{ HAUPTBEITRAG / SESAME-S

SESAME-S: Semantic Smart HomeSystem for Energy Efficiency

Anna Fensel · Slobodanka TomicVikash Kumar · Milan Stefanovic

Sergey V. Aleshin · Dmitry O. Novikov

IntroductionRising energy costs have created an increased needfor energy-efficient systems, and an increased de-mand for energy-saving solutions around the world.To respond to these rapidly growing markets of en-ergy efficiency, our work focuses on the design ofhighly personalized services based on a sensor andsmart meter-enabled data-intensive smart homesystem and building automation.

Energy efficiency remains a topic of growingimportance. According to the Analyst Briefing Pre-sentation on the Global Smart Homes Market heldon 27th May 2010, the Global Smart Homes market“is estimated to be $ 13.4 billion by 2014, growing ata CAGR of 16.5 % from 2009 to 2014. The smart homesmarket is segmented into products and services mar-kets which are expected to grow at a CAGR of 16.3 %and 17 % respectively”1. Achieving a 20 % reductionin primary energy use by 2020 through improvedenergy efficiency is one of the key measures of the 20-20-20 targets to keep CO2 emissions under control,and includes the well-known introduction of smartmeters on a European-wide basis, to be implementedwithin the next few years [4]. A recently set and evenmore ambitious EU goal is to cut greenhouse gasemissions by 80–95 % by 2050 [3].

Success in applied services-driven research andindustrial settings largely depends on the ability toidentify promising directions and technologies andto invest in those that will eventually lead to eco-nomically viable services or products. In this work,we focus on designing and evaluating end-consumer

1 Markets and Markets: http://www.marketsandmarkets.com/AnalystBriefing/smart-homes-market.asp.

energy-efficient services that are grounded on andperform fine-granular processing of semantic linkeddata, unleashing the current large commercial-ization potential of semantic data. Specifically, weanalyze the end-consumer acceptance of a semanticsmart home system enabling energy efficiency.

Semantic data stem from the Semantic Web [9],which represents the next-generation World WideWeb, where information is published and inter-linked in order to facilitate the exploitation of itsstructure and semantics (meaning) for both hu-mans and machines. To foster the realization of theSemantic Web, the World Wide Web Consortiumdeveloped a unified metadata model (RDF), ontol-ogy languages (RDF Schema and OWL variants),and query languages (e. g., SPARQL). Research inthe past several years has been primarily concernedwith the definition and implementation of these lan-guages, the development of accompanying ontologytechnologies, and applications in various domains,as well as currently, on publishing, linking and con-

DOI 10.1007/s00287-012-0665-9© Springer-Verlag Berlin Heidelberg 2012

Anna Fensel · Slobodanka Tomic · Vikash KumarThe Telecommunications Research Center Vienna (FTW),Vienna, AustriaE-Mail: {fensel, tomic, kumar}@ftw.at

Anna FenselSemantic Technology Institute (STI) Innsbruck,University of Innsbruck,Innsbruck, Austria

Milan StefanovicE-Smart Systems d.o.o.,Belgrade, SerbiaE-Mail: milan.stefanovic@e-smartsys.com

Sergey V. Aleshin · Dmitry O. NovikovExperimental Factory of Scientific Engineering,Chernogolovka, RussiaE-Mail: {saleshin, novikovd}@ezan.ac.ru

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AbstractAs the urgent need for efficient and sustain-able energy usage becomes ever more apparent,interest in Smart Homes is on the rise. TheSESAME-S project (SEmantic SmArt Meter-ing – Services for Energy Efficient Houses)uses semantically linked data to actively assistend-consumers in making well-informed deci-sions and controlling their energy consumption.By integrating smart metering and home au-tomation functionality, SESAME-S works toeffectively address the potential mass market ofend consumers with an easily customizable solu-tion that can be widely implemented in domesticor business environments, with an expected sav-ings of over 20 % from the total energy bill. Thedeveloped system is a basis for conceptualizing,demonstrating and evaluating a variety of in-novative end consumer services and their userinterface paradigms. In this paper, we presentthe SESAME-S system as a whole and discuss thesemantically-enabled services, demonstratingthat such systems may have broad acceptance inthe future. The data obtained through such sys-tems will be invaluable for future global energyefficiency strategies and businesses.

suming Linked Open Data2. This work has been verysuccessful, and semantic web technologies are beingincreasingly adopted by mainstream corporationsand governments (for example by the UK and USAgovernments) and in several fields of science (forexample, life sciences or astronomy).

Also major search engine providers such asGoogle have recognized the benefits of using se-mantic data [15]. Recently, they have launched newservices that leverage semantic data on the Web toimprove the end-user search experience. There arealso ongoing research efforts and projects on howthese technologies can be beneficial for the field ofenergy and smart homes, for example publishingthe energy companies-related data as linked openenergy data3.

The state of the art in energy-efficiency sys-tems indicates a severe need for intelligent or

2 Linked Open Data (LOD) Cloud: http://linkeddata.org.3 Open Energy Information Initiative (OpenEI): http://en.openei.org.

semantic data processing in energy management.Some of the requirements are: “to enable rapid re-sponse to changes in regulation and competition”or “to provide tools that will expedite the flowof business information to the critical decision-making processes and support enterprise valueoptimization” [6].

Our SESAME smart home system softwareand the services based on it, leverages semantictechnology allowing the system to be person-alized, intuitive, with very short response time,interoperable with different devices and easy toextend, maintain, and upgrade. Furthermore, inSESAME-S [8], we go a step further, installing thissystem in real buildings and testing it with activeusers.

The paper is structured as follows. In Sect. “Re-lated Work and Motivation”, we describe the worksrelated to our paper, as well as our motivation andapproach. In Sect. “Architecture Design and Imple-mentation”, we discuss our energy-managementhardware and software design, including the useof semantic technologies. We also show the end-consumer services implemented by us in thissection. In Sect. “Evaluation and Results” we presentthe evaluation and results. Finally, Sect. “Conclu-sions and Outlook” concludes and summarizes ourpaper.

Related Work and MotivationIn this section, we discuss other relevant approaches,products, and services which are similar in characterto the system we have developed.

Regarding innovative hardware settings, cur-rently Apple is pioneering smart plugs [1] followedby other innovative startups4, and Cisco is manufac-turing a Home Energy Controller5 able to connectand control a large variety of heterogeneous de-vices. Cisco is applying these tools in pilot projectsunder the Connected Urban Development initiative6,particularly in Madrid. Research-oriented devel-opments have also demonstrated the efficiency ofspecific targeted nonsemantic solutions, for ex-ample, home controlling via operation of a ZigBeecommunication interface [13].

4 AlertMe: http://www.alertme.com/products/energy/how-it-works.5 Cisco’s Home Energy Controller: http://www.cisco.com/web/consumer/products/hem.html.6 Connected Urban Development: http://www.connectedurbandevelopment.org.

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Regarding software development, start-up com-panies developing mobile services for buildingautomation already exist in the US and Ger-many [7]. Google’s PowerMeter7 also providesenergy-efficiency services for end-consumers, basedon the technical infrastructure of certain providersand manufacturers with whom they have partner-ships. However, Google’s Power Meter was retired insummer 2011 due to the fact that the “efforts have notscaled as quickly as we would have liked”, the lattercaused presumably by the high entry barrier to theplatform restricted by B2B agreements. Pachube8 isa platform for supporting data streams in general,and it is currently comprised of certain individualdata streams relevant to energy usage in particular.However, it provides neither smart home hardwarenor energy-efficiency services. Yet the sensor datacollected with the SESAME user interfaces can bestreamed in Pachube or similar semi-structured orsemantic environments.

Among approaches of research origin, similarsemantically enabled demotic systems have beenconstructed [10], but user interface and accept-ance aspects have yet to be investigated there. Alsopurely Web-based services and interfaces for energyawareness have been designed, however, withoutconnections to sensors and smart homes [17]. Assoon as semantically enabled platforms for mashingup sensor data [11] become readily available, our sys-tem and interfaces would facilitate the provisioningof smart home and energy data.

The technical advantages or differences betweenour services and those aforementioned are primarilydue to three key factors:

1. Delivering a flexible, easily extendable solutionaddressing a mass market of end-consumers witha system that comprises sensor, smart meteringand controlling support, as well as services.

2. Relying on efficient operation and commer-cialization on the basis of semantic linked dataenabling automated management and forecastingcapabilities.

3. The end-user services would be more person-alized, context aware and more attractive tothe end-user than the potential competitors.

7 Google’s PowerMeter: http://www.google.com/powermeter.8 Real-Time Open Data Web Service for the Internet of Things – Pachube, 2011,https://pachube.com. It was sold to LogMeIn in summer 2011 for ca. 15 millionUSD, URL: https://investor.logmein.com/releasedetail.cfm?releaseid=592763.

A system like Cisco’s does not allow residentsto control all the appliances in their house. Bycontrast, this consolidated control feature hasbeen implemented in the SESAME system. User’sprimary interest in saving energy is very muchin accordance with earlier studies conductedin different settings – in the form of a game onthe Facebook social network [14]. However, thestudies with the demonstrator were more tangibleand also made users contemplate the complexityand cost of the system.

As mentioned in the previous section, the marketsaddressed by energy-efficient end-consumer ser-vices are growing rapidly. The produced serviceswould eventually be addressed to heterogeneousB2B and B2C user groups. For the energy-efficientsmart home services, the primary target group is:

1. Individual private apartments, public build-ings and factories, and construction companies,because of the introduction of the house energy-consumption awareness system, enacting remotedevice controlling and end-user mobile services.

2. Electrical appliance and device manufacturersand resellers, because of the possibility of expos-ing targeted advertisements based on vital userstatistics.

3. Energy distribution companies, because of thecollection of real-time energy-consumption datain the house, which can be used for forecastingpeaks, etc.

Architecture Design and ImplementationThe hardware-oriented architecture of the systemis shown in Fig. 1. The demonstrator integrates

Fig. 1 SESAME system architecture

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Fig. 2 SESAME portabledemonstrator

a variety of components, such as real smart meters,different types of sensors and actuators, as wellas a simulator that can flexibly integrate virtualappliances (such as the washing machine) and fa-cilitate the study of real-life situations. Its portableversion is shown in Fig. 2. The system also inte-grates a simulator of external utility Web services(tariffs) and relevant Internet services such as theweather forecaster. This particular architecturewas chosen due to its use of low-cost components,scalability, and ability to integrate with existing com-ponents such as smart meters deployed by energyproviders.

The prototype demonstrator is a mobile metalcase containing the hardware (smart meters, LEDs,fans, sockets, etc.). The state of the electrical devices(physical and virtual) is shown in the front panel,which illustrates a real home setup. The demon-strator allows for the debugging of hardware andsoftware as well as all the main SESAME system al-gorithms. Using this software and hardware modelgives us the opportunity to simulate the real smarthome systems in order to evaluate the effective-ness of technology for smart homes. It also enablesthe possibility of further innovations in the imple-mentation of modern control algorithms for smartenergy-efficiency systems.

Hardware LayerHardware monitors environment sensor data (tem-perature, humidity, light, energy consumptions etc.)and is also capable of controlling in-house sys-tems (heating, cooling, water supply, computers).

Our test installation currently controls comput-ers through a Power Management Service (PMS).Installed components include a PMS to control com-puters, light, temperature and humidity sensors,plugwise smart plugs9 for monitoring and con-trolling plugged devices, an android-based tabletfor monitoring the services and several androidmobile apps for monitoring as well as controllingdevices.

A simple scalable controller (TeleCont) wasused to increase the number of compatible third-party devices in the SESAME system, and to includethe low-cost equipment without a specific digitalinterface.

It has various types of input signals (analog,digital, relay output) for data acquisition. TeleContprocesses obtained data and transmits it via Ethernetto the router (Gibraltar). It provides a secured tunnelto the end-user. Devices with an Ethernet port candirectly connect to the router.

The demonstrator is being used for initialevaluation of hardware and software solutionsand for SESAME’s marketing purposes. Real-lifesituations are being simulated by semanticallyenabled software.

Inside the building the controller collects thedata from sensors for a specific flat in real applica-tions and transmits them via Ethernet to the centralcontrol point for data processing. The controller alsohas a scalable set of add-on modules that can be usedto connect different actuators and sensors (tempera-

9 www.plugwise.com.

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ture, humidity, light). It enables remote control ofgeneral house process equipment and provides thefastest response in case of emergency (flooding, fire,etc.).

Software LayerSemantically enabled software makes automateddecisions based on data coming from the physicaland virtual devices, system users and utilities. Usingsemantic models and communication technology,it links the hardware layer with the end-user inter-faces of services. For the purpose of managing andusing benefits of the smart home, the developedsoftware is easy to use, intuitive, with a very shortresponse time, interoperable with different devicesand easy to maintain and upgrade. The OWLIM se-mantic repository10 is being used for storing live dataavailable from a school building in the form of RDFtriples. Various applications built for the SESAMEsystem use data queried from this triple store to pro-vide several services discussed later. The SESAMEsystem software, in particular, is responsible for:

– downloading tariff profiles from utility publiclocations on the Web;

– communication with sensors and actuators in thehouse;

– smart meter data acquisition through the dataconcentrator;

– managing and administrating the whole systemand

– reasoning on the data received.

In the following sections, we explain the underlyingontologies, rules and their execution, as well as thethree types of user interface services evaluated.

SESAME OntologiesSESAME uses an ontology-based modeling approachto describe an energy-aware home and the rela-tionships between the objects and actors withinits control scenario. The main components of theSESAME ontology expressed in OWL are Automa-tion Ontology, Meter Data Ontology, and PricingOntology [16].

SESAME Automation Ontology includes a num-ber of general concepts such as Resident andLocation, and concepts in the automation and in the

10 http://www.ontotext.com/owlim.

energy domain, such as Device, and Configuration.To model different types of control functionality,the SESAME ontology introduces the Configura-tion class, which has two subclasses: Activity (orautomation activity) and EnergyPolicy. An Activ-ity connects Appliance, Sensor and UI Device intoa joint task. A ContextBased Activity can provideregulation of different types, for example regulationon time, occupancy of location, threshold value.For this purpose it includes properties includingthresholds and scheduled times.

SESAME Meter Data Ontology is based on theDLMS standard model [2] for meter data model-ing. The DLMS/COSEM specification defines a datamodel and communication protocols for data ex-change with the metering equipment. With the setof interface classes (e. g., Register, Activity Calendar,Clock) and a set of instances of these classes, themeter acts as a server and publishes its data to theutilities, customers, or providers which can accessthe meter data as clients. A published measuredobject has a unique OBIS code that consists of sixnumbers. OBIS naming is used in logical name (LN)referencing.

In this ontology, stored data is divided into threemajor types. Every type of data can give the userdifferent information. For example, “Register” keepsall data about active/reactive (+/-) current averagepower, active/reactive (+/-) energy, voltage, current,THD, cosφ, active/reactive (+/-) maximum powerfor a defined period; “Configuration Data” keepsinformation about status of the meter, status of everymeasured data, last calibration date; “Clock” keepsinformation about time and time parameters on themeter.

SESAME Pricing Ontology captures the conceptfor making energy-aware decisions and select-ing the optimal tariff model for a specified timeand energy load. This ontology is based on thefollowing classes: SelectonCriteriaPonders is a rea-soning configuration class that has only one instanceand its properties represent ponders (signifi-cance of a criteria in choosing the optimal tariffmodel); Provider and EnergyType classes repre-sent providers and energy types which are availablechoices for decision making; TariffModel representtariff models to which the customer is subscribed;DeviceWorkingTime for device start time.

The pricing Ontology is used for creating theschedule for turning on and off devices that are

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high-energy consumers through use of data fromTariff plans and Resident preferences. The main classin this ontology is DecisionSet which is preparedfor ontology from external processes. It containsall possible combinations of tariff providers, tar-iffs and prices per each 15-minute time interval, onwhich the ontology will, using preferences as pon-ders, make a decision. The decision contains dataabout which tariff plan at that time is the most ap-propriate for use by big consumers (appliances) inthe house.

SESAME Rules and PoliciesPolicy-based decision-making mechanisms inSESAME are a part of the policy infrastructure. Poli-cies complement the knowledge base realized withthe described ontologies and capture the configura-tion of the system and preferences of users in respectof system behavior. They are input for reasoning,specify more complex business logic and activationand orchestration of corresponding services. TheSESAME project has designed three paradigms ofuser interfaces for energy end-consumer services:

– Touch screen interfaces for settings control, activityscheduling (HAN) (Fig. 3);

– User profile-based policy recommendation andcreation (EPR) (Fig. 4);

Fig. 3 SESAME touch-screeninterface, HAN

– Interface for the acquisition of arbitrary policiesemploying the ontology concepts (PAT) [18](Fig. 5).

The functioning of the SESAME system, specificallyin terms of energy saving, critically depends on thequality of the installed policies or rules. However,creation of more complex rules may overwhelm anordinary user. Therefore, in the SESAME system,the creation of policies is designed as a two-stageprocess: (1) specific system-level policies are auto-matically created based on the current ontology andthe knowledge base, keeping the system flexible andopen for changes; (2) through a user-friendly graphi-cal interface the end-customer specifies user-createdpolicies (preferences) regarding the energy-awareenvironmental control by integrating system-levelpolicies. By being offered to just select from the setof recommended rules, the user is guarded fromunintentional errors or wrong decisions.

System-level policies are classified into theenergy-management rules and automation rules.Energy-management rules are executed after au-tomation rules to verify the automation decisionbased on energy constraints. For example, after theautomation rule sets the status of the Appliance to“isToBeSwitchedOn,” the energy-management rule,which acts on the Tariff information, can set up the

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Fig. 4 SESAME interface EPR

Fig. 5 Policy Acquisition Tool, PAT

activation parameter to “switch on.” System-levelrules are created by “power users” well acquaintedwith the model of the devices and activities. Systemvendors can also create such rules in which case theyautomatically come when the devices are installed inthe environment.

Power-user interactions for creation of prefer-ences and policies are supported through a graphicaluser interface towards a rule engine, a reasoning toolwhich can assist in defining coherent system-levelautomation and energy policies (such as the PolicyAcquisition Tool (PAT) in Fig. 5).

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The end-user interface for a “normal user”is a “user-friendly cockpit” through which theuser is granted control over the system, and isinformed about his/her context. To control theSESAME system, the user designs policies by se-lecting, combining and inserting system-levelpolicies into the system. In the “cockpit” the en-ergy usage is visualized based on a pre-selectionof measurable values available through real-timereadings coming from various devices. Overlays ofenergy-consumption graphs from different weeksor days are a common way to show the user howhis/her semantic policies changed his/her overallconsumption and cost. Comparing consumptionpatterns with friends over the Internet and shar-ing of semantic policies may establish itself asa means to optimize energy usage by leverag-ing other peoples’ experience. This visualizationmay also illustrate CO2 footprint and monetaryimpact.

Ontology reasoning is based on SWRL andSQWRL rules that are applied using the Jess RuleEngine [5]. Reasoning in this ontology consists ofa set of rules that oversees the schedule of activ-ities, presence/absence in the room and turningon/turning off devices, depending on the desiredstate of environment.

To appeal individually to each user of theSESAME system, a more personalized approachto policy creation was envisaged. This user profileparadigm for policy construction takes into accountthe fact that a user might not always know what ex-actly he/she wants from the system or how best tooptimize his/her resources using system policies.These observations formed the basis for creation ofa web-based policy construction/editing tool, theEnergy Policy Recommender (EPR) which wouldtake into account resident behavior in coming upwith policy suggestions suited best to his/her habits.User information is gathered through a question-naire inspired from the experimental feedback ofa study conducted on two real home users [12].The user is then presented with a set of policiesthat we predefine, keeping in mind the capabilitiesof the SESAME system and instantiated accord-ing to his/her individual profile (Fig. 4). Mostof these policies also represent potential savingsin terms of cost in Euro per day which can beachieved through the application of the respectivepolicies.

Primarily, the three user interfaces discussedwere oriented towards the acquisition of houseautomation rules from the end-users. We con-ducted user studies displaying a high level ofacceptance and the high expectations that usershave from such energy-efficient interfaces forsmart home systems and services. All three in-terfaces were tested by users and found to havean acceptable level of accessibility, with the EPRranking highest for clarity, intuitiveness, and easeof use.

Evaluation and ResultsIn this section, we present the evaluation of the pro-totype, namely, the goals of the user study, and its setup and procedure, as well as the outcomes. The goalsof the user study were as follows:

– To investigate the users’ attitudes towards employ-ing energy-efficiency services for smart homes, andapplication and commercialization preferences forsuch services;

– To estimate the service approach and practicespreferred by the potential users.

We presented the demonstrator to the research andindustry public in demo booths at the I-Semantics’10(September 1–3, 2010, Graz, Austria) and ESTC’10(December 1–2, 2010, Vienna, Austria) confer-ences. A feedback form on the system was designedand distributed to the booth’s visitors followingthe demonstration, and we received 28 completedquestionnaires in response.

The user trials and questionnaire feedback andtheir outcomes are summarized in the followingsubsections of this section. The questions weremultiple-choice in nature where respondents wereallowed to choose more than one of the given optionsas their answer.

Question 1: Which functionalities of the systemdo you find particularly useful?

The most popular functionality of the system(22 votes) seemed to be the ones that allowed themto plan and control their energy consumption inthe most efficient way and compare various tariffsavailable. Fifteen respondents also liked the functionof setting the desired state of devices in the houseand that of the system automatically responding tochanges in the environmental conditions of variouslocations in the house.

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Fig. 6 Acceptance of thesystem

Fig. 7 Minimum expected savings from the system

Question 2: Which of the following decisionswould you delegate to the system?

The most popular functionality in this case(23 votes) was the one allowing the users to manage

various stand-by devices (e. g., switching them off)in the house. Around 17 respondents also showedtheir interest in delegating certain security tasksto the system like getting an alert when some-

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body enters their home, etc. Other decisions thatmost users would allow to be delegated were ap-pliance scheduling (14 votes) and air-conditioning(17 votes).

Question 3: Do you think you would use this ora similar system regularly in the future? If yes, howmuch money (in %) would such a system need to saveyou so that you get it installed and run it in yourhouse? (Figs. 6 and 7)

Fig. 8 Expected investment on the system by private households

Around 21 respondents rated their willingness touse such a system between 1 and 3 (1 = yes, 5 = no),while five others were not sure about it. Only fourrespondents showed a relatively lower confidencelevel of 4/5 in their willingness to use such a system,showing that the users generally liked the system andthe concept behind it.

To the amount of savings they would expectfrom such a system, we received a largely mixed

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Fig. 9 Approval of system being used in larger facilities

response with eight respondents expecting up to20 % savings, and five each expecting 10 % and15 % respectively. Another four expected up to 30 %savings from such a system. This indicates thatalthough users do expect the system to save en-ergy/cost for them, their expectations vary betweenindividuals.

Question 4: If you would have to purchase a se-mantic smart metering system yourself, how muchwould you spend on such a system? (Fig. 8)

Just as in the previous question, we concludedthat the preference of the amount of money to bepaid by the respondents for such a system and thechoice of the mode of payment (monthly/yearly)varies over a broad range.

Question 5: Can you imagine using such a sys-tem in the future for managing larger facilities (e. g.,offices, schools, factories etc.)? (Fig. 9)

23 of the respondents were very confident ofthe prospects of such a system being used in largerfacilities with 15 individuals, 8 of whom gave a confi-dence rating of 1 and 2 (1 = yes, 5 = No) respectivelyas their answer. Only three of them answered witha lower confidence rating (of 4/5) while two othersdid not provide any rating score (“other” option).We can thus successfully infer that in general peopledo foresee automated systems like SESAME beingused to control large buildings and installations inthe future.

Conclusions and OutlookSemantically enabled technology offering energyoptimization for efficient home and business appli-cations is a rapidly emerging market in Europe. This

paper presented the system SESAME, a semantics-enabled platform for energy-efficiency applications,discussing in detail ontologies, rules as well asthe end-consumer services. We used communica-tions, services and semantic technology to createa flexible system with automatic reasoning anda variety of innovative user interfaces that canstimulate and facilitate users in their more respon-sible use of energy. We conducted user studiesdisplaying a high level of acceptance and the highexpectations that users have from such energy-efficient smart home systems and services. Hence,we see a high potential for such technology asa basis for energy-efficient strategies of the fu-ture, especially if services are user friendly andeasy to operate. Currently, the SESAME-S systemis being installed in two real-life pilot buildings,where we refine the technology to prove its oper-ational feasibility and market expectations. Thesystem has been adapted to real building settingsand installed in two real buildings: at a schoolin Kirchdorf, Austria and on a factory floor atChernogolovka, Russia, with the user trials ongoingin April–June 2012.

The collected and semantically representeddata has a large potential for being combined, ex-tended and reused for numerous scenarios andparties, such as grid operators seeking balanceon their smart grids, utilities, interested in opti-mal energy trading prices – based on real, and notsynthetic user profiles, and municipalities, inter-ested in more information about their citizens.Mechanisms to adequately access and commer-cialize such data within services (in particular,complying with the rigid security and privacy re-quirements typical for smart home use cases) arecertainly among the next most relevant researchquestions.

AcknowledgmentsThis work is supported by the FFG COIN fundingline, within the SESAME and SESAME-S projects(http://sesame-s.ftw.at). FTW is supported bythe Austrian government and the City of Viennawithin the competence center program COMET.The authors thank the SESAME-S project team forvaluable contributions: apart from the paper co-authors’ organizations, the consortium membersare eSYS Informationssysteme GmbH, Upper Aus-trian University of Applied Sciences, and Semantic

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Web Company GmbH (Austria). We also thank AmyStrub for editing and proofreading the English inthis paper.

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