comfort mode, economy mode, on/o)but
also for the profile generator (ComfortP).
Basically, the smart system applies the temperature
indicated by the comfort temperature profile as long as the
system detects presence in the room/house (Cases I and VII).
The rest of the cases follow the typical recommendations
of heating/cooling experts for comfort and energy savings,
trying to take advantage of the information oered by the
occupancy profile.
6.3. Simulation Environment and Results. The simulation
environment is based on MATLAB/Simulink and the HAM-
Lab tools (HAMBase). HAMBase is a simulation model for
the heat and vapor flows in a building. With the model,
the indoor temperature, indoor air humidity and energy
use for heating and cooling of a multizone building can be
simulated. The physics of the HAMBase model is based on
ELAN, a computer model for building energy design [30].
More recently, the ELAN model together with an analog
hygric model, has been implemented in MATLAB, resulting
in the current HAMBase model. HAMBase is part of the
HAMLab tools [32], a complete set of MATLAB files for
the implementation of a Heat, Air and Moisture Laboratory.
Figure 7 gives an overview of the used simulation environ-
ment. Area 1 marks the building model. Area 2 determines
the time step, selects the strategy, and fixes the heating
setback temperature. Area 3 is the core of the simulation
and represents the setpoint temperature control agent of
the smart system. It decides the next setpoint temperature
based on the time, selected strategy, occupancy data, and the
current occupancy profile (if the strategy based on profiles
is selected). Area 4 shows the heating controller, a PID
controller that takes the indoor and the setpoint temperature
as inputs and outputs of the heating power for the building
14 EURASIP Journal on Embedded Systems
Tab le 2: Simulation results.
Profiles On/oSchedule Combined Best value
Q(Kwh) 1.30 1.13 1.47 1.25 lowest
dT (C) 0.10 0.25 0.32 0.17 lowest
TiC (hours) 127.8 111.0 126.8 117.8 highest
TtC (hours) 11.3 28.0 12.2 21.3 lowest
Q(Kwh) 1.22 1.03 1.46 1.21 lowest
dT (C) 0.13 0.31 0.82 0.17 lowest
TiC (hours) 94.9 76.9 88.3 88.8 highest
TtC (hours) 16.1 34.1 22.8 22.2 lowest
Q: consumption
dT:dierence between real and desired temperature
TiC: time while the system keeps comfort conditions
TtC: time needed to reach comfort conditions.
model’s heating system. The components not circled are used
for visualization, data management, and storage.
Weather data for the simulation is obtained from real
weather databases (that are already supplied by HAMLab).
Data reflecting the occupancy are taken from Leako System
Database. This database actually stores data on the water
usage from more than 700 dwellings over the past 5 years,
but can also be exploited to provide occupancy data. Out
of the huge data amounts, five dwellings and 16 days are
selected and taken into account. For the simulation, the
setback temperature has been fixed to 18C and the comfort
temperature is set to 23C. The preparation time (tp)isfixed
to 1 hour, while for the waiting time (tw) 4 hours have been
assumed. As already mentioned, the heating controller is
designed as PID controller with the parameters KP=2,
KI=0.8, and KD=0.4.
The dierent strategies are compared by means of four
performance indices: total consumption of heating energy
over time Q,averagedierence between real temperature and
desired temperature when people are present dT, the total
time the system matches the comfort temperature TiCi,and
the necessary time to reach comfort temperatures TtCi.
Exemplary taken out of several test runs executed for
the five dwellings, Table 2 presents two of the simulation
results. It shows, that as long as only the energy savings are
valued, the On/ostrategy performs best, with the profile-
based strategy still being within reach. The explanation for
the extreme energy savings is the fact that heating is only
turned on if a presence is detected. Nevertheless, it can also
be observed that the comfort-related indices (dT,TiCi,and
TtCi) of the On/ostrategy for the given test cases are quite
bad and, thus, it must be concluded that users do not feel
fully comfortable in these situations. In contrast to that,
the strategy based on profiles exhibits the best performance
when focusing on the comfort indexes, thus confirming the
usefulness of our approach.
Figure 8 shows some characteristics of the strategy based
on profiles. Detail 1 shows a nondesirable (yet possible)
situation, in which the smart system does not expect people
coming home in a long time and decides to switch othe
heating. However, there is unexpected occupancy and the
system has to switch on again to reach comfort temperature
as soon as possible. In this case, the strategy shows the same
behavior as the On/oStrategy. Details 2 and 3 mark the
main advantage of the profile-based strategy: the profile is
a powerful tool to (correctly) predict the next occupancy and
thus the system adjusts the temperature before somebody
arrives. Detail 3 shows this fact and also how the comfort
temperature drops due to the profile (or a manual change in
the setpoint temperature). In addition, it changes to setback
when the dwelling becomes unoccupied but recovers comfort
values as soon as people return.
7. Related Work
There already exist some preliminary works which attempt
to integrate some aspects of a smart home with the help of
ontologies. Retkowitz and Pienkos [33] describe a possibility
to integrate heterogeneous services in smart homes. It is
based on an ontology mapping for semantically equivalent
service interfaces. In [34] the respective system architecture
is specified in more detail. Dierent service layers are
exemplified with the help of use cases. Main achievements
are the unified service interfaces that enable a continuous
specification, configuration and deployment process.
In [35] Chen et al. propose an ontology-based system
for a smart meeting room. They introduce several use cases
for a meeting room and employ context reasoning. Another
approach for ontology-grounded context reasoning is taken
in [36], where the suggested system uses OWL for context
modeling. Benta et al. [37] describe a multiagent system
working with an ontology mapping of the environment.
The work also focuses on context awareness as well as user
tracking and especially user behavior. Although these articles
show some promising approaches in the field of context
modeling and context awareness, architectural or energy
deliberations is not suciently considered. The authors of
[38] propose a system which is based on J2EE and also uses
multiple agents in combination with an ontology. The focus
of their system is put on the industrial sector, in particular
targeting logistics and scheduling applications. Nevertheless,
their study is a rare example of the practical application of
an ontology-based multiagent approach in a large real-world
EURASIP Journal on Embedded Systems 15
Copyright TU/e
1. Profiles
2. on/o
3. Scheduled
Matlab function
To w or ks p a c e 2
16 days
Tem p real
To workspace
PID controller
U U(E)
U U(E)
U U(E)
U U(E)
U U(E)
U U(E)
To workspace3
To workspace1
Qplant (W)
Gplants (kg/s)
HAMBase building model
JvS/Mdw 2008/03
Figure 7: Simulation environment for strategy comparison.
The article [39] proposes an ontology that allows a
vendor-independent representation of a domotic system
(DogOnt). In addition, the authors propose a reasoning
mechanism that supports the integration of new domotic
components into existing systems. However, the integration
inamultiagentsystemaswellasenergyeciency consider-
ations is not in explicit focus. The authors of [40] further
describe the design of a requirement ontology in order to
support an automatic design process of building automation
Also some works that at least touch the architectural
constraints for ontology-based smart home control can be
found. The DogOnt project mentions an extension to the
architectural domain for the proposed system but does
not implement it. The DomoML project [15]proposesa
taxonomy which emphasizes household appliances and takes
into account their location, but does not deal with the
building structure explicitly. The authors of [41]present
their ideas on how to map data represented in Industry
Foundation Classes to OWL. The proposed method is
exemplified on the Sydney Opera House [42]. The ontology
representation however mainly puts emphasis on the field of
facility management.
While not focusing on ontologies, an integrated ap-
proach can also be observed in the inHaus project in
Duisburg, Germany [43]. The IT infrastructure combines
dierent technologies (ZigBee, WLAN network, RSSI-based
people tracking system, UHF RFID gate, mobile LF- and
UHF-Reader units, etc.) with the help of a middleware layer.
In the context of inHaus, the authors of [44] also propose
a generic probabilistic reasoning framework for networked
homes based on ontologies, however, more than the specific
application in a smart home, a generic framework approach
is followed.
Some interesting works are also found in the field of
agent technologies applied to control a smart home. For
example, the University of Essex developed iDorm [45], an
intelligent dormitory that operates with multiple systems
and networks. It consists of an adaptive agent that uses
fuzzy algorithms to work in a lifelong learning mode,
assimilating the users’ needs and preferences. On the other
hand, works like HomePort [46] simply propose a way
to connect dierent home control systems through an
intelligent gateway, exclusively attending the broad variety of
existing technological solutions in the residential sector.
Putting focus on multiagent concepts, the OSGi (for-
merly Open Services Gateway initiative) platform has pro-
posed to implement an agent-based framework [47]. This
project pursues the integration of dierent domotic devices
allowing remote control and fault diagnosis. UPnP (Univer-
sal Plug and Play) in combination with an agent framework
is used for device discovery, registry, and management.
Multiagent architectures have also been proposed specif-
ically for wireless sensor networks [48]. Here, the eort
focuses on developing a cooperative and distributed control
system with conflict resolution and users’ behavior identifi-
cation capabilities under a wireless infrastructure (ZigBee).
Moreover, the multiagent concept can be understood in a
16 EURASIP Journal on Embedded Systems
Setpoint temp.
Indoor temp.
00.5 11.5 2 2.5
Time (seconds)
Temperature (C) and occupancy (0/1)
12 3
Outdoor temp.
Figure 8: Profile-based strategy.
dierent way. Chen and Tseng [49] define space agents,
which are distinguished by house zoning and commanded
by a governor agent. UPnP and Microsoft’s SCP (Simple
Control Protocol) are used to communicate and manage the
whole system. In the MavHome project, Cook et al. [50]
propose the use of an MAS in the home that is capable of
learning inhabitant behavior. Data is gathered using agents
that communicate with the help of the Common Object
Request Broker Architecture (CORBA). The relationship and
communication within an agent network is discussed in the
MASBO project (Multiagent System for Building Control)
[51]. It concentrates on the study of agents’ negotiation as a
technique to reach eective consensus of agents. With respect
to control strategies, Mozer’s Adaptive House in Colorado
[52] can be mentioned. Neural networks have been used for
control intelligence, targeting a home that programs itself.
The eort is mainly put on inferring patterns from the
inhabitants’ lifestyle and performing actions under predic-
tion assessments. Exploitation of energy savings potential is
however treated only as a subgoal.
Although all articles specialize on one topic or another
that is also relevant for ThinkHome, none of them touches
all important aspects of intelligent smart homes in a
comprehensive way. Dierent MAS are specified, but mostly
fail to ground agents in a knowledge base. Also most agent
systems propose some context awareness approach, but few
exploit the MAS to increase energy eciency. Ontology-
based approaches mainly support context awareness and
integration of heterogeneous sensor networks. Even if
extended with agent systems, important considerations on
building structure, user behavior, or energy-related topics are
still not modeled in a knowledge base.
Main advantage and distinguishing characteristic of the
ThinkHome system is therefore the comprehensive approach
that takes into account all important aspects collectively:
a knowledge representation modeling energy, user, context,
building, comfort and automation system aspects comple-
mented by an intelligent MAS that autonomously makes
use of these data to control the smart home in an energy-
ecient and comfort-oriented way. Nevertheless, selected
project parts were considered as a starting point for the
specification of the respective concepts in ThinkHome.
This concerns especially context awareness and artificial
intelligence mechanisms.
8. Conclusion and Outlook
This paper presented a comprehensive approach to fully
unleash the energy savings potential of smart homes. Novel
use cases that would technically already be feasible are not
executed due to a lack of information in the system, and
therefore act as a starting point for the system design. The
proposed system architecture builds on a knowledge base
that achieves the integration of previously unconsidered
information. Starting from a multitude of new parameters
coming from the architecture, engineering and construction
domain like materials, thermal properties, building layout
also integrates conventional data in a unified way. As we
have shown, by choosing an ontology to implement the
EURASIP Journal on Embedded Systems 17
knowledge base, we introduced a first part of the system
intelligence, namely, knowledge inference and reasoning, at a
very early design stage. It allowed us to make some decisions
already on the data level, thus facilitating the higher control
The comprehensive knowledge storage was comple-
mented by a multiagent system, that finally uses all the stored
knowledge to realize a more energy ecient building oper-
ation. As an example we showed how optimized profiling
schemes are embedded in the agent system and evaluated
the energy savings and comfort gains of this control strategy,
which showed very promising results. The design of our
multiagent system was also geared to build a modular
architecture that allows an easy integration of intelligent
control strategies, interfacing with additional (external) ser-
vices (e.g., a context inference service), as well as the flexible
extension and exchange of existing or new components.
Additionally, we showed that the ThinkHome approach
considers dierent aspects that too often have been neglected
in previous approaches.
(i) designing an open system, that (inter)operates on
open standards and open software and provides open
interfaces to other systems and domains,
(ii) centering the system on the user to increase user
acceptance, yet building an autonomous, not patron-
izing and unobtrusive system,
(iii) realizing an intelligent system that transparently inte-
grates dierent parts (devices, protocols, parameters,
data) to achieve higher energy eciency and comfort.
Still the work on such a comprehensive system will not
be finished soon. Future work will deal with fathoming
the possibilities how ThinkHome could most reasonably
be coupled with other systems, for example, smart grids
or demand side management applications. We will also
have a closer look on how multiple ThinkHome equipped
homes could be connected to be able to for example,
exchange learnt knowledge as well as to cooperate on energy
production/usage on a neighborhood or even district level.
Furthermore, a focus will be put on the evaluation and
integration of conflict resolution mechanisms (e.g., weight-
ing schemes, Markov diagrams, neuro-fuzzy approaches)
to resolve potential conflicts of dierent user goals as
well as of energy-ecient versus comfort-oriented build-
ing operation. Finally, the system implementation will be
accompanied by a refinement and extension of the presented
simulation framework. The simulation will be used to test
and evaluate the ThinkHome approach on the long term
and also to select and improve further intelligent control
The work presented in this paper was funded by the HdZ+
fund of the Austrian Research Promotion Agency FFG under
the project 822170.
[1] W. Kastner, G. Neugschwandtner, S. Soucek, and H. M.
Newman, “Communication systems for building automation
and control,Proceedings of the IEEE, vol. 93, no. 6, pp. 1178–
1203, 2005.
[2] D. Dietrich, G. Fodor, G. Zucker, and D. Bruckner, Eds.,
Simulating the Mind, Springer, New York, NY, USA, 2009.
[3] J. Borggaard, J. A. Burns, A. Surana, and L. Zietsman,
“Control, estimation and optimization of energy ecient
buildings,” in Proceedings of the American Control Conference
(ACC ’09), pp. 837–841, June 2009.
[4] K. Kappel, Persuasive technology: ambient information systems
for residential energy awareness, Ph.D. dissertation, Vienna
University of Technology, 2009.
[5] “Intelligent use of buildings’ energy information, a collabora-
tive project co-funded by the European Commission,” Project
[6] R. Janssen, “Towards energy ecient buildings in Europe
(final report),” 2005,
[7] B. A. Miller, T. Nixon, C. Tai, and M. D. Wood, “Home net-
working with Universal Plug and Play,” IEEE Communications
Magazine, vol. 39, no. 12, pp. 104–109, 2001.
[8] Digital Living Network Alliance (DLNA), “Networked device
interoperability guidelines,” 2006,
[9] European Commission Joint Research Center, “Electricity
consumption and eciency trends in the enlarged European
Union—status report,” Tech. Rep., Institute for Energy, 2006.
[10] “, Project Homepage, http://www.digi-
[11] G. Rowlands, “Going green: using KNX-based smart control
to save energy,” HiddenWires Magazine, http://hiddenwires
[12] T. R. Gruber, “A translation approach to portable ontology
specifications,Knowledge Acquisition, vol. 5, no. 2, pp. 199–
220, 1993.
[13] E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, and Y. Katz,
“Pellet: a practical OWL-DL reasoner,” Web S emant i c s , vol. 5,
no. 2, pp. 51–53, 2007.
[14] B. Dong, K. Lam, Y. Huang, and G. Dobbs, “A comparative
study of the IFC and gbXML informational infrastruc-
tures for data exchange in computational design support
environments,” in Proceedings of the International Building
Performance Simulation Association Conference (IBPSA ’07),
pp. 1530–1537, 2007.
[15] L. Sommaruga, A. Perri, and F. Furfari, “DomoML-env: an
ontology for human home interaction,” in Proceedings of the
Italian Semantic Web Workshop, pp. 14–16, 2005.
[16] “OpenLink Virtuoso Universal Server,
[17] “Jena—A Semantic Web Framework for Java,” Project Home-
[18] “SPARQL Query Language for RDF,” W3C Rec. January 2008,
[19] M. Wooldridge, An Introduction to Multiagent Systems,John
Wiley & Sons, New York, NY, USA, 2nd edition, 2009.
[20] M. Bratman, Intention, Plans, and Practical Reason,Harvard
University Press, Cambridge, Mass, USA, 1987.
[21] F. Bellifemine, G. Caire, A. Poggi, and G. Rimassa, “JADE: a
software framework for developing multi-agent applications.
Lessons learned,Information and Software Technology, vol. 50,
no. 1-2, pp. 10–21, 2008.
18 EURASIP Journal on Embedded Systems
[22] L. Padgham and M. Winiko,Developing Intelligent Agent
System—A Practical Guide, John Wiley & Sons, New York, NY,
USA, 2004.
[23] J. M. Gascuena and A. Fernandez-Caballero, “Prometheus
and INGENIAS agent methodologies: a complementary
approach,” in Agent-Oriented Software Engineering IX,Lecture
Notes in Computer Science, pp. 131–144, Springer, New York,
NY, USA, 2009.
[24] W. Kastner and M. J. Kofler, “Using AI to realize energy
ecient yet comfortable smart homes,” in Proceedings of
the IEEE International Workshop on Factory Communication
Systems, pp. 169–172, 2010.
[25] P. Palensky, F. Kupzog, A. A. Zaidi, and K. Zhou, “Modeling
domestic housing loads for demand response,” in Proceedings
of the 34th Annual Conference of the IEEE Industrial Electronics
Society (IECON ’08), pp. 2742–2747, November 2008.
[26] “JACK autonomous software,” Project Homepage,
[27] S. Theiss, V. Vasyutynskyy, and K. Kabitzsch, “AMES—a
resource-ecient platform for industrial agents,” in Pro-
ceedings of the 7th IEEE International Workshop on Factory
Communication Systems (WFCS ’08), pp. 405–413, May 2008.
[28] T. S. Ha, J. H. Jung, and S. Y. Oh, “Method to analyze
user behavior in home environment,Personal and Ubiquitous
Computing, vol. 10, no. 2-3, pp. 110–121, 2006.
[29] M. Foster and T. Oreszczyn, “Occupant control of passive sys-
tems: the use of Venetian blinds,” Building and Environment,
vol. 36, no. 2, pp. 149–155, 2001.
[30] D. Lindel¨
of, Bayesian optimization of visual comfort,Ph.D.
dissertation, Ecole Polytechnique Federale de Lausanne, 2007.
[31] F. Iglesias and W. Kastner, “Usage profiles for sustainable
buildings,” in Proceedings of the IEEE International Conference
on Emerging Technologies and Factory Automation, 2010.
[32] C. Kidd, R. Orr, G. Abowd et al., “The aware home: a living
laboratory for ubiquitous computing research,” in Proceedings
of the International Workshop on Cooperative Buildings. Inte-
grating Information, Organizations and Architecture, pp. 191–
198, 1999.
[33] D. Retkowitz and M. Pienkos, “Ontology-based configuration
of adaptive smart homes,” in Proceedings of the Workshop on
Reflective and Adaptive Middleware, pp. 11–16, 2008.
[34] D. Retkowitz and M. Stegelmann, “Dynamic adaptability for
smart environments,” in Proceedings of the International Con-
ference on Distributed Applications and Interoperable Systems,
R. Meier and S. Terzis, Eds., Lecture Notes in Computer
Science, pp. 154–167, Springer, 2008.
[35] H. Chen, F. Perich, D. Chakraborty, T. Finin, and A. Joshi,
“Intelligent agents meet semantic web in a smart meeting
room,” in Proceedings of the 3rd International Joint Conference
on Autonomous Agents and Multiagent Systems (AAMAS ’04),
pp. 854–861, July 2004.
[36] D. Ejigu, M. Scuturici, and L. Brunie, “An ontology-based
approach to context modeling and reasoning in pervasive
computing, in Proceedings of the 5th Annual IEEE Interna-
tional Conference on Pervasive Computing and Communica-
tions Workshops, pp. 14–19, March 2007.
[37] K.-I.Benta,A.Hoszu,L.V
acariu, and O. Cret¸, “Agent based
smart house platform with aective control,” in Proceedings of
the Euro American Conference on Telematics and Information
Systems: New Opportunities to Increase Digital Citizenship
(EATIS ’09), pp. 1–7, June 2009.
[38] J. Himo, P. Skobelev, and M. Wooldridge, “MAGENTA
technology: multi-agent systems for industrial logistics,” in
Proceedings of the 4th International Conference on Autonomous
Agents and Multi agent Systems (AAMAS ’05), pp. 60–66, July
[39] D. Bonino and F. Corno, “DogOnt—ontology modeling
for intelligent domotic environments,” in Proceedings of the
International Semantic Web Conference (ISWC ’08), pp. 790–
[40] S. Runde, H. Dibowski, A. Fay, and K. Kabitzsch, “A semantic
requirement ontology for the engineering of building automa-
tion systems by means of OWL,” in Proceedings of the IEEE
Conference on Emerging Technologies and Factory Automation
(ETFA ’09), pp. 413–420, 2009.
[41] H. Schevers and R. Drogemuller, “Converting the industry
foundation classes to the web ontology language,” in Proceed-
and Grid (SKG ’05), p. 73, November 2005.
[42] H. Schevers, J. Mitchell, P. Akhurst et al., “Towards digital
facility modelling for Sydney Opera House using IFC and
semantic web technology,Electronic Journal of Information
Technology in Construction, vol. 12, pp. 347–362, 2007.
[43] E. Naroska, “AMIGO middleware-based ambient-intelligence
user interaction,” in Proceedings of the KNX Scientific Confer-
ence, 2007.
[44] T. Dimitrov, J. Pauli, and E. Naroska, “A probabilistic reason-
ing framework for smart homes,” in Proceedings of the 5th
International Workshop on Middleware for Pervasive and Ad-
Hoc Computing (MPAC ’07), pp. 1–6, November 2007.
[45] H. Hagras, V. Callaghan, M. Colley, G. Clarke, A. Pounds-
Cornish, and H. Duman, “Creating an ambient-intelligence
environment using embedded agents,IEEE Intelligent Sys-
tems, vol. 19, no. 6, pp. 12–20, 2004.
[46] P. P. Madsen, “HomePort: an intelligent home control system,
in Proceedings of the 4th IEEE Conference on Industrial
Electronics and Applications (ICIEA ’09), pp. 2519–2523, May
[47] H. Zhang, F.-Y. Wang, and Y. Ai, “An OSGi and agent based
control system architecture for smart home,” in Proceedings of
the IEEE Networking, Sensing and Control (ICNSC ’05), pp. 13–
18, March 2005.
[48] P. Duan and H. Li, “ZigBee wireless sensor network based
multi- agent architecture in intelligent inhabited environ-
ments,” in Proceedings of the 4th International Conference on
Intelligent Environments (IE ’08), pp. 1–6, July 2008.
[49] W.-H. Chen and W.-S. Tseng, “A novel multi-agent framework
for the design of home automation,” in Proceedings of the
4th International Conference on Information Technology-New
Generations (ITNG ’07), pp. 277–281, April 2007.
an agent-based smart home,” in Proceedings of the IEEE
International Conference on Pervasive Computing and Commu-
nications, pp. 521–524, 2003.
[51] J. Duangsuwan and K. Liu, “Multi-agent control of shared
zones in intelligent buildings,” in Proceedings of the Interna-
tional Conference on Computer Science and Software Engineer-
ing (CSSE ’08), pp. 1238–1241, December 2008.
[52] M. Mozer, R. Dodier, D. Miller et al., “The adaptive house,
IEE Seminar Digests, vol. 1, no. 39, 2005.
The 2011 European Signal Processing Conference (EUSIPCOȬ2011) is the
nineteenth in a series of conferences promoted by the European Association for
Signal Processing (EURASIP, This year edition will take place
in Barcelona, capital city of Catalonia (Spain), and will be jointly organized by the
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) and the
Universitat Politècnica de Catalunya (UPC).
EUSIPCOȬ2011 will focus on key aspects of signal processing theory and
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relevance and originality. Accepted papers will be published in the EUSIPCO
proceedings and presented during the conference. Paper submissions, proposals
for tutorials and proposals for special sessions are invited in, but not limited to,
the following areas of interest.
Areas of Interest
• Audio and electroȬacoustics.
• Design, implementation, and applications of signal processing systems.
l d
Montserrat Nájar (UPC)
• Mu
ia signa
processing an
• Image and multidimensional signal processing.
• Signal detection and estimation.
• Sensor array and multiȬchannel signal processing.
• Sensor fusion in networked systems.
• Signal processing for communications.
• Medical imaging and image analysis.
• NonȬstationary, nonȬlinear and nonȬGaussian signal processing.
I d i l Li i & E hibi
Procedures to submit a paper and proposals for special sessions and tutorials will
be detailed at Submitted papers must be cameraȬready, no
more than 5 pages long, and conforming to the standard specified on the
EUSIPCO 2011 web site. First authors who are registered students can participate
in the best student paper competition.
P l f i l i
15 D2010
Proposalsȱforȱtutorials 18ȱFeb 2011
Electronicȱsubmissionȱofȱfullȱpapers 21ȱFeb 2011
Notificationȱofȱacceptance 23ȱMay 2011
SubmissionȱofȱcameraȬreadyȱpapers 6ȱJun 2011
... Likewise, Reinisch et al. (2010Reinisch et al. ( , 2011 propose an AI concept for smart homes as a dig-525 ital ecosystem. The smart home digital ecosystem comprises the knowledge base (KB) and multi-agent system (MAS), which has many AI components that work together on small goals to achieve the main energy-saving goal. ...
... Four Stage Framework (Lee et al., 2010) Identification of user activities based on active appliances states and finding irrelevant active appliances Energy conservation by turning off irrelevant active appliances Digital Ecosystem (Reinisch et al., 2010(Reinisch et al., , 2011 Prediction of smart home control strategies by KB Energy saving guaranteeing the user comfort. Z-Wave Automation (Tejani et al., 2011) Incorporation of smart control automation system Energy conservation using smart gateways Stochastic Based Predictor (Arghira et al., 2012) Energy Consumption Prediction Next day energy consumption prediction for user services OLA (Qela and Mouftah, 2012) Adding intelligence to a PCT for energy management ...
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Smart homes are equipped with easy-to-interact interfaces, providing a more comfortable living environment and less energy consumption. There are currently satisfactory approaches proposed to deliver adequate comfort and ease to smart home inhabitants through infrared sensors, motion sensors, and other similar technologies. However, the goal of reducing energy consumption is always a significant concern for smart home stakeholders. A detailed discussion about energy management techniques might open new leads for advanced research and even introduce more ways to improve existing methods since a summary of effective energy conservation techniques are helpful to get a quick overview of the state-of-the-art techniques. This review study aims to provide an overview of previously proposed techniques for energy conservation and energy-saving recommendations. We identify various critical features in energy conservation techniques, i.e., user energy profiling, appliance energy profiling, and off-peak load scheduling to perform a comparative analysis among different techniques. Then, we explain various energy conservation techniques, describe common and rare evaluation metrics, identify several techniques for realizing synthetic smart home energy consumption datasets, and provide a statistical analysis of the existing literature. The survey finally points out possible research directions which might lead to new inquiries in energy conservation research.
... ThinkHome by Reinisch et al. [22] describes a multi-agent system architecture with two main premises: ensuring energy efficiency at home and comfort optimisation. The control strategies realised by the multi-agent system are split into problem aspects which are directly mapped to the different agents of the framework: Control, Users, Global Goals, Context Inference, Auxiliary Data, Knowledge Base Interface and Building Automation System Interface. ...
... BonSAI [33] Modelling of service-oriented smart building. ThinkHome [22,34] Home energy assessment and device control. ...
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As prices on renewable energy electricity generation and storage technologies decrease, previous standard home energy end-users are also becoming producers (prosumers). Together with the increase of Smart Home automation and the need to manage the energy-related interaction between home energy consumers and Smart Grid through different Demand Response approaches, home energy management becomes a complex and multi-faceted problem, calling for an extensible, interoperable and secure solution. This work proposes a modular architecture for building a Smart Home Energy Management System, integrable with existing Home Automation Systems, that considers the use of standard interfaces for data communication, the implementation of security measures for the integration of the different components, as well as the use of semantic web technologies to integrate knowledge and build on it. Our proposal is finally validated through implementation in one real smart home test-bed, evaluating the system from a functional standpoint to demonstrate its ability to support our goals.
... These applications learn from the behaviors of residents daily life routine and schedule the loads as required by efficient decision making. The study in [66] and [67] provides AI-optimized smart home by using knowledge (consumer routine patterns or preferences) based information needed to achieve energy efficiency and user comfort. Moreover, cloud analytic-assisted smart meters were developed using advanced AI for Demandside management for Smart Homes [68]. ...
Electrical smart grids are units that supply electricity from power plants to the users to yield reduced costs, power failures/loss, and maximized energy management. Smart grids (SGs) are well-known devices due to their exceptional benefits such as bi-directional communication, stability, detection of power failures, and inter-connectivity with appliances for monitoring purposes. SGs are the outcome of different modern applications that are used for managing data and security, i.e., modeling, monitoring, optimization, and/or Artificial Intelligence. Hence, the importance of SGs as a research field is increasing with every passing year. This paper focuses on novel features and applications of smart grids in domestic and industrial sectors. Specifically, we focused on Genetic algorithm, Particle Swarm Optimization, and Grey Wolf Optimization to study the efforts made up till date for maximized energy management and cost minimization in SGs. Therefore, we collected 145 research works (2011 to 2022) in this systematic literature review. This research work aims to figure out different features and applications of SGs proposed in the last decade and investigate the trends in popularity of SGs for different regions of world. Our finding is that the most popular optimization algorithm being used by researchers to bring forward new solutions for energy management and cost effectiveness in SGs is Particle Swarm Optimization. We also provide a brief overview of objective functions and parameters used in the solutions for energy and cost effectiveness as well as discuss different open research challenges for future research works.
... Several studies have shown that energy efficiency can significantly reduce total energy consumption [4][5][6]. For these reasons, smart grids have a fundamental role in the efficient integration of energy generation and consumption. ...
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Smart grids are an alternative to minimize environmental impacts, such as CO2 emissions, and improve the efficiency of electricity consumption in buildings. Power grids enable adequate management and monitoring of consumption because of the periodic storage of measurements and easy access to them. In this scenario, an accurate prediction is a challenging task. Forecasting of consumption series is a defiant problem because data present linear and nonlinear patterns, and a dependence on external variables may be observed. Hybrid models are an alternative to mapping both patterns, which have been widely used to forecast load time series. Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression (SVR) models are used for this purpose, to map the linear and nonlinear patterns of the series, respectively. In this paper, a nonlinear optimized hybrid system based on ARIMA, SVR, and Particle Swarm Optimization (PSO) is proposed. The system can be divided into three steps. First, the linear patterns are predicted by the statistical model ARIMA. Then, the residual series is modeled using an optimized SVR, in which the parameters are selected from the PSO. One particularity from the proposal is to incorporate the choice of the topology and the inertia coefficient into the system. Lastly, the predictions are combined using the SVR. The simulations were conducted using a real database from smart meters of a building in Taiwan. To evaluate the performance of the proposed method, four related approaches were implemented and compared: a single ARIMA, two linear combination systems, and one non-linear combination system. The results show a superiority of the proposed method in terms of the metrics Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).
... SHTs have been transformed from wired, expensive, and niche gadgetry to widely reachable connected smart appliances that turn consumers' home routines into more comfortable experiences. Smart home technologies [1] are often used synonymously with "home automation" [2,3], "home network" [4], "household technology" [5], "smart domestic products" [6], or "home intelligence" [7,8]. Various definitions have been used to conceptualize and define smart homes. ...
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Smart homes embrace advanced technologies and the connectedness of devices that aim to increase consumers’ life quality. They are based on data integration over shared platforms collected via sensors and wireless networks. However, although consumers’ current and potential adoption of smart homes have received some research interest, there is a low number of studies considering the foreseeable future of smart homes from the business perspective. To fulfill this gap in the literature, this study presents the results of an exploratory research attempting to reveal the foresight of the business side regarding the penetration of smart home technologies (SHTs) into consumers’ lives. Based on the opinions of industry experts collected through 13 semistructured in-depth interviews, numerous drivers of and barriers to SHT adoption are uncovered and displayed in their intertwined relationship in a thematic map. In creating this map, the qualitative data gathered through the interviews are integrated with widely used theories/models of technology adoption in the literature to develop a full-fledged set of determinants. As a result, drivers of SHT adoption (five sub-themes) and barriers that hinder smart home penetration (eight subthemes) were determined. Drivers consist of relative advantage, enjoyment, image enhancement, modern and multifunctional design, and consumers’ technology innovativeness. In contrast, the main barriers are high cost, complexity, lack of compatibility, lack of trialability, lack of observability, lack of a trusted brand in the market, lack of facilitating conditions and support services, and consumers’ technology anxiety. This rich set of SHT adoption determinants can be used in future studies to examine their relative impact on consumers’ adoption of SHT.
With smart homes’ development and market expansion, we must explore the smart home service experience with a long-term perspective with future thinking. First, this study reviewed the literature of smart home service experience, future thinking, and foresight. Then, data about smart home experience were collected from a Chinese social media, Weibo, and the weak signals of future smart home service experience were explored by conducting the social media analysis. The weak signals were analysed according to three dimensions of future triangle to predict the alternative futures of smart home service experience. Finally, four strategies were proposed for future smart home service experience from two aspects: functional and emotional experiences. First, system-connection experience must be improved, and incompatibility problems between different brands or platform products must be resolved through the means such as technology and communication protocols. Second, the device-usage experience must be enhanced, and the automation degree of smart home products must be improved using algorithms. Furthermore, enhanced privacy experience, user data security and social equality must be improved, protected, promoted, respectively. Finally, value-perception experience should be improved, the problems of energy saving and environmental protection for smart homes must be addressed, and user value-perception experience of ecological sustainability must be encouraged. In conclusion, we can use future thinking to develop smart homes service experience with a long-term perspective and enhance social equality and environmental sustainability.
The development of solar energy and the concept of net zero or positive energy buildings are being increased in a sort of symbiotic relationship. To make buildings more energy efficient, smart and optimal energy management systems called predictive building control were developed both for heat and electricity utilization. In these energy management systems (EMSs), a weather forecasting platform is often incorporated and allows to anticipate the meteorological events influencing the electrical and thermal energy consumption and to react accordingly. The objectives of this chapter consist in showing how the introduction of a solar radiation forecasting tool into the EMS improves its performances and saves energy and money. A brief overview of solar radiation forecasting methods is shown and focuses on solar prediction at short time horizon using statistical and artificial intelligences technics. These solar radiation prediction models are applied, validated, and compared on the Mediterranean site of Ajaccio, France. The most reliable forecasting tool, ARMA model, is then incorporated into the EMS which manages electricity into a microgrid composed of a photovoltaic/battery energy system and supplying a building and an electrical vehicle. The electricity cost benefit is then estimated and discussed. It appeared that the addition of PV forecasting based on an ARMA model into the EMS increases the gain of about 7% compared to a EMS without forecasting; this gain could reach 10% with a perfect forecasting reaches.KeywordsEnergy management systemSolar energySolar radiation forecasting methodsEnergy savingsOptimization
Healthcare is a sensitive domain for human beings and needs extra care to monitor patients and elderly people. Massive exploitation of associated devices is the primary source for collecting health data during the monitoring of patients. All connected devices such as sensors, actuators, and RFIDs produce challenging data simultaneously and sometimes lack consistency and provide incorrect information. Recently, semantic web technologies have been involved in overcoming these issues and providing semantic and syntactic representation of data to interpret by machines and humans. Ontologies are presented as a backbone of the semantic web and presented as semantic models with explicit representation of health data and connected devices. The primary aim of semantic modeling of healthcare data is to facilitate its domain's reusability and generalized representation. In this chapter, a brief introduction of healthcare semantic models has been discussed in general-purpose and domain-specific IoT-based semantic models. It also covered semantic modeling phases such as semantic annotation, semantic linking, construction process, and semantic representation. Various case studies and examples present all phases.
Conference Paper
Absbnct-This paper presents a mobile-agent and OSGl based three-tier control system architecture for smart home. In the OSGi platform of Home Gateway, UPnP technology and intelligent agent technology works together to achieve automation of devices discovery, registry, and management* By means o f OS(3 middleware and mobile agent technology, the proposed hierarchical architecture supports remote devim control and fault diagnosis, dynamic service provisioning, flexible system performance management, integration of heterogeneous-devices, and agent-based distributed control. Inah Te-Home Gateway, mobile agent, OSGi (Open Service Gateway initiative) UPnPfUniversal Plug and Play)
Significant progress has been made in the area of common data exchange in the building industry with the development of information technology. Currently, the Industry Foundation Class (IFC) and Green Building XML (gbXML) are two prevalent informational infrastructures in the architecture, engineering and construction (AEC) industry. IFC and gbXML are both used for common data exchange between AEC applications such as CAD and building simulation tools. This paper presents a detailed investigation and comparative study of the differences between IFC and gbXML in terms of their data representations, data structures and applications. It aims to explicitly illustrate the complex data representation through selected examples of the respective schema. Two specific demonstrative cases will include building element specification (i.e.,enclosure geometry) and building sensors (control and operation). Findings will be reported on the following aspects: (1) the strength and challenges of the diametrically opposing approaches between IFC and gbXML; (2) hierarchical structure of the schema in support of extensibility, data extraction, ease of implementation etc.; (3) formal adoption and application. Based on the results of this study, the gbXML schema is selected for development to demonstrate the features of gbXML. A proposed XML schema for lighting simulation will be presented. It aims to provide a seamless data integration platform between a CAD model (i.e., REVIT) and lighting simulation software (i.e., Radiance) in this study to support concurrent design of high performance buildings.
The approach to developing models described within the following chapters breaks with some of the previously used approaches in Artificial Intelligence. This is the first attempt to use methods from psychoanalysis organized in a strictly topdown design method in order to take an important step towards the creation of intelligent systems. Hence, the vision and the research hypothesis are described in the beginning and will hopefully prove to have sufficient grounds for this approach.
Build your own intelligent agent system. Intelligent agent technology is a tool of modern computer science that can be used to engineer complex computer programmes that behave rationally in dynamic and changing environments. Applications range from small programmes that intelligently search the Web buying and selling goods via electronic commerce, to autonomous space probes. This powerful technology is not widely used, however, as developing intelligent agent software requires high levels of training and skill. The authors of this book have developed and tested a methodology and tools for developing intelligent agent systems. With this methodology (Prometheus) developers can start agent-oriented designs and implementations easily from scratch saving valuable time and resources. Developing Intelligent Agent Systems not only answers the questions 'what are agents?' and 'why are they useful?' but also the crucial question: 'how do I design and build intelligent agent systems?' The book covers everything a practitioner needs to know to begin to effectively use this technology - including an introduction to the notion of agents, a description of the concepts involved, and a software engineering methodology.