end-user interfaces for energy-efficient semantically enabled smart homes

End User Interfaces for Energy Efficient Semantically-Enabled Smart Homes

Anna Fensel 1, 2, *, Vikash Kumar 1, 3, Slobodanka Tomic 1

1 FTW Forschungszentrum Telekommunikation Wien GmbH, Donau-City-Straße 1/3. Stock, A-1220 Vienna, Austria

2 Semantic Technology Institute (STI) Innsbruck, University of Innsbruck, Technikerstraße 21a, A-6020 Innsbruck, Austria

3 Inland Revenue, Auckland, New Zealand

* Corresponding author. Tel.: +43-512- 507-96884.

[email protected], [email protected], [email protected]

Abstract: The need for energy efficient technological solutions is becoming ever more prevalent in today’s world. However, current advances are failing to offer end consumers with a flexible solution that can be widely implemented in domestic or business environments. This is particularly relevant at the user interface level where energy consumers should be allowed to easily engage in effective energy saving technology. With the help of semantically linked data, we aim to actively assist end-consumers in making well-informed decisions in order to successfully control their energy consumption. By integrating smart metering and home automation functionality, our SESAME system offers end-consumers energy efficient and cost cutting options for their homes or businesses. The developed SESAME system conceptualizes, demonstrates and evaluates a variety of innovative end consumer services, here focusing specifically on their user interface paradigms. In this paper, we present three types of interactive participatory user interfaces, all of which enable users to interact with the house automation settings modelled as semantic rules, as well their evaluation in user studies based on the demonstrator system. We show that the proposed interfaces have the potential for broad acceptance, and provide a detailed analysis of the effectiveness of their varying design principles and features.

Keywords: Smart Home, Semantics, Energy Efficiency, User Interfaces.

Reference: Fensel, A., Kumar, V., Tomic, S.D.K. "End User Interfaces for Energy Efficient Semantically-Enabled Smart Homes". Energy Efficiency, Vol. 7, Iss. 4, pp. 655–675, Springer, 2014. ISSN: 1570-646X.

1 Introduction Rising energy costs and ecological awareness have created an increased need for energy efficient systems, and an increased demand for energy-saving solutions around the world. Energy efficiency remains a topic of growing importance. Here, the residential sectors merit specific attention, as more people have been moving into cities. Achieving a 20% reduction in primary energy use by 2020 through improved energy 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 smart meters on a European-wide basis, to be implemented within the next few years [EU2020]. By 2050, the EU’s goal is to cut greenhouse gas emissions by 80–95 % and ensure that two thirds of the energy in the EU comes from renewable sources [EU2050]. To respond to the rapidly growing demand of achieving energy efficiency, our work comprises the design of end-user addressed services based on a sensor and smart meter-enabled data intensive smart house system, building automation and smart houses.

According to estimations by the IBM-driven Service Science initiative [IBMSe], global markets are increasingly service-based economies; in developed countries, up to 80% of the economy is service-based [Spohrer and Maglio, 2008]. Thus, the employment growth will be further concentrated in the service-providing sectors of the global economy. A considerable amount of service innovation is needed to maintain profits, especially in new expanding areas such as Internet of the Things, Smart Homes and Energy Efficiency, all of which we address in this work.

Success in applied services-driven research and industrial settings largely depends on the ability to identify promising directions and technologies, the investment in those that will eventually lead to economically viable services or products. In this work, we present a design and evaluation of end consumer energy efficiency services that are based upon and perform fine-granular processing of semantic linked data, unleashing the current large commercialization potential of semantic data. Through user tests and questionnaires, we perform early evaluations of the service approaches, thus getting an indication of those that prove to be the most successful.

On the technology side, semantic technologies stem from the Semantic Web [Berners-Lee, 2001], which represents the next generation World Wide Web, where information is published and interlinked in order to facilitate the exploitation of its structure and semantics (meaning) for both humans and machines. To foster the realization of the Semantic Web, the World Wide Web Consortium (W3C) developed a set of metadata (RDF), ontology languages (RDF Schema and OWL variants), and query languages (e.g., SPARQL). Research in recent years has been primarily concerned with the definition and implementation of these languages, the development of accompanying ontology technologies, and applications in various domains, as well as currently, on publishing, linking and consuming Linked Open Data [LOD]. This research has been very successful, and semantic web technologies are increasingly being adopted by mainstream corporations and governments (for example by the UK and USA governments) and in several fields of science (for example, life sciences or astronomy).

Also, major search engine providers such as Google and Yahoo have recognized the benefits of using semantic data. Recently, they have launched new services that leverage semantic data on the Web to improve the end user search experience. There are also ongoing research efforts and projects on how these technologies can be beneficial for the field of energy and smart homes, e.g. publishing the energy companies related data as linked open data [OpenEI].

The latest advances in technology particularly indicate an urgent need for intelligent or semantic data processing in energy management, where multiple stakeholders are involved and need to share the data. Some of the requirements are: “to enable rapid response to changes in regulation and competition”, or “to provide tools that will expedite the flow of business information to the critical decision-making processes and support enterprise value optimization” [Hall et al., 2005]. However, the vast amounts of end consumer semantic data on energy consumption and habits will not become available without attractive end-consumer services and interfaces, whose concepts we design and evaluate in this research, employing the developed experimental smart metering system referred as “SESAME”. In this paper, we particularly focus on the presentation and comparison of the three types of possible user interfaces for such a system, following the tablet touch-screen, rule modelling and questionnaire-based paradigms.

In this paper, we describe the SESAME system as a whole, and particularly, the three types of developed semantically-enabled user interfaces and their user acceptance. The paper is structured as follows. The problem statement and research questions are described in the next section. The system architecture, its hardware, software and semantic layers are outlined in Section 3. The implemented end consumer services


and their user interfaces are described in Section 4. The evaluation and results are provided in Section 5. Related work is described in Section 6, and Section 7 concludes the paper.

2 Problem Statement and Research Question

Owing to the existence of a wide number of standards in the field of smart homes, our goal was to make the system compatible with most of the standard appliances and systems available on the market. This should be achieved without compromising on the level of intelligent interface expected from such a system. Other considerations include assuring the ability to reuse the vast amount of data generated from a host of such systems individually, as well as in conjunction with other third party data available. We therefore decided to use a semantic layer for addressing these requirements. Whilst the semantic encapsulation of appliance data helps in offsetting the huge number of industry standards, it also provides data structured in a format that can be easily reused and combined with any semantically structured data on the web. This maximizes the effectiveness of services provided by integrating third party information such as weather forecasts, power outage information, etc.

The semantic layer is particularly effective for modelling and sharing building automation settings and rules [Kumar et al., 2012]. These can be easily and flexibly accessed and modified by the end consumers, employing suitable end-user interfaces. Among more specific requirements for user interfaces, we wanted to experiment with the paradigm of a policy-based control of appliances by the respective users. This paradigm has been combined with already existing typical and popular smart home functions such as providing current status in terms of temperature, humidity, ON/OFF state of appliances, etc.

Taking into account the difference in ages, ICT exposure and other behavioural patterns of targeted users, we aimed to create user interfaces catering to the different user groups. For addressing this requirement, we came up with several user interfaces and tested with users at varying levels of IT knowledge. We addressed different modes of the user interfaces for remote monitoring and control of the smart home via internet, such as mobile/tablet based interfaces, and the Web-based interfaces for a PC that allow more fine-grained and detailed modelling of smart home automation settings and policies. The main contributions of the work is thus the designs of such interfaces for the semantic policy based energy saving smart home automation system, and their detailed evaluations with real users, suggesting appropriate usage contexts for these interfaces, as well as indications of further improvement potential.

3 Smart Metering System SESAME The primary target groups for energy efficient end consumer smart house services include individual private apartments, public buildings, factories, and construction companies. This is due to the introduction of the house energy consumption awareness system, enacting remote device control and end-user mobile services.

Once this target group has been successfully addressed, targeting additional market groups will further contribute to an increase in revenue for electrical appliance and device manufacturers and resellers. By identifying the devices and daily habits of users, targeted advertisements can be created. This would also benefit energy distribution companies with their subsequent collection of real-time energy consumption and devices data in the house e.g. to address the problem of handling energy peaks.

The project SESAME [SESAME] has used semantic technology to create a technical solution that integrates smart metering and demand management, building automation and policy-based reasoning and offers an energy-optimization capability for the energy consumer and provider. The information used comes from sensors with multiple domains, including the physical sensors (e.g., temperature, light, presence) and information sensors from the energy management domain services, as well as from user profiles and policies [Tomic et al., 2011].

The proposed concepts are implemented and validated in an extensible portable smart home demonstrator system (Figure 1), which provides a proof-of-concept for the innovative technical solution. In our follow-up work (SESAME-S project [SESAME-S]), we rely on the developed demonstrator and its technical feasibility and use it in real buildings. In the following subsections we briefly describe hardware, software and semantic layers comprising the base of the system.


3.1 SESAME Hardware

The hardware-oriented architecture of the system is shown in Figure 2, which clearly depicts the exact hardware components and protocols that were used. The demonstrator integrates a variety of components, such as different types of sensors and actuators, as well as a simulator that can flexibly integrate virtual appliances (such as the washing machine) and facilitate the study of real-life situations. The system also integrates a simulator of external utility Web services (tariffs) and relevant Internet services such as the weather forecaster. This particular architecture was chosen due to its use of low-cost components, scalability, and ability to integrate with existing components such as smart meters deployed by energy providers.

Figure 1. SESAME Portable Demonstrator

The demonstrator is a mobile case containing the hardware: smart meter, LEDs, fans, sockets, movement sensors, etc. as depicted on the left side of Figure 2. The state of the electrical devices (physical and virtual) is shown in the front panel, which illustrates a real home setup. In Figure 1, the front panel depicts a desktop version of one of the user interface versions discussed, analysed and evaluated in this paper. Correspondingly, in Figure 2, the user interfaces are depicted as a computer screen on the right side of the picture, even though the actual interfaces are primarily implemented using Web technologies and are able to be used on other devices such as tablets and mobile phones. The specifics of the user interfaces constitute the main focus of this paper.

In the demonstrator (in addition to the data coming from the smart meter), the information about the usage of the devices is acquired via the explicitly marked up “TeleCont” controller component in Figure 2. The latter is also used for controlling the appliances. It has various types of input signals (MP-module, analog, digital, relay output) for data acquisition. TeleCont processes obtain data and transmit it via Ethernet to the router (marked up as “Gibraltar” in Figure 2). It provides a secured tunnel to the end-user. Devices with an Ethernet port can directly connect to the router. Inside the building, the controller collects the data from sensors for a specific flat in real applications and transmits them via Ethernet to the central control point for data processing. The controller also has a scalable set of add-on modules that can be used to connect different actuators and sensors (temperature, humidity, light). It enables remote control of general house process equipment and provides the fastest response in case of emergency (flooding, fire, etc.). Apparently, cost effectiveness is a major factor in the success of the solution in practice. For example, private customers are only ready to invest in such a solution when amounts paid are on par with those expected to be saved by it in the next years, i.e. 300-400 Euro per installation in an apartment [Fensel et al., 2013]. This target is most effectively accessible in practice, when the installations are practiced in larger buildings containing many apartments or rooms, and these accommodations share the server infrastructure and controllers (factually, home gateways), which are the most expensive additions to the typical settings modules of the hardware installations. The other parts, such as smart meters, as well as the


electrical appliances and devices with open data interfaces (e.g. provided via smart plugs), are becoming increasingly available in the homes of energy consumers. The suggested semantic layer infrastructure and services are therefore built directly on these data interfaces with minimum hardware costs. Also, the users interact with the suggested services, employing the already available to them interaction devices, such as PCs, tablets, and mobile phones.

Figure 2. SESAME System Architecture

3.2 SESAME Software Layer

A connector software was used to write wrappers for the data received via web services or REST calls from these devices. The data was collected in intervals of 15 minutes from the devices and stored in an OWLIM1 based semantic repository. End user services could then read the combined data from devices as well other static information available in the semantic repository to reason over them and to provide various functionalities to the user. Semantically enabled software makes automated decisions based on data coming from physical and virtual devices, system users and utilities. Using semantic models and communication technology, it links the hardware layer with the end user interfaces of services. For the purpose of managing and using benefits of the smart home, the developed software aims to be easy to use, intuitive, with appropriate response times (i.e. adequate according to the users’ perception), interoperable with different devices and easy to maintain and upgrade. The software was developed with the main objective of monitoring and controlling the environment and appliances in the smart home. The SESAME system software, in particular, is responsible for:

- downloading tariff profiles from utility public locations on the Web; - communication with sensors and actuators in the house; -Smart Meter data acquisition through the data concentrator; - managing and administrating the whole system and - reasoning on the data received.

The data is being permanently collected, lifted up to the ontological representation, and placed in the semantic repository. Further, the reasoning mechanisms and building automation rules are enabled to access and apply this data in varying building automation management scenarios.

1 OWLIM: http://www.ontotext.com/owlim


http://www.ontotext.com/owlim

3.3 SESAME Ontologies and Rules

The SESAME system uses Environment and Pricing Ontologies for reasoning purposes. Ontologies are published using a web service, and the request for its initialization comes from an external service. The reasoning process consists of loading ontology data, running the reasoning engine and storing the reasoning results. The Pricing Ontology is used for creating the schedule for turning on and off devices that are big energy consumers. This is organized using data from tariff plans and resident preferences. The main class in this ontology is DecisionSet, which is prepared for ontology by external processes. It contains all possible combinations of tariff providers, tariffs, and prices per each 15 minutes interval, on which the ontology will make a decision, based on user preferences. The decision contains data about which tariff plan in which moment would be most appropriate to use by the higher energy consuming household appliances. The Environment Ontology is more complex, consisting of actuator actions concepts (actuator, device, device-type), system policy behaviour concepts (desired state, resident activity, current state, location) and appropriate rules. The details of the designed and used ontologies are presented in our previous publication [Tomic et al., 2011], as well as their later version published openly on DataHub2.

Ontology reasoning is based on SWRL3 and SQWRL4 rules that are applied using the Jess Rule Engine [Friedman-Hill, 2003]. Reasoning in this ontology consists of a set of rules that focus upon the schedule of activities, presence/absence in the room and turning on / turning off devices, depending on the desired indoor environmental conditions. Depending on the user interface requirements, the rules were represented employing various notations, and the corresponding rule transformation modules were then put into place. A rule example in the notation N3 is presented in Listing 1.

Listing 1. A Semantic Rule Representation in SESAME

: ElectricalBlowerKitchen a : Appliance . : Cooking a : Activity . : consumesPerHour a owl : DatatypeProperty . : isSwitchedOn a owl : DatatypeProperty . : canBeStarted a owl : DatatypeProperty . : hasActivityDuration a owl : DatatypeProperty . { : ElectricalBlowerKitchen : consumesPerHour : 5 . : ElectricalBlowerKitchen : isSwitchedOn :FALSE. : ElectricalBlowerKitchen : canBeStarted :TRUE. : Cooking : hasActivityDuration :30} => { : ElectricalBlowerKitchen : isSwitchedOn :TRUE}

The selection of a current activity in a particular room is such that the following conditions must be satisfied: presence of a person in any room in the house or presence in the house. An activity that falls under the second option is not a default activity, as it exists in an active schedule for the room, defining the duration of the activity during the period that belongs to that current time. The selected activity needs to incorporate some of the activities that the user has defined (in the system) as its own possible activity in the room. The current state of the room should comply with the requirement that not spend than the permitted time to leave the room and time elapsed between current time and the last presence detected in the same room must be less than or equal to the allowed time. Similarly modelled are the rules defining the setting of the predefined activity ResidentActivity_NoActivity (being passive) and ResidentActivity_OutOfHome (no presence in any room in the house), as well as rules which choose to turn on devices that would reduce the room temperature, and other rules for the control devices that affect the environment in the home. A rule for the exclusion (and inclusion) of standby devices differs from the other rules, as it has only condition for selecting a desired behaviour of standby devices.

2 Smart Building Ontology SESAME-S: http://datahub.io/dataset/smartbuilding-sesames 3 SWRL: http://www.w3.org/Submission/SWRL 4 SQWRL: http://protege.cim3.net/cgi-bin/wiki.pl?SQWRL


http://datahub.io/dataset/smartbuilding-sesames

http://www.w3.org/Submission/SWRL

http://protege.cim3.net/cgi-bin/wiki.pl?SQWRL

There can be many devices that may be affected in the same environment state parameter setting; therefore there are the rules that define the selection of devices with the lowest energy power, e.g. for selecting one device from a set of devices that impact the rising temperature.

4 End Consumer Interfaces We have designed three paradigms of user interfaces for energy end consumer services:

1) Touch screen interfaces for settings control, activity scheduling (HAN); 2) Interface for the acquisition of arbitrary policies employing the ontology concepts (PAT), 3) User profile -based policy recommendation and creation (EPR).

4.1 Touch Screen Interface (HAN) In order to manage and oversee the structure and behaviour of the system, the resident can use two client applications: HAN Manager and HAN Monitor. The development of functionalities for these applications was guided by a preliminary analysis of user requirements expected from such a smart home system. The first one is a Web based application with authorized access in which the user can direct and control all the sensors and actuators in the house. The HAN Manager regularly reads all the sensors in the house, making the system aware of the current state in the house. This interface of the HAN Manager application is presented in Figure 3. The underlying technologies like rule language, ontology design, etc. were also iteratively modified based on our requirements and performance of the respective technology.

Figure 3. Desired State Interface of the HAN Manager

The main form of user interface for everyday interaction with the SESAME system is the application HAN Monitor. This application is responsible for:

- Displaying various types of information regarding environmental conditions in the house, - Displaying relevant information about energy consumption, - Manual management of environmental conditions, - Manual plan for controlling devices with significant energy consumption (washing machine, boiler,

heaters), - Displaying actual plans for switching on devices with significant consumption, - Displaying various alerts and state information about technical parts of the system.

As an application designed for the broadest population, HAN Monitor fulfils the following requirements for usability and accessibility:

- Intuitiveness, with minimal requirements for formal training and education, - Capability to operate without keyboard, mouse or any other device attached to the main SESAME

monitor, - Displaying information in a consistent way among the application forms,


- Capability for easy management of the application state, switching between the screens and options.

Two views of the HAN Monitor are illustrated in Figure 4 and Figure 5. Particularly in Figure 5, the system shows all desired states that the user has configured. Each desired state is specified with the acceptable intervals for humidity, temperature and illumination measurements. If these are interrupted, the system switches the appropriate appliances (heater, lights, air condition) to perform regulation.

Figure 4. Touch Screen Interface of HAN Monitor

Figure 5. Desired State Interface of HAN Monitor

4.2. Arbitrary Policy Construction (PAT) Interface

Sometimes a resident might want to create a totally new policy from scratch, which might not be possible through the selective control that other smart home software gives to the resident. For example, it might not be possible to create a very user specific policy like: From November to February, Switch on the kitchen heater 30 minutes after I wake up during the weekdays. This rule, although very specific for a particular user/scenario, would make the system even more attractive for consumers who intend to use the system to the full potential it offers.

The Policy Acquisition Tool (PAT) [Zeiss et al., 2008] serves this objective by allowing users to create Semantic rules in the form of statements of triples. Each rule consists of a head (representing the intended goal) and a tail (representing the condition(s) under which the rule gets activated). The head contains a


unique statement while the tail can have a single or a conjunction of several statements. Each statement in turn is constructed in the form of an RDF triple.

Figure 8 shows a typical rule constructed using this tool. This tool, as well as the other ones, can be used as a web application allowing policies to be created (and saved) by the user even from a remote computer terminal outside the home.

Figure 6. SESAME Policy Acquisition (PAT) Tool

4.3 User Profile Paradigm for Energy Policy Recommendation (EPR)

To appeal individually to each user of the SESAME system, a more personalized approach to policy creation is envisaged, especially as the user might not always know what exactly he/she wants from the system or how to optimize his/her resources using the SESAME system policies. Our past experiences with several policy editing tools like PAT (section 3.3), Protégé SWRLTab (a SWRL editor)5, etc. convinced us that we needed an extremely simple interface for allowing non-expert users to edit/modify policies. Based on these observations, a policy construction and edition paradigm and interface, Energy Policy Recommender (EPR), has been created, taking into account the resident behaviour by coming up with policy suggestions best suited to his/her habits. Here, we ask each individual about their habits and preferences with respect to some of their basic chores in everyday life like sleeping, cooking, watching TV, etc. and reuse them in instantiating certain predefined policies according to their personal profile.

The service interface initially presents the user with a simple questionnaire asking him/her questions relating to habits and preferences, e.g. wake up time, on various aspects of his/her daily life (Figure 7). The questionnaire was inspired by the experimental feedback of a study conducted on two real home users while trying to gather all the information that would be necessary to fully harness SESAME’s features, by implementing appropriate policies.

5 SWRL editor: http://protege.cim3.net/cgi-bin/wiki.pl?SWRLTab


http://protege.cim3.net/cgi-bin/wiki.pl?SWRLTab

Figure 7. SESAME EPR User Profile Interface

To offer an idea of the type of queries in the questionnaire, we provide a preview below of some of the questions asked. Later in this section, we explain how their answers help us in generating customized policies for each user. Questions relating to user habits are of the following types:

a) Time of going to sleep and waking up during the weekdays and weekends, b) Time taken for taking a shower, c) Time from home to office and returning back home on various days of the week, d) Time spent in the kitchen to cook breakfast in the morning (weekdays and weekends).

Questions pertaining to user preferences might include: a) Preferences for type of energy available, e.g. Solar, Hydel, Coal, Atomic, etc., b) The order of preference amongst cheap power, energy type (green, hydel, coal, etc.) and energy

provider, c) Preferences in terms of higher and lower temperature thresholds for various locations in the

house, d) Choices with respect to the desired amount of lighting during various activities in the house and at

various times of the day.

The information collected from the EPR is stored as an individual user profile in an ontology, which can later be used to create user specific policies.

In the next step, the user is presented with a set of policies which we have predefined, keeping in mind the capabilities of the SESAME system and instantiated according to his/her individual profile (Figure 8). Most of these policies also represent the potential savings in terms of costs in euro per day, which can be achieved by the application of the respective policies.

The users can further fine-tune each policy and/or accept or reject them altogether. An example of such a policy is as follows:

Policy: Schedule the washing machine, dishwasher and other schedulable appliances to operate when the tariff is cheapest and you are not asleep.

This policy has been instantiated based on inputs relating to the user’s time of sleeping during various days of the week and his/her preference order relating to cheap power, energy type, etc.


Figure 8. SESAME EPR Policy Editing Interface

For each of these devices, the tool allows the users to either completely accept/reject the policy or introduce some minor changes to it. In the case of this policy, the change could be in terms of editing the timings of operation of the device to include situations when the user is watching television. For each schedulable device, it also displays the real time information about estimated consumption and/or savings in Euro per day made by the appliance. The users can therefore observe in real time the cost savings made by the changes they have introduced on the respective policies. In the end, the user may save the modified policies, which would be immediately available to the SESAME system for application. So, while PAT could be used in any generic application for creating policies, HAN and EPR were built specifically for the smart home setup described in this paper.

5 Evaluation and Results We have conducted field trial user studies with the set up and procedure described as follows. The goals of the user study have been:

a) To estimate the service approach and practices preferred by the users; b) To identify usability issues of the evaluated types interfaces and to derive suggestions for their

improvement; c) To find out how the interfaces for different levels of IT expertise were accepted by users in general; d) Acceptance of the level of automation of the system; e) Acceptance of the policy based paradigm of smart home control by “common” users (non-engineers).

As for the hands-on user study setup and procedure, the study was conducted with 11 test subjects of different ages (between 22 and 35) and sex (4 female, 7 male). In order to address the potential typical user group, the education and occupation of the volunteers were outside the field of computer science. To obtain an average opinion score, the subjects were asked to rate the criteria on a five-grade scale (1-fully disagree, 3-undecided, 5-fully agree) with half-grades possible (e.g., 2.5). The working set up has a smart home


demonstrator and a computer placed on a table. In the evaluations, we followed an established “Living Labs” approach [Hippel, 1986], where the users have been employing our services and their interfaces to co-create semantic energy saving policies.

The test with each user lasted for ca. 2 hours and comprised testing of the three variations of the user interface. The users were introduced to the system and the approaches in general, were given time to explore the system, and then were given a set of policy, automation and profile modelling tasks that comprise using the services for optimising energy efficiency. The instructions and the tasks given to the users on the three interfaces are provided in the Appendix of this paper. A questionnaire, the log files created during the test and the filled out observation forms were used to analyse the user study. A test conductor was observing the test person’s progress with the tasks, and taking notes during the test.

In the following three subsections, we present the aggregated results from the tests performed on potential users of the system’s three types of interfaces, particularly, what the users indicated they liked, disliked and recommended. This is an aggregation of the answers provided by the test takers to a written questionnaire. The questionnaire consisted of separate sets of questions for each of the three energy end consumer service paradigms (see the Section 4) tested. Figures 9, 10 and 11 illustrate summary feedback on HAN, EPR and PAT approaches. The last subsection, as well as Figures 12 and 13 contain comparison of the approaches.

5.1 Summary of feedback from the questionnaire for HAN

The user assessment of the HAN end user interface was as follows.

Question 1: How would you rate the overall clarity of the interface?

The user response to this query was mixed with around 46% of the respondents rating it 3 (1 = intuitive, 5 = confusing). The mean score achieved was 3.18 with a standard deviation of 1.08 indicating that although the interface was reasonably intuitive to the users, further improvements could make it more user friendly.

Question 2: You managed to find the information you were looking for?

A majority of the test takers (37%) gave 2 rating points (1 = easy, 5 = with difficulty) to the software on this criterion. The mean score achieved was 2.73 with a standard deviation of 1.01 indicating the relative ease of accessibility of information in the HAN interface.

Question 3: Describe the time it took for you to familiarize yourself with the interface.

46% of the test takers rated 2 (1 = very short, 5 = too long) as the time needed for them to get familiarized with the system. The mean score achieved was 2.55 with a standard deviation of 0.93. This indicates that the time taken for users to get familiarized was “acceptable” and this opinion was pretty uniform among all test users.

Question 4: Did you find the interface appropriate for everyday use?

To this question, an impressive 55% of the respondents rated the system as 1 (1 = yes, 5 = No). The mean score given by the test takers to the appropriateness of HAN in everyday use was 2.09 with a standard deviation of 1.45 suggesting decent overlap between user expectations and functionality offered by the system.

Likes: The HAN features most liked by test takers were myriads of functionalities and controls offered by it like creating user profiles, activity registration, device scheduling, desired state concept, holiday registration, etc. Users also liked the visual colourful representations of the user interface complete with pictures, sliding menus and the touch screen interface.

Dislikes: The biggest issue for most of the test takers of HAN was that it was too complex for them to understand all the technicalities offered. Furthermore, the features were distributed among three tools, which confused them when they wanted to search for some specific feature.

Recommendations: Most of the suggestions asked for the system to be merged into a single tool rather than three separate ones. Users also suggested removing functionalities that were too complex. Other recommendations included improvements in user interface, navigation, labelling of buttons and removal of too many tabs from the user interface.


5.2 Summary of Feedback from the Questionnaire for EPR

The user assessment of the EPR end user interface was as follows.


The response to this question was impressive with 78% of the test takers finding the interface to this tool very intuitive and rating it 1 out of 5 (1 = intuitive, 5 = confusing). The remaining 22% of users rated it 2 on the same scale while none of the ratings of 3,4 or 5 were observed indicating the way users could immediately relate to the simple interface.

Figure 9. Evaluation Results for HAN

Question 2: Rate the relevance of the questions in the questionnaire

On being asked whether the questions asked were relevant to what the users would expect in such a system, 45% of the respondents gave 2 rating points (1 = very similar, 5 = very different) to it. The mean of the overall ratings given to this query was 1.89 with a standard deviation of 0.78 indicating the users would readily answer similar questions, which forms the core of the tool enabling it to create customized policies.

Question 3: How close are the default policies to the ones you prefer?

45% of the test takers found the policies adequately relevant giving it a rating of 2 (1 = very similar, 2 = very different). The mean rating achieved was 2.11 with a standard deviation of 1.17 implying that the policies suggested matched user needs fairly well. This result is relevant, especially keeping in mind the fact that different users always have different needs and ways of automating the house by means of their preferred policies. Therefore, coming up with a set of policies that cater to most of their needs and is not irrelevant to any of their potential needs is very important in the context of this tool. A mean of 2.11 may thus be considered a good score in this scenario.

Question 4: Do you like the way in which the interface allows you to modify the policies?

An overwhelming 67% of the test takers found the policy modification system very intuitive giving it the highest rating of 1 (1 = Yes, 5 = No). The mean rating of 1.44 with a standard deviation of 0.73 suggests that the way users are allowed to modify the policies to tune it to their wishes was generally hassle free.

Question 5: Rate the length of the questionnaire.

Many of the respondents (45%) found the length of the questionnaire to be perfectly appropriate rating it 1 (1 = appropriate, 5 = too long/short). With the mean score being 2 and standard deviation of 1.12, we can comfortably conclude that the length of the questionnaire was appropriate for the users.

Likes: EPR was found to be simple, intuitive, precise and fast by most of the test takers. The feature showing the cost savings made by individual policies was also liked by most users. Among individual functionalities, users also liked the stand-by device management and device scheduling.

3.18

2.73

2.55

2.09

0 1 2 3 4 5

Clarity of interface

Ease of finding…

Time to get…

Appropriateness…

Mean score

Evaluation Results for HAN


Dislikes: There were generally no common features disliked by the users. The most relevant dislike was that this tool does not permit fully customized policies from scratch.

Recommendations: Apart from the recommendation concerning the functionality of creating policies from scratch, users also suggested slight improvements in the user interface to include greater resolution for the time scale, improving the policy modification interface, etc. Other suggestions included adding more questions for better user profiling and including more policies for even more control.

Figure 10. Evaluation Results for EPR

Besides this, we also asked the users to suggest a few policies of their own that they would like to see in such a system. Some of the more frequent suggestions will be used in developing future versions of EPR.

5.3 Summary of Feedback from the Questionnaire for PAT

The user assessment of the PAT end user interface was as follows.


Most of the users found the interface to be very intuitive as is suggested by 55% of them rating the clarity as either 1 or 2 (1 = intuitive, 5 = confusing). The mean score of the clarity of the PAT interface was 2.55 with a standard deviation of 1.37 suggesting an acceptable user interface.

Question 2: Combining simple sentences into more complex constructions to express your ideas was…

An impressive 64% of test users reported a score of 1 for this question (1 = very easy, 5 = very difficult). Furthermore, a mean score of 1.64 with a standard deviation of 1.03 clearly implies that users found it very easy to construct complex sentences from simple ones.

Question 3: Finding and selecting the right terms to express your ideas was…

Almost 37% of the users testing this tool rated the ease of finding correct terms to express their ideas as 4 (1 = very easy, 5 = very difficult). This figure suggests that for many users it was not very convenient to search for correct terms for expressing their ideas. However, a mean score of 2.82 with standard deviation 1.17 suggests that it was not too difficult for some others. Overall, slight improvements in the interface in this respect may yield much better results.

Question 4: Do you think that the amount of time you have generally spent on construction of a policy is appropriate?

An impressive 46% of users found the time spent in constructing policies perfectly fine by providing a rating of 1 (1 = yes, 5 = no) to this question. However, a mean score of 2.73 (standard deviation of 1.85) suggests that although some users find the time spent in creating these policies acceptable, this was not the case for others.

Question 5: Do you think you would be able to use this tool more efficiently with practice?


An overwhelming 91% of users testing the PAT tool strongly believed that they would be able to use the tool more efficiently with practice by giving a rating point of 1(1 = yes, 5 = no) to this question. The mean score of 1.36 (with a standard deviation 1.21) similarly reflects users’ confidence in being able to master this tool with a little practice.

Question 6: Was it easy for you to understand the system?

All the users testing the system gave rating points of 1, 2 or 3 (1 = yes, 5 = no) to this question while none rated it 4 or 5. A mean score of 2.09 and a standard deviation of 0.94 also suggests that it was easy for most users to understand the system and none of them found it extremely complex to understand.

Likes: Most of the users testing the PAT liked its simplicity, clarity, expressivity and ease of constructing even very complex policies by combining simple sentences. Many users could easily relate to the concise yet precise way of expressing sentences as “who-does-what” and the rules as “if-then” constructs.

Dislikes: Almost all users complained about the slowness of the system leading to longer rule creation times. For a few of the users, the system was not immediately intuitive.

Recommendations: Apart from unanimously recommending a faster system, users also suggested the use of more intuitive natural language terms in the drop boxes instead of the more technical ones currently in use. Another suggestion was to use complete sentences instead of phrases in drop boxes.

Figure 11. Evaluation Results for PAT

5.4 Comparison of the Three Interface Paradigms Evaluated

During the same user tests, we asked the test takers to rate the three user interface types based on a few criteria. The results are summarized in Figure 12. When users were asked to rate the clarity of the interface separately for each of the tools tested, almost 78% of the users preferred the EPR, 36% favoured the PAT and 0% favoured the HAN. Using the rating system between 1 and 5 (1 = intuitive, 5 = confusing), this conclusion suggests that EPR has the clearest user interface while HAN interface needs further improvement. PAT was also found to have acceptable ratings by the users. It must, however, be noted that all the interfaces developed have their own unique purpose and are designed at different levels of complexity. Therefore, user perceptions on intuitiveness of use without taking into account the underlying complexities is merely a way of knowing whether users normally prefer a simple interface with few functionalities or a slightly more difficult one with more functionalities.

After having tested all three tools, we asked the users to rate the most “intuitive”, “easiest to use” as well as the most “preferred tool” in general for system configuration, according to their perception. It turned out that in terms of intuitiveness as well as ease of use, the EPR scored the highest (7 votes) while the system configuration of HAN and the EPR were preferred by an equal number of test takers (5 for each) respectively (Figure 13). PAT was found to be the least intuitive and the least preferred tool for system configuration, while HAN was deemed to be the easiest by only 1 user. Through this trend, we can conclude that while the users would like the configuration tool for their system to have many visually attractive functionalities (like HAN), they would prefer a simplistic user interface to go with it (like EPR). Overall, with respect to our goals of evaluation, we came to the following conclusions:


a) Service approach and practices preferred by the users: Policy based approach was well received. Including user habits and preferences for automation was also popular but gives rise to unacceptability towards features not suited to a certain user habit;

b) Identification of usability issues of the evaluated types of interfaces and suggestions for their improvement: The common problem faced during evaluations was encountered whenever complex tasks were to be performed. The general conclusion was to keep the user interface as simple as possible and break down complex tasks into a number of simple sub-tasks;

c) Finding out how the interfaces for different levels of IT expertise were accepted by users in general: Observing the users, as they have learned the tools and executed the tasks, we have recorded that they have been able to use the tools to conduct the given tasks correctly. However, for 2 users it took considerably longer than average to understand and employ EPR, and for 1 user it took considerably longer than average to understand and employ PAT;

d) Acceptance of the level of automation of the system: Respondents were generally excited about the level of automation achieved by the mentioned tools, especially with the use of customizable policies;

e) Acceptance of the policy based paradigm of smart home control by “common” users: The policy based approach was well accepted by the users, although their tolerance of various policy editing tools understandably varied from one to the other.

Figure 12. Interfaces Clarity Comparison for Three Paradigms

6 Related Work In this section, we relate to other potentially relevant services of a similar character as to that which we have developed, referring to their hardware, software, and user response. Regarding innovative hardware settings, currently Apple is pioneering smart plugs [AppleSP] followed by other innovative startups [AlertMe].Cisco is manufacturing a Home Energy Controller [CiscoHEC] able to connect and control a large variety of heterogeneous devices. Cisco is applying these tools in pilot projects (predominantly in Madrid), under the Connected Urban Development initiative [ConnUrbanD].

Regarding software and services (including semantic) development, start-up companies developing mobile services for building automation already exist in the US and Germany [Schulz, 2010]. Google’s PowerMeter [GooglePM] also provides energy efficient services for end-consumers, based on the technical infrastructure of certain providers and manufacturers with whom they have partnerships. However, Google’s Power Meter was discontinued in the summer of 2011 due to the fact that the “efforts have not scaled as quickly as we would have liked”, the latter caused presumably by the high entry barrier to the platform restricted by B2B agreements. Pachube [Pachube], which has been acquired by LogMeIn, is a platform for supporting data streams in general, and is currently comprised of certain individual data streams particularly relevant to energy usage. Nevertheless, it provides neither smart home hardware nor energy efficient services. The sensor data collected with the SESAME user interfaces however, can be streamed in Pachube and similar environments.

Among approaches of research origin, similar semantically enabled domotic systems have been constructed [Bonino et al., 2008], but user interface and accessibility have yet to be investigated. Likewise,


purely Web-based services and interfaces for energy awareness have been designed, however, without connections to sensors and smart homes [Zapico et al., 2011]. As soon as semantically enabled platforms for mashing up sensor data [Gray et al, 2011] become available in practice, our system and interfaces would facilitate the provisioning of smart home and energy data.

The work done by Xu et al. [Xu et al., 2009] addresses some of the reasons why a semantic smart home system is superlative to other conventional smart home approaches. The underlying motivation stems from the fact that every home is different and therefore there cannot be a ‘one size fits all’ approach for smart home development. They proposed an ontology based framework for automatic composition of smart home services and applications based on the equipment present in the respective households. The environment parameters specific to individual customer needs are dynamically adapted based on the resources available. They try to ontologically represent the whole process model involved in the smart home application along with its individual components. Another available semantic technique applied to smart homes is semantic decision tables [Tang and Ciuciu, 2012]. Being semantically enabled, all such systems could efficiently share data, as well as interoperate and communicate, when deployed in practice.

Other authors also leverage the advantages of semantic technologies towards attaining a more flexible resource discovery and orchestration of Home Building Automation [Loseto et al., 2012].In such research, an enhancement of the ISO/IEC 14543-3 standard is devised that enables a framework supporting user profiles and device features to be represented semantically among other things. This standard, which is also known as EIB/KNX (European Installation Bus/Konnex) [Konnex, 2006], is extended to include a context aware, multi-agent framework that enhances the decision support capabilities in building automation by integrating semantics in the underlying stack. Other broader details of end user services however, such as their ease of integration and the place of semantic rules in the system, are missing.

Additionally, these solutions do not focus on an end to end smart home setup based on semantic technology aimed for real users. Less attention is paid to end user services and interfaces which are an essential part of the system, as the end users who are mostly non-experts with limited knowledge, need to be able to operate the full functionality offered optimally by the system.

Figure 13. User-Perceived Comparison of Three Paradigms

Regarding the user acceptance aspects, Loviscach [Loviscach, 2011] classifies the tasks accomplished by “energy conservation assistants” as (a) information (b) advice (or persuasion) and (c) automation. Most of our apps are focused on (a) and (b). Users’ primary interest in saving energy is very much aligned with earlier studies conducted in different settings – such as in the form of a game on the social networking site Facebook [Schwanzer and Fensel, 2010]. As shown in [Kimura et al., 2011], when users are presented with appropriate apps enabling energy savings, there is a tendency for them to try to surpass each other in terms of achieved savings. However, the studies with the demonstrator were more tangible and also made users contemplate the complexity and cost of the system.

Further studies [Hu et al., 2011] focus on the undesired behaviour caused by combining several smart home features and custom user policies which they call “feature interactions”. The main aim is to identify and solve interoperability issues that may arise when intelligent services based on semantic policies are


being customized by end users. A Semantic Web-based policy interaction detection method (SPIDER) framework is proposed which would semantically model the rules for detecting user interactions with smart home policies. This would, in turn, help in detecting the feature interaction problems for smart home service creation. The solution however, is mainly focused on feature interaction which forms only a small part of the entire smart home setup.

7 Conclusions Semantically enabled technology offering energy optimization for efficient home and business applications is a rapidly emerging market in Europe. This paper presented the SESAME system, a semantically-enabled platform for energy-efficient applications, discussing end consumer services and evaluations. We used communications, services and semantic technology to create a flexible system with automatic reasoning and a variety of innovative user interfaces that can stimulate and facilitate users to use energy more responsibly. We conducted user studies demonstrating a high level of acceptance along with the high expectations that users have of such energy efficient smart home systems and services. Especially EPR’s user profile orientation, user centricity and its simple menus for modification of profile and rules is the easiest to use and the tool of choice for the majority of users. Overall, we see a high potential for such technology as a basis for energy efficient strategies of the future, especially when services and interfaces are tailored to the users individually and easy to operate. In the follow up, we installed the SESAME-S system in two real-life pilot buildings, where we prove the technology’s real-life operational feasibility and market applicability [Kumar et al., 2012]. Based on our experience, many systems in present day households are themselves automatic or ‘smart’ and need no further assistance from a smart home system. For example, refrigerators have their own cooling cycles which are suitable to their underlying architecture and build. Any external tampering with this cycle by another system would be ill advised, especially if the external system does not take into account the details of the internal working of the refrigerator. While our system does not aim to control these “independently-smart” household appliances, we definitely want to gather and semantically process the data from them for at least two reasons:

(a) To monitor the overall efficiency of the home system whose actual state is only a consolidated state of its appliances combined. While one may not be able to control a central heating system, any data relating to its state like ON/OFF, temperature, etc. may be used in conjunction with other contexts in a home to decide the state of other controllable appliances. For example, based on the state of the heater and temperature in a room, the system might decide whether to open a controllable window or switch ON an exhaust fan.

(b) In case none of these is possible, the system may use data from the central heater in conjunction with other information to show an alert in one of its user interfaces in order to shut down the heater or close the window. Other innovative services may also use this data in providing various functionalities in a smart home system.

Thus, the real time data coming from various appliances as well as sensors should be integrated with the static home data and existing system policies to enable such decision making. To solve this issue, all the data coming from various devices was annotated semantically based on the already existing ontologies that we specifically created for this purpose, and updated within a large scale semantic repository. Most of the smart home energy efficiency scenarios have not required an immediate real-time reaction on the changing data, so the data access could be resolved by regular querying, policy adaptation and removal of the historical data which was no longer relevant. In terms of the broader, real-life deployment of the SESAME system, for the future work, it would be possible to obtain, from the real system’s usage data, a more extensive quantitative analysis of the effectiveness of applications and user preferences of various energy efficient smart buildings and user interface paradigms.

Acknowledgements

This work is supported by the FFG COIN funding line, within the SESAME and SESAME-S projects. FTW is supported by the Austrian government and the City of Vienna within the competence centre program COMET. The authors thank the whole SESAME project team for their valuable contributions – especially


colleagues from Experimental Factory of Scientific Engineering (EZAN), Russia, for the SESAME hardware development and E-Smart Systems d.o.o., Serbia, for the development of the HAN interfaces, as well as Amy Strub for the editing and English proof-reading of this paper.

Appendix: User Trial Tasks Descriptions

Case study 1: HAN Simulator

1) Aim: Scheduling a device. Parts of this software can be used to schedule operation of certain devices at desired times of day based on, for example, when the tariff is low compared to peak consumption hours or (potentially) when you are away at work.

i) In this respect, your first task is to check the status of the Laundry machine. If it is switched ON, then turn it off on the interface. Now you are ready to schedule this device and set it to start at a desired time of day.

ii) In this step you need to set the device plan for washing according to your preference. Try to set the plan for Laundry washing in a way in which the time interval coincides with the current time.

iii) If you have correctly set the plan to coincide with the current time as suggested in the above step, you should now see that the LED denoting the washing machine is lit. You can cross check this in the interface you used in step (a).

2) Aim: Observe and familiarize yourself with concept of desired state. Another important feature of this system is that it allows you to save your preferred temperature, humidity, lighting, etc. in the form of ”Desired States”. You can create several of these desired states and set them in advance to be activated at various times of the day/day of the week.

i) Your first task is to check the currently active “Desired state” in the living room and note down its active parameters (temperature, humidity, etc.). This software also allows you to classify your days in advance as weekdays (working days), weekends, and holidays. Classifying your days in advance allows you to set different desired states to be activated on different days. You can, for example, set 26th October as a holiday and tell the system to activate a holiday specific desired state for days marked as holidays.

ii) To experiment with this feature, set today’s date as a “holiday” in the system. iii) Now, set the current living room profile for holiday to a different value than the currently

active desired state observed in (i). Check if the desired state was activated. iv) On the demonstrator (suitcase), you will find two temperature sensors which look like thin

metal sticks protruding out of the part of the demonstrator where the picture of a house is shown. Gently hold the temperature sensor for the living room till the temperature rises by almost 1°C. At this point, the small fan (and an LED representing the same) should automatically be switched ON, indicating that when the temperature rises above the comfort zone you set in task 2 (b), the system automatically intervenes in order to bring the conditions back to your comfort level.

3) Aim: Understanding the SESAME Simulator.

i) Set your presence and absence conditions and change various environmental conditions of various rooms in the SESAME simulator. Observe and understand the changes taking place on the demonstrator.

Case study 2: Questionnaire based Policy Acquisition (EPR)

Aim: Understanding the tool.


This tool has been developed to give you a jump start into the SESAME system. One of the highlights of our project is that it enables you to create smart policies (rules) for the activation/deactivation of various appliances in your home which you can store in the system, and the system will behave accordingly. As a first step in this direction, we ask you to give us some information about your daily habits and mundane routines to the best of your knowledge and based on your input, we instantiate several pre-defined policies customized according to your day-to-day habits. You can either accept or reject these policies as you choose. We also allow you to tweak these policies further in case you do not wish to accept them, as it is but in a modified format. a) Your first task is to input your habits in the introduction page to the best of your knowledge.

b) In the next task, you are invited to play with the policies that we have created based on our

perception of what would be convenient for you as per your input in the previous task. You can accept/modify/reject these policies. Go through each of them and see how you could make your home automation work in a more eco-friendly way.

c) Save the rules after you are done with all the modifications.

Case study 3: Policy Acquisition Tool (PAT)

Aim: Creating and saving a complex policy.

a) Introduction: This tool allows you to create smart policies/rules aimed at providing a smart home experience while also reducing the overall costs of energy usage. Each rule starts with a triple of fields represented by (i) Who (ii) is/does (iii) what. You are required to input under “precondition” which field you would expect the rule to be triggered. This is followed by a “result/consequence” field where you should put the actions that need to be performed if the “precondition” you specified is met and the rule is triggered.

b) Create a rule whose precondition consists of three sentences (represented by 3 sets of triples) for example:

i) The Cooking activity has a duration of 30 minutes, AND ii) The Electric Blower in the kitchen is switched ON (its status is TRUE),

c) In the post-condition (the triple followed by result/consequence) put the action to be done as:

i) Electric Blower is Switched ON is set to TRUE.

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