SMART HOMES AND SAVING ENERGY...4 I SMART HOMES AND SAVING ENERGY SMART HOMES AND SAVING ENERGY I 5 EXECUTIVE SUMMARY The REFIT project explored how householders use and interact with

SMART HOMES AND SAVING ENERGY

REFIT Smart Homes and Energy Demand Reduction

The REFIT project final report for industry and government

SMART

This report was prepared by:

www.refitsmarthomes.org

CONTENTSExecutive Summary 4

Project Overview 6

Overview of REFIT Smart Home field trial 8

Overview of REFIT dataset 10

TOPIC 1 - Users and Smart Homes 12

TOPIC 2 - Control in the Smart Home 14

TOPIC 3 - Challenges in installing Smart Home technologies 16

TOPIC 4 - Heating practices in Smart Homes 18

TOPIC 5 - Understanding appliance use through disaggregation 20

TOPIC 6 - Understanding energy demand through activities 22

TOPIC 7 - Retrofit advice and the REFIT Home Energy Report 24

TOPIC 8 - Evaluating energy savings using Smart Home data 26

TOPIC 9 - Future service provision 28

List of REFIT outputs to date 30

Smart Homes and Saving Energy: The REFIT project final report for industry and government

This report was prepared by Dr Jason Palmer and Nicola Terry at Cambridge Architectural Research Ltd and the REFIT project team based on the research undertaken by the REFIT project during 2012-2015.

Please cite as: Firth SK, Cockbill S, Dimitriou V, Hargreaves T, Hassan TM, Hauxwell-Baldwin R, Kane T, Liao J, May A, Murray D, Mitchell V, Oliveira L, Stankovic L, Stankovic V, Webb L and Wilson C. (2015). ‘Smart Homes and Saving Energy: The REFIT project final report for industry and government’, prepared by Cambridge Architectural Research and published by Loughborough University, UK

The REFIT project: Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology

The REFIT project is a Research Councils UK funded project (Grant reference: EP/K002457/1, EP/K002368/1, EP/K002430/1) involving Loughborough University, the University of Strathclyde, the University of East Anglia and nine industry project stakeholders: Adapt Commercial, BSRIA, COMIT, FIATECH, Green Energy Options, National Instruments, National Refurbishment Centre, RWE and Sentec.

For more information see www.refitsmarthomes.org

For further information on the REFIT project please contact:

Dr Steven Firth (Principal Investigator) Building Energy Research Group School of Civil and Building Engineering Loughborough University LE11 3TU, UK +44 (0) 1509 228546 [email protected]

Dr Tom Kane (Project Manager) Building Energy Research Group School of Civil and Building Engineering Loughborough University LE11 3TU, UK +44 (0) 1509 565182 [email protected]

The REFIT project team:

• Loughborough University – School of Civil and Building Engineering: Dr Steven Firth, Prof Tarek Hassan, Dr Tom Kane, Dr Lynda Webb, Vanda Dimitriou, Dr Michael Colman, Dr Farid Fouchal

• University of Strathclyde: Dr Vladimir Stankovic, Dr Lina Stanovic, Dr Jing Liao, David Murray, Amar Seeam

• University of East Anglia: Dr Charlie Wilson, Dr Tom Hargraves, Dr Richard Hauxwell-Baldwin

• Loughborough University – Loughborough Design School: Dr Andrew May, Dr Val Mitchell, Dr Luis Carlos Rubino de Oliveira, Stuart Cockbill, Dr Chris Parker

The REFIT Advisory Board

We would like to thank the following members of our Advisory Board for their support and advice throughout the project.

• Roger Thornton, Steve Dawson and Tak Kaneko, Sentec

• Simon Anderson, Green Energy Options

• Edmund Barrett, RWE

• Sarah Birchell and Mike Smith, BSRIA

• Robert Lee, National Instruments

• Stephen Passmore, the Energy Savings Trust

• Chris Ward-Brown, National Refurbishment Centre

SMART HOMES AND SAVING ENERGY I 3

SMART HOMES AND SAVING ENERGY I 54 I SMART HOMES AND SAVING ENERGY

EXECUTIVE SUMMARY

The REFIT project explored how householders use and interact with Smart Home technologies. It also sheds light on how such technologies could support efforts to retrofit homes, save energy and cut carbon emissions.

Smart Homes are usually taken to include sensors, automation devices and controls for local and remote control of energy systems. However, their purpose is still unclear. Should the focus be energy management, or leisure and entertainment, or even security and assisted living? Currently, energy dominates, but it is quite possible that comfort and convenience are more important to households, and there is a real risk that Smart Home technologies simply add to the number and complexity of energy-consuming products in the home.

Our approach in the REFIT project included:

• Before-and-after interviews with 20 households that installed Smart Home technologies

• Comparing Smart Home technology marketing materials against householder expectations

• Analysing internal temperatures and energy use to look for opportunities to save energy for heating

• Using smart thermostatic radiator valves to record how people adjust radiator settings over time

• Using the notion of ‘activities’ as a more meaningful way to monitor energy use at home

• Applying co-design, where householders themselves take part in energy audits and are better able to understand the implications of their energy use

We examined the gap between marketing material produced by smart technology providers, and householders’ lived experiences of using smart technologies. In particular, we tried to find out whether these technologies actually gave people more control over their homes.

New methods were developed for ‘disaggregating’ electricity use in the home: working out what fraction of electricity is used by individual appliances. We looked at energy use through the lens of ‘activities’: how householders spend their time, and how they usually conceptualise their time at home.

We found that people’s use of smart technologies changed over time, with enthusiasts using the equipment intensively in the first four months, then using it less, and in simpler ways, once they had seen what it could do. However, there are still some problems with user interfaces, and some householders were overwhelmed by advanced controls.

Before installing equipment, it was quite common for people to be fearful of over-powering and complicated technology replacing simple tasks like adjusting a radiator valve. After the event, some householders did learn how to use new controls – but often only one person in each household, leaving that one in control even if there were several occupants.

We looked specifically at opportunities for saving energy for heating – by tracking daily temperature profiles alongside smart radiator sensors and energy use data. This allowed us to pin down heating practices and compare them against the assumptions in common energy models. We are currently using this to develop models to predict average heating on and off times based on the weather. This would bring major benefits to electricity generators in managing peak demand periods as the UK transitions to electric heating.

Better understanding of appliances in the home – when they are used, and how much electricity they use – would help to overcome some of the challenges in reducing household electricity use. We addressed a number of the challenges in identifying individual appliances using only ‘whole-house’ or ‘meter-level’ measurements: so-called ‘disaggregation’. This enabled enhanced appliance retrofit/upgrade decisions, demand prediction from appliances within households, opportunities for load shifting and improved understanding of households’ daily routines.

Collaborating across academic disciplines in science and humanities allowed us to explore the concept of ‘activities’ (such as cooking, laundering and socialising). This opened up a new area of research and we made progress towards real-time feedback for householders, based on their domestic activities. We designed new ways of detecting what activities householders were doing, based on a new classification of activity types, coupled with analysing electricity use profiles. Potentially, this could offer a more economical way to collect national time-use data showing how people spend their time at home. Activities are also much more meaningful for householders than esoteric measures of ‘kWh’.

We used ‘co-design’ approaches to involve householders themselves in carrying out energy audits of their homes and provided personalised advice on improving energy efficiency. This included several iterations of presenting feedback and improvement advice. The main conclusion to emerge was that householders are often keen to engage, but that they need information to be put into a meaningful context first. ‘What-if’ scenarios were particularly popular, with householders asking how much money they could save by changing their behaviour in different ways.

We used advanced statistical techniques and new, sophisticated modelling methods, to try to disentangle the savings due to energy efficiency measures from changes in energy use due to the weather or changed controls. This is an important step towards developing a robust way to test new energy efficiency products and – if they are not delivering the expected saving – to intervene.

Lastly, we used creative methods with homeowners to outline a set of services that could be offered in future, based on the data from Smart Meters and other technologies in the home.

Our overall recommendations

• There is a need to develop a more thorough understanding of Smart Home technologies and how users interact with them – going beyond reductionist questions about energy saving and controllability.

• True interdisciplinary approaches should be used in developing smart technologies, particularly by involving design research and social science research alongside traditional engineering disciplines.

• There is a need for scalable ways to identify and understand how householders use energy services, both appliances and heating, in order to offer tailored energy saving recommendations.

• Advanced heating controls must include a simple override function (like ‘turn off now’, or ‘one hour of extra heating’) that does not force people to log onto a computer to change timer settings.

• Room-based heating controls should use advanced learning algorithms to learn the heat loss characteristics of individual rooms so that energy use is minimised while thermal comfort is maintained.

• Initiatives aimed at providing advice on household energy use must involve householders. They need to give simple and meaningful messages, connected to householders’ own lived experiences and activities.

• Such initiatives should also take advantage of ‘trigger points’ to overcome barriers – eg. linking energy efficiency work to moving to a new home.

• A standard method of estimating the likely savings from energy efficiency upgrades needs to be developed, which makes use of measurements recorded by Smart Meters and Smart Home technologies.

The REFIT project studied Smart Home technologies and their potential for energy savings

Our homes make up 25% of the UK’s carbon emissions

By 2020, most homes could have Smart Meters and other Smart devices.

2050 target80% CO2 reduction

25%

THE OPPORTUNITY THE SMART REVOLUTION

PROJECT OVERVIEW

80%

REDUCTION

CARBON EMISSIONS

THE PROJECT HAS PROVIDED US WITH

New insights into how to integrate Smart Home technologies

A new understanding of the diverse user experience of Smart Homes in everyday lives

SMART

201526 million UK homes

SMART

SMART HOMES AND SAVING ENERGY I 76 I SMART HOMES AND SAVING ENERGY

The REFIT project was a 3.5-year research project funded by Research Councils UK. It was one of the 22 research projects in the Transforming Energy Demand through Digital Innovation (TEDDI) research programme, funded by a joint initiative of the Research Councils UK Energy Programme and Digital Economy Programme.

The project ran from May 2012 to October 2015 and employed five full-time university researchers and eight academic staff. The work was a collaboration between Loughborough University, the University of Strathclyde and the University of East Anglia, together with a number of stakeholder partners.

The University partners were:

• The School of Civil and Building Engineering at Loughborough University – which led the overall project and provided expertise in monitoring technologies, data analysis and modelling of space heating energy use in domestic buildings

• The Electronic and Electrical Engineering Department and the University of Strathclyde – which led the monitoring of household electrical appliances and provided expertise in electricity disaggregation and activity recognition algorithms

• The Tyndall Centre at the University of East Anglia – which led the study of user behaviour and provided the behavioural science expertise in the project

• The Design School at Loughborough University – which led the work on retrofit advice to households and provided expertise in user-centred design and co-design techniques

The REFIT project explored the Smart Home concept in the UK context to determine the potential for Smart Home technologies to help with energy saving and energy management for UK homes. We focussed in particular on retrofit technologies and supporting households with new retrofit advice.

We installed existing off-the-shelf Smart Home products into 20 homes in this study. This included the Smart Home product range manufactured by RWE and imported from Germany, the VERA product range, the CurrentCost product range and the British Gas HIVE heating system.

Our work focussed on a small and detailed study of 20 households. This choice was made so that we could observe how Smart Home technologies would integrate into a real-world setting of family lives and existing buildings. The small sample allowed us to carry out very detailed measurements and interviews that provide unique insights into Smart Home technologies.

This report provides our latest project results. The nine Topic sections demonstrate the wide variety of methods and perspectives that the university partners’ various disciplines bring to the study of energy use and behaviours in homes. The project demonstrates the clear benefits of people from different subject areas working closely together.

The work is on-going and we are currently preparing a number of academic journal papers that will present the research findings in more detail. These will complement the conference paper publications listed at the back of this report.

We are also committed to making the data from the REFIT project available to the wider research community as an open-access dataset. The preparation of this dataset is underway and will be released on the REFIT website.

The UK will not get the full benefits of Smart Homes by default. This project provides underpinning scientific knowledge to enable the potential benefits of Smart Home technologies to be fully realised. However, achieving this will require larger, more extensive and representative studies of household energy use to be conducted in the near future. Such studies are essential and timely given the UK Government’s commitment to install Smart Meters in every UK home by 2020, resulting in a step-change in our ability to record and manage the use of energy in homes.

For more information, please visit:

www.refitsmarthomes.org

THE REFIT PROJECT SAVING ENERGY WITH SMART HOMES

ENERGY SENSOR DATADetailed dataset collected from

20 Smart Homes

PERSONALISED ENERGY SAVING ADVICE

New insights into consumer feedback for Smart Homes

New service insights for Smart Homes

CLOUD ANALYSIS OFNew building physics model

New data analysis approaches

THE CHALLENGE TRANSFORMING HOUSEHOLD ENERGY

8 I SMART HOMES AND SAVING ENERGY

YOURHOUSE

1• 20 East Midlands homes recruited• A wide diversity of households• Trial period -June 2013 to April 2015

THE TRIAL RECRUITMENT

4

20

GAS USE

11,527 kWh

GAS COST

£621.73

ELECTRICITY USE

3,969 kWh

ELECTRICITY COST

£538.11

TOTAL USE

15,497 kWh

TOTAL COST

£1,159.11

HOME


2INSTALLATION

35

• Up to 30 monitoring sensors installed in each home• After 12 months, the homes were upgraded to Smart Homes• Around 15 Smart Home devices were installed in each home• The household members took control of their Smart Home

Motion Detector

Smart Meters

Window Sensor Networked Smoke

Detector

Smart Thermostat

Programmable Radiator

ValveSmart Plug

Upgraded Appliances and

LightbulbsDraught Proofing

New Windows

Loft Insulation

Solar Panels

Wall Insulation

New Boiler OR

A Heat Pump

OVERVIEW OF REFIT SMART HOME FIELD TRIAL

• Each home received up to 12 visits• Household members were interviewed throughout their Smart Home experience• Video and phone interviews captured user expectations, experience and acceptance levels

• Detailed energy feedback was created for each home, using the sensor data • Feedback was given in the REFIT Home Energy Report and on the links between energy consumption and household activities• Household responses to the feedback provided insights into expectations, usefulness and potential improvements

• Green Deal assessments were conducted for each home• Household drivers and barriers to home retrofits were studied • The impact of detailed energy feedback on the willingness to retrofit was investigated

HOME VISITS AND INTERVIEWS

FEEDBACK

RETROFIT ADVICE


OVERVIEW OF REFIT DATASET

Months from June 2013 - 20 Houses

1 2 3 4 5 6 7 8 9 10 11 12

Recruitment survey of

attitudes to new technologies

Initial interviews and videos

Post-install Impressions Interviews

Smart Meter Data Interviews

Smart Home Service Propositions

Interviews

Pre-install Expectations

Surveys

Sensor data• Mains electricity and nine appliances measured every 8 seconds• Mains gas measured every 30 minutes• Air temperature in all rooms measured every 30 minutes• Radiator surface temperature in all rooms measured every 30 minutes• Humidity and light levels in three rooms measured every 30 minutes• Domestic hot water pipe temperature measured every 30 minutes

Socio-demographic information

Home Energy Audits

Home survey of rooms and appliances

The data collected during the project is being made available for other people working in the field. There is data from each stage in the time-line below, including information about the households in the study, their attitudes towards Smart Homes, transcripts from interviews and the energy use data. There is also information on appliances, internal temperatures, humidity, light levels and heating system use.


13 14 15 16 17 18 19 20 21 22 23 24

Time Use DiariesSmart Home Experience Interviews

Smart Home Experience Focus

Group

Longitudinal journey notes

Smart Home data – recorded as events occurred• Occupant motion – four sensors• Window and door opening – five sensors• Additional temperature, humidity and light levels – all rooms • Use of Smart Home devices – all devices• User control settings and profiles

USERS AND SMART HOMES

How does industry marketing differ from user expectations?


WHAT WE DIDWe conducted a systematic search of the peer-reviewed literature using key words denoting ‘‘user’’ and ‘‘Smart Home’’. This initial search yielded 12,310 articles. In two initial sifts, we reduced the sample to 538 articles by reviewing titles, and then titles and abstracts, and excluding all spurious or otherwise irrelevant hits.

We then used a final sift to exclude articles which mentioned ‘users’ but on closer examination did not focus on users either directly or indirectly in the research and analysis. The final sample was 150 articles that either explicitly investigated prospective users of Smart Homes, or implicitly considered users by addressing the usability, design or attractiveness of Smart Home technologies.

This set of 150 articles was dominated by engineering and technical sciences (61 %) with the remainder split evenly between health-related disciplines (19 %) and social sciences (20 %).

We also conducted a content analysis of industry marketing materials from 62 companies and compared this with results of a survey of 45 prospective smart home users.

Key scientific insightsThe research literature on Smart Homes and their users is marked by three distinct perspectives on Smart Homes that give rise to different research agendas. The functional view gives rise to a series of technological challenges around how enhanced functionality can be efficiently and reliably delivered. The instrumental view gives rise to a set of design challenges around how users can be made to accept and align with the energy reduction goals of the Smart Home, often based on rational responses to information and price signals. The socio-technical view gives rise to a more foundational and broadly cast set of challenges relating to the balance between users and technologies in Smart Homes, recognising the complex and contested nature of homes as places for technology adoption and use.

To date, however, industry and policy thinking has tended to emphasise functional and instrumental views in isolation from one another. Strong cross-cutting linkages between these three views appear to be absent from policy and industry thinking.

In contrast, we found that the Smart Home vision represented in industry marketing materials was clear and consistent, with agreement that Smart Home technologies:

• focus on enhancing whole lifestyles rather than delivering single, task-specific functions

• are installed incrementally, not as one-off whole home systems

• are additional to, or integrated within, existing technologies in the home rather than substituting them

• consist of largely inconspicuous, background technologies, with only some of the interfaces being conspicuous

• are targeted at an all-purpose audience rather than distinguishing specific types of users – apart from the assisted- living market

• engage users through multiple, familiar and intuitive interfaces

• allow users to ‘set and forget’ their control preferences

• do not engage users more broadly in the design, development or installation of the technologies

At the same time, our analysis highlighted three main areas of divergence or omission in Smart Home industry marketing materials:

1. Although Smart Home technologies were uniformly depicted as being in the background of domestic life, user interfaces were portrayed as highly visible in the foreground.

2. Data security was mentioned in only eight of the marketing materials from the sample of 62 companies. Only five of these mentioned data encryption. Instead, other approaches were adopted to secure users’ trust in Smart Home systems, including an emphasis on users being in control, and technologies being adaptable and reliable.

3. While the materials recognised that homes are lived in by whole families, they paid little attention to interactions between multiple users, or to deciding whose preferences should win out when more than one person is present.

Users’ expectations of the potential benefits of Smart Home technologies were very close to the value proposition offered by the marketing material, with energy management dominant, but saving time and improving security also important. Consistent with the marketing materials, prospective users expected both pre-set and immediate control capabilities via multiple and mobile software interfaces, with the Smart Home hardware working largely in the background. There are, however, two key areas of misalignment between the industry vision and user expectations.

First, prospective users have clear expectations that Smart Homes will be controlled by multiple users, potentially including even guests and visitors. Second, prospective users place greater importance than industry marketing materials on a range of approaches for building trust and confidence in Smart Home technologies, particularly with respect to privacy and data security.

Key policy or industry implicationsIf Smart Homes diffuse more widely into the fabric of everyday domestic life, the functional, instrumental and socio-technical views identified in the research literature will increasingly interact and combine, presenting more (and potentially more difficult) challenges. Policy makers and the Smart Home industry need to broaden the debate around Smart Homes beyond a narrow focus on technology or energy towards a discussion of how user wants, needs, and practices may co-evolve with technologies in a smart energy future.

Challenges, limitations and future researchThere is a pressing need to develop a better picture of who users are and how they might use Smart Homes. Too often, user-focused research has emerged as a consequence of a technological vision that is struggling to gain user acceptance, rather than taking users as the core entry points for thinking about the purpose of Smart Homes. The result is that current visions of Smart Homes have limited appeal and are perceived as failing to meet user needs. Our research identified four core research priorities for future work on Smart Homes and their users:

1. Privacy, security and trust: If Smart Homes are to diffuse more widely, it is critical that both policy makers and technology developers make user privacy and security more central to their Smart Home vision.

2. Control: Marketing materials play down automation to emphasise user-control. This risks sidelining the abilities of advanced algorithms to learn about user activities and automatically optimise home energy management.

3. Co-presence: How Smart Homes will deal with multiple householders and how this will impact on access to, and control of, the technologies is a significant and under-researched issue. Introducing new technologies for controlling the home environment may entrench, de- stabilise, or realign the broader dynamics of control over everyday life and between household members.

4. Conflicting benefits and energy intensification: Smart Homes are being marketed as simultaneously able to deliver energy management, leisure, security and health benefits, but these ambitions do not necessarily align. It is possible that users will adopt Smart Home technologies less because of their energy saving potential, and more because of the benefits they may bring in terms of improved convenience, comfort or social status. In this respect, rather than reducing energy use, current smart-home visions risk increasing it by failing to challenge ever more resource-intensive social conventions and developing and diffusing more energy consuming products and services.

For more details on this research see: Wilson, C., Hargreaves, T., & Hauxwell-Baldwin, R. (2015). Smart Homes and their users: a systematic analysis and key challenges.

Personal and Ubiquitous Computing, 19(2), 463-476; and Hargreaves, T, & Wilson, C. (2013). Who uses Smart Home technologies? Representations of users by the Smart

Home industry. Paper for the European Council for an Energy Efficient Economy (ECEEE) Summer Study 2013, Toulon/Hyères, France, June 2013.


TOPIC 1

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90%

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areas of misalignment

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CONTROL IN THE SMART HOME

RWE’s ‘Time-profile’ here shows the relatively intricate ways some participants tried to control the heating in some rooms to match how they used their homes.


WHAT WE DIDWe used a multi-method approach to complement the literature review described in Topic 1, comprising:

• a series of three interviews with 10 of the households in the REFIT field trial – the first interview before any technologies were installed, the second around a month after installation, and the third several months after installation

• analysis of screenshots of the online interfaces for the RWE and Z-Wave control systems captured monthly between August 2014 to April 2015

While the screenshots provide only snapshots of particular moments in time and do not show households’ real-time interactions with the systems, they provide valuable insights into the kinds of automation profiles that people set up. This provides an indication of what the households were trying to achieve by using the Smart Home systems.

Key scientific insightsThe research literature is marked by four distinct understandings of control in the Smart Home:

• Device-focus: concentrating on what devices are being controlled, and how

• User-focus: concentrating on users’ feelings of being in control

• Household-focus: concentrating on control relationships when there are multiple users

• System-focus: concentrating on wider systemic issues and the balance of control between society and technology

Existing research is dominated by device- and user-focused approaches. Social science research has occasionally explored a household-focused approach. There is much less work adopting a system-focus. Most research also concentrates on a single way of understanding control issues – resulting in a narrow and simplistic interpretation, which is unlikely to explain how smart-home controls will play out in real-life situations.

In interviews, we found very different levels of use of Smart Home technologies. Some homes used the installed technologies a great deal and in a wide variety of ways, others used the technologies very little, while others did not use them at all or abandoned them after a short period.

We also found that how people used smart-home technologies changed over time. High users tended to have an initial ‘learning period’ in which they described themselves as ‘experimenting’ with the equipment. Some created lists of things they wished to try out to increase their confidence. As a result of this learning period, we observed more diverse uses of the technology in the first four months of use as households tried to find out what the equipment could do. In most cases, however, this later settled down into simpler uses.

In general, there tended to be single users of the Smart Home systems even where there were multiple household occupants. Generally, adult household members took control, and usually (though not always) it was the individual with most experience or confidence in using computers and other digital technologies.

Several participants were frustrated by what they saw as ‘hard’ to use interfaces and ‘poor’ integration between the RWE, VERA and HIVE systems. The RWE and Vera systems were seen as difficult to control, which helps explain the more limited use of these systems. The difficulty of controlling these systems also partly explains why users tended only to use straightforward or simple ‘time schedules’ (see image opposite) rather than more complex event- or rule-based settings, which were deemed difficult to set up. In one case, for example, the householder explained that setting up an ‘event profile’: “Was a bit of a leap of faith. I wasn’t sure whether it was going to work because you have to set up all these logic gates.”

Where households abandoned using the system – sometimes after a few months of trying to learn how to use it – they often mentioned that they found it more difficult to use than their pre-existing manual system. In these cases, the additional control capabilities provided by the Smart Home systems were deemed unnecessary, excessively complicated and most definitely not ‘smart’.

Key policy or industry implications• User interface design and the integration and interoperability of Smart Home systems remains a key challenge for the Smart Home industry. It is critical that Smart Home technologies are easy to use without prior technical knowledge or expertise and that they can operate together seamlessly.

• The advanced control functions provided by Smart Home systems can be overwhelming, even to technically competent householders. Systems should be designed to allow all users to learn how to use them gradually.

• Many users were not aware of the full capabilities of the systems installed. Systems could therefore be designed to make suggestions to householders about what they might wish to do with them, rather than leaving householders alone to determine what they should control and how.

• Smart Home systems are demanding of their users – they take time to configure, learn and control. They also make householders think about their everyday routines and energy use. This is an opportunity to try to get more out of Smart Home systems, but also a significant risk because if householders deem the systems too complicated, they are likely to be abandoned. It is particularly important that Smart Home systems are seen as easier and more convenient to use than the systems they replace.

• Systems should be designed for more than one user, and take into account that users of Smart Home technologies may not be the same individuals who are in control of household management.

Key implications for usersUsers who were less technically competent often felt that the systems installed concentrated control in one user’s hands, leaving them feeling ‘out of control’ and unable to use systems they’d previously had access to.

Sometimes whoever took responsibility for setting heating controls before smart-home technologies were installed also took on this responsibility afterwards. On other occasions, the technical competence required by the Smart Home systems meant that these roles changed hands. This was perceived as disempowering for those that did not interact with the smart controls and led to conflicts and some households even abandoning the systems.

Challenges, limitations and future researchEven if technologies themselves are easy to use and operate together seamlessly, it is important that there is an effective support infrastructure in place to support householders. This infrastructure should not only be able to fix broken systems, but should also be able to assist householders in working out what Smart Home systems can do and how they might benefit them in their unique everyday lives.

Current understandings of control are far too narrow in most industry and policy debates. There is a tendency to focus firmly on controlling technologies and devices and, occasionally, on how this shapes people’s feelings of control. This leads to a near total neglect of how smart technologies impact upon the wider domestic environment (as a household-focus would illuminate) or upon the wider social world (as a system-focus would illuminate). Our research shows that these broader understandings are critical factors in shaping whether and how people interact with and use Smart Home technologies. This means there is a pressing need to develop a more thorough understanding of the multi-dimensional nature of control in Smart Homes and in ‘smart’ futures generally.

For more details on this research see: Hargreaves et al (2015). Smart Homes, control and energy management: How do Smart Home technologies influence control over

energy use and domestic life? Paper for the European Council for an Energy Efficient Economy (ECEEE) Summer Study 2014, Toulon/Hyères, France, June 2015


TOPIC 2

HEATING

BATHROOM

TIME - 24HRS MONDAY TO FRIDAY

0

30°

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CHALLENGES IN INSTALLING SMART HOME TECHNOLOGIES Comparing pre-installation expectations and barriers with the reality, after a year of use


WHAT WE DID

This section describes what we learnt from conversations with households participating in the study which happened in two stages. First, before equipment was installed in homes, when the smart-home technology was presented to the households for the first time. Second, about a year later, when participants had a long experience of living with the systems.

We conducted semi-structured interviews, and, when possible, all family members took part in the discussion. Sixteen households were interviewed in the first stage of the study, and nineteen in the second stage.

We transcribed the audio recordings from these interviews and analysed them by theme, to provide insights into households’ perceptions of the introduction of the technology. All recordings were imported to ‘NVivo’ (specialist software for analysing unstructured data), to code each statement and provide a better understanding of the qualitative data.

These interviews were used to investigate issues surrounding the introduction and adoption of Smart Home technology into participants’ lives. The longitudinal aspect of this research made it possible to compare user expectations and experience before and after the incorporation of the technology.

The first round of interviews provided a wealth of information about the expectations and challenges to introducing smart-home technologies. The topics for the second interviews were triggered by points raised in the first round, in an attempt to understand whether or not expectations were met, and if the initial challenges were overcome.

Key scientific insightsThis study investigated peoples’ perceptions before installing smart-home technology, as well as attitudes towards the systems after one year of experience using the devices. Even though participants appreciated the functional aspects and potential benefits of Smart Homes, they had numerous concerns initially relating to the technology (hardware and software), design (acceptability and usability) and spaces (home as complex places). After living with the devices for one year, participants reported that some expectations were not met, a number of the perceived challenges still persisted, and new challenges emerged.

Before installing the devicesAnalysing interviews before the introduction of smart technologies revealed that households cited a myriad of factors related to both enablers and barriers to the adoption of the systems. Participants’ expectations included the benefits already established by previous work: functional (comfort, convenience, security), instrumental (savings, sustainability, feedback) and the link between people and technology (keeping up with technology).

Energy-intensive activities at home are embedded in aspects of lifestyle (so-called ‘energy practices’), which are incredibly difficult to change. Participants mentioned a large number of challenges, even before they started using the technology. Householders described the diverse routines they had in place for activities like laundry, cooking or heating. These activities were determined by a range of factors, including what equipment was available, householder competencies, and what they thought was the most efficient way to achieve their objectives (eg. drying clothes with the windows open). Frequently, heating systems were used in a ‘set and forget’ fashion, and introducing smart controls disrupted this convenience and comfort.

A frequent issue raised by participants was the perceived difficulty of use. People are often prejudiced against new technologies because of past experience of unreliability and complexity. Householders were also unsure of the suitability of the technology to their environments. They were concerned about possible over-powering and complex technology replacing simple tasks like switching off radiator valves.

After one year of living with the systemsDuring the second round of interviews we asked if their expectations had been met, and if the challenges initially reported persisted. The abridged table opposite highlights our main findings. The comparison between the two phases throws light on expectations and barriers before and after introducing the technology. The first column shows the number of householders that mentioned each topic.

Key policy or industry implicationsAnalysis by theme indicated that the perceived challenges for smarter homes are greater than the motives for acquisition and use. In addition, several challenges persisted during the use of the technology. Design research and social science research can be incorporated into the engineering and technical sciences to help develop Smart Home technologies and improve their acceptance. A truly interdisciplinary development would improve the chances of a wider adoption of these technologies, and help to realise the benefits of these technologies to improve homes, lives and promote sustainable energy consumption.

We need to focus on how technology fits into people’s lives, and how people will use this technology. Household routines are often variable and unpredictable, which means that any system trying to control them needs to be flexible. Similarly, homes can be complex places, and we need to know more about how people interact with their homes.

Key implications for usersOne of the advantages of using Smart Home technology is that it can empower homeowners to devise their own solutions to improve energy efficiency. However, these benefits will not be realised if technologies are not designed to fit seamlessly into existing homes. Our participants were concerned about physical damage or compromised aesthetics caused by Smart Home technologies. Some participants’ homes had been changed or extended several times over the years. We also noticed complex integration between the built environment and existing technologies, compatibility issues with existing technologies, and the possibility of incremental smartness. This points to the need for systems that enable incremental

smartness to be introduced into people’s homes alongside existing technologies, and are flexible enough in design to be fitted seamlessly alongside the non-standard fixtures and fittings common to many UK homes.

Challenges, limitations and future researchInstalling three Smart Home systems during this research was a challenge in itself. Participants unsurprisingly reported that it would be better to have a single product, managed via a single interface. There would be fewer technical barriers if the systems were integrated in one product.

Participants in our study did not have to pay any of the costs of installing the Smart Home technologies. This meant that financial barriers were not mentioned as a challenge for adoption. Initial costs and payback periods are always a concern among those who have to fund their own systems. Installation often requires unexpected upgrades to existing hardware, such as incompatible or defective radiator valves, as well as the cost of devices and labour.

For more details on this research see: Oliveira, L., May, A., Mitchell, V., Coleman, M., Kane, T. and Firth, S.K. (2015). Pre-installation challenges: classifying barriers to the

introduction of Smart Home technology. Third International Conference on ICT for Sustainability - ICT4S 2015, Copenhagen, September 2015.


TOPIC 3

Most common expectations pre-installation Reality after a year’s use

Functional

13 Security – monitor intruders and occupants It worked more psychologically. Participants recognize that it’s not a proper security system, and will not deter intruders.

10 Control heating remotely Most participants with smartphones were happy with this feature, reporting using it on different occasions

9 Control room temperatures individually All participants reported achieving fine control of temperatures in rooms

Instrumental

9 Reduce bills [?] Difficult to attribute to the technology due to seasons, other simultane-ous improvements, or due to participants simply not monitoring expenses

Most common barriers pre-installation Reality after a year’s use

Hardware and software

15 House structure• Old radiators• Radiators close to the wall• Size or configuration of the house

Structural changes were financed by the project so not a concern Valves didn’t fit some radiators so a small number of participants had a

limited use of these controls Most of the other perceived barriers turned out to not be the case:

The systems were successfully fitted to different house configurations

10 Reliability, failure, errors• System unreliable, Failures

Most of the householders reported experiencing reliability issues including unforeseen technical problems

10 Simultaneous overlapping systems – new vs existing systems

The new systems entered as another layer, co-existing in the house The issue of being 3 different systems still concerned users

Acceptability and usability

9 Intrusiveness of the devices• Devices are big or ugly• Valves make noise

Aesthetically, this barrier reduced, as participants became accustomed to devices and ‘faded into the background’

The noise the valves make was reported negatively by most participants

12 Overpower users with complex technology - difficult to understand

Some users learnt to control the technology to a large extent But in some homes there is a clear distinction of who is able to control it

Domesticating technologies

10 How households currently manage the heating

New routines introduced, with advantages of fine tuning for comfort, savings, convenience

But some older users without mobile devices couldn’t access the benefits

HEATING PRACTICES IN SMART HOMES

Measured temperature and occupant settings for a single day in a one room, showing examples of wasted heating energy


WHAT WE DIDEnergy use for space heating has the greatest potential for energy saving in the home. This work aimed to better understand how occupants use their heating systems and identify opportunities for using smart heating controls to reduce energy use.

We recorded air temperatures and radiator surface temperatures in all rooms of the 20 homes every half hour for the duration of the project. New data analysis techniques were developed to gain insights into how occupants heat their homes and to identify times when energy is wasted.

We developed a systematic approach for calculating heating practices, defining how occupants interact with their heating controls on a daily basis to manage their heating requirements. We also calculated daily values for heating on and off times, thermostat settings and thermostatic radiator valve settings for the 20 homes over 18-months. The daily heating practices show how occupants change their settings day to day to accommodate their routine, maintain their comfort and minimise energy costs.

The smart thermostatic radiator valves installed during the project recorded changes to occupant settings and room temperatures in all 20 homes. By analysing this data for five homes, we identified three areas where Smart Home heating controls could be used to reduce energy demand while maintaining thermal comfort: heating system characteristics, identifying excessive heat loss, and unoccupied heating times.

Key scientific insights1. Identifying heating practices

We calculated metrics relating to how heating controls are used on a daily basis, identifying how they change over time and assessing how the changes link to external weather conditions. We found that timer settings changed most days, suggesting that occupants override their timer settings according to their daily routines (see chart below). Regression analysis showed that some of the variation in heating duration was related to the weather.

This work was a significant step forward in understanding how homes are heated during transition periods between summer, when little or no heating is used, and winter, when heating is used every day. Much less variation was observed in room thermostat settings, boiler supply temperature and radiator thermostat settings.’

2. Identifying potential energy savings

Recent advances in smart heating controls provide an opportunity to assess how closely room temperatures follow the temperatures set by occupants. Sensor data was used to identify periods when energy savings were possible, and to develop methods to achieve these energy savings – by incorporating this learning into advanced heating controls in the future.

The chart opposite shows a typical plot of the data collected in a single room on one day. The blue line shows the occupant temperature setting and the red line, the actual temperature measured in the room. In the morning the room temperature did not start to rise until after the start of the heating period set by occupants and it continued to rise to a higher temperature after the end of the heating period. In the evening heating period the measured temperature never reached the occupants’ setting. This

highlights three ways that improved controls could save energy:

• Adjusting for heating system characteristics – to minimise times when temperatures do not follow occupant settings (overshooting the set point, or lagging behind heating controls)

• Identifying times of excessive heat loss – such as leaving a window open in a room

• Identifying times of unoccupied heating – when the heating is in use but the room is unoccupied.

When is the heating on? Details for a single home recorded between 1 November 2013 to 28 February 2015

Key policy or industry implicationsThis work has identified opportunities for Smart Home technology developers to improve their heating controls. Work is continuing to develop algorithms which could be incorporated into future room-based heating controls, which will optimise the heat supplied to a room by maximising thermal comfort while reducing energy use.

As space heating is electrified over the coming years, insights into when heating is required and how this is related to the weather will be extremely valuable for electricity suppliers who have to manage peak demand. The daily heating on and off times calculated in this work could be of real value in this area.

Key implications for usersThis work has shown that users do not always control their heating systems in the most effective way and, therefore, waste energy and money. This is partly related to the Smart Home technologies which were trialled for this study, and one significant conclusion is that an effective Smart Home heating control must be able to control the boiler and zone controllers in one integrated system. It must also be intuitive to use and provide simple and effective ways to override timer settings without having to log onto a computer.

Challenges, limitations and future researchIdentifying heating practices using Smart Home sensor data is a challenge, as most sensors gather data at set fixed intervals. As this work was undertaken using data gathered every 30 minutes, it is not possible to identify heating on and off times with precision above half hour intervals. Future developments in Smart Home heating controls will lead to improved measurements and analysis becoming available. Smart boilers and heating controls will log settings and changes to settings automatically, and enable data to be downloaded by users.

Work is ongoing to quantify the potential energy savings at times when heating energy is wasted in the home. Algorithms will be developed which will enable future heating systems to learn the preferences of occupants, as well as the thermal properties of each room, and so optimise the heat energy supplied to each room.

For more details on this research see: Coleman, M., Kane, T., Dimitriou, V., Firth, S.K., Hassan, T. and Liao, J. (2015). Utilizing Smart Home data to support the reduction of

energy demand from space heating – insights from a UK field study. 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL), August

2015, Lucerne-Horw, Switzerland.


TOPIC 4

UNDERSTANDING APPLIANCE USE THROUGH DISAGGREGATION

Load disaggregation using our decision-tree based algorithm, for one household, which gives a breakdown of monthly electrical consumption by appliance.


WHAT WE DIDStrathclyde’s work focused on developing analytical tools for understanding household electricity consumption, patterns of appliance use, and energy feedback generation. We developed a reliable backend solution, with a database that enables simple and quick access to a variety of quantitative data. This allows simple queries to interrogate the remote, real-time energy monitoring.

We collected electricity data with an 8-10 second sampling rate for active power, similar to that provided by a Consumer Access Device that reads measurements from a Smart Meter directly. As well as total electricity use and appliance-specific consumption, we measure temperature, occupancy and light levels.

Our database has enabled us to test the robustness of several novel load disaggregation (non-intrusive appliance-level monitoring) algorithms. These were developed in a real, noisy setting where users go about their daily lives and the households contain many appliances unknown to the disaggregation module.

The Strathclyde team also investigated appliance load modelling to:

• inform appliance retrofit/upgrade decisions

• predict demand from appliances and households

• find opportunities for load shifting

• understand households’ daily routines

Key scientific insightsThis topic focused on developing ‘non-intrusive appliance load monitoring’ (NILM) algorithms suitable for low resolution energy data. Low resolution here means seconds or minutes. NILM refers to splitting total household energy use data down to energy use by individual appliances – applying software analytical tools on whole-house active power. Our novel algorithms offer alternatives to the more popular ‘Hidden Markov Model’ methods in the scientific literature, which usually require periods of

training with only one appliance running and become much more complex as the number of appliances increases. Our ‘supervised’ algorithms (requiring a labelled set of appliances for training), based on Decision Tree and Support Vector Machine are relatively simple, robust, and require short training periods. We found that they outperform the conventional method of accounting for energy use down to individual appliances. They do, however, require an appliance survey for training the model.

Unsupervised algorithms do not require a labelled set of appliances for training. We developed a novel approach based on Dynamic Time Warping capable of matching load signatures even when they are of different durations. This approach compares well against supervised algorithms, but the complexity (execution time and memory) is affected by the number of appliance signatures in the database.

Since training is often impractical, we created a third approach which requires no training. This exploits graph-based signal processing to set adaptive thresholds, with clustering and feature matching. This approach does not require prior knowledge of appliances, and instead the algorithm automatically learns appliance characteristics online. These are then used to perform disaggregation (appliance level monitoring).

The key to disaggregation is understanding appliances, their unique features and how they are used. So we also focused on ‘appliance mining’: modelling appliance-use patterns. Our kettle model, for instance, helped in estimating the water volume in kettles across the households purely from the power load data in our database. For example, we investigated overfilling and inefficient re-boiling in depth, and showed one of our participants how replacing their standard kettle with a ‘smart’ vacuum kettle had reduced their kettle-use electricity consumption from 17.2kWh to 12.8kWh per month. We also observed that across our study householders use their kettle in a well-established routine, and that kettle use makes up 3-10% of a households’ total electricity consumption. Similar trends emerge for other appliances.

Rose plots are useful for visualising hourly consumption across 24 hours, and for demand management and dynamic pricing.

Key policy or industry implicationsModelling and analysing appliance use patterns has demonstrated the potential for upgrading appliances, predicting appliance-specific electricity demand and the potential for load shifting. The work is also an enabler to understanding technology-driven practices in the home, by inferring activities associated with appliances, which is essential for sociologists and for time-use statistics. Using simple queries, our electrical load database can summarise a household’s electricity consumption, for any chosen period, and benchmark it against other households. We also analysed households’ energy use to assess the suitability of their utility tariffs. For example, only 40% of the households who were on the Economy 7 off-peak tariff were benefitting from this. The other 60% were paying more than if they were on a standard tariff. Utilities would benefit with improved customer satisfaction if they could provide this feedback to their customers.

Load disaggregation is seen as the next step towards providing effective energy feedback. Load disaggregation providers supply energy disaggregation through a combination of hardware sub-metering and software analysis. However, these solutions are currently limited to disaggregating high loads and industry is keen to adopt approaches that can operate at Smart Meter data rates, are practical, simple, accurate, and robust for a range of training periods. Our new algorithms are good candidates for future industry uptake. However, more testing is needed to bridge the gap between the scientific achievement and industrial standards.

Key implications for usersLoad disaggregation can improve users’ understanding of their appliance consumption, give more control over energy use (with the potential for energy reduction) and support householders’ decisions about Smart Home automation and load shifting. Combined with appliance modelling, load disaggregation can also identify malfunctioning appliances and those in need of an upgrade. Our study found that 70% of households would be willing to shift some of their daily routines to off-peak periods.

Challenges, limitations and future researchDisaggregation is still a challenge, especially as more appliances with similar signatures penetrate households. We could detect 44% to 82% of the total number of appliances in 10 REFIT households. Those we could not detect were mostly small loads. The first big barrier is technical: how to train models in an unknown house, when there is no appliance survey; how to increase accuracy with low-resolution energy data; and how to distinguish between many appliances with similar signatures.

The second significant barrier is user acceptance. Initial feedback showed that 70% of households would like to see their electricity bills itemised down to appliance consumption, but only 40% are willing to buy a Smart Meter with multiple plugs, let alone invest in a disaggregation service.

For more details on this research see: ‘Murray, D., Liao, J., Stankovic, L., Stankovic, V., Hauxwell-Baldwin, R.,Wilson, C., Coleman, M., Kane, T, and Firth, S.K. (2015).

A data management platform for personalised real-time energy feedback. 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting(EEDAL),

August 2015, Lucerne-Horw, Switzerland, August 2015.’Conference on Energy Efficiency in Domestic Appliances and Lighting(EEDAL), August 2015, Lucerne-Horw,

Switzerland, August 2015.’


TOPIC 5

Winter month-February

Monthly Consumer per Hour House 20 - January 2015

UNDERSTANDING ENERGY DEMAND THROUGH ACTIVITIES

These profiles show how much activities contribute to total monthly electricity consumption and the relative consumption of various activities.


WHAT WE DIDWe developed a novel method for making inferences about activities, involving five steps, and combining very different types of data.

First, household interviews, video ethnography (recording activities in subjects’ natural settings), and technology surveys are used to identify appliances and devices in the home, and their roles in specific activities.

Second, activity-technology mapping is carried out to establish the relationships between activities and technologies (appliances and devices) in the home. One or more technologies may point to certain activities being carried out.

Third, data from Smart Meters and energy plug monitors are collected. Smart Meter data measuring aggregate electricity use are disaggregated with an algorithm, as described in Topic 5, giving start and end times as well as energy consumption for each appliance used.

Fourth, our activity-recognition algorithm is used to infer time profiles of 10 everyday activities. That is, we identify when and for how long different activities occur, together with their energy use, where appropriate. The activities include washing, doing laundry, watching TV (reliably inferred), cleaning, socialising, working (inferred with uncertainties) and sleeping.

Fifth, activity-time diaries and structured interviews with the households are used to validate both the ontologies and the inferred activity-time profiles. This helps refine and improve the analysis.

Key scientific insightsA major challenge is how to interpret real-time energy data in terms of activities – as a first step towards providing meaningful activity-related energy feedback to households with Smart Meters. In simple terms, activities are what people do at home, eg. cooking, laundering, socialising and entertaining. Activities may be routine or irregular, may vary or stay consistent between week and weekend, and may involve one or all household members. Activities provide a valuable lens through which to interpret and provide feedback on household energy use because activities are:

• meaningful: households think about their own daily lives at home in terms of activities;

• salient: activities are noticeable, easy-to- recall features of domestic life;

• activities provide a comprehensive account of life at home matched closely to households’ lived experiences; and

• useful: activities are, by definition, actionable through decisions and behaviour that can potentially be altered.

The key scientific contribution of our work has been to demonstrate that this is feasible, reliable, and scalable. Our approach is novel in four respects.

• First, we make inferences about a comprehensive set of ten activities rather than a limited set of energy- intensive services.

• Second, we use qualitative data from household ethnography before making activity inferences, in order to map relationships between technologies and activities.

• Third, we use disaggregation algorithms to interpret energy consumption from a single Smart Meter reading with minimal alterations to other infrastructure.

• Fourth, we do not need to rely on additional sensor data (such as occupancy, light, temperature, humidity) to make our activity inferences.

Our approach has two important advantages. Using quantitative and qualitative data sources, improves the scope and robustness of the activities ontology. Household interviews, house and appliance surveys, and time diaries of both appliance use and activities, are all integral to the method.

The post-inference validation step improves confidence in both the activity-technology mapping and resulting inferences. The ontology can be revised and corrected before a final iteration of the inference routines on the same underlying data.

Key policy or industry implications1. Timely feedback: Our method generates time- and energy-use profiles for different activities, implying that the time-/energy-intensity of these activities can be readily compared. Currently, we have used our method retrospectively to make inferences about activities. Various steps require manual intervention for testing, checking and validation. However, once the activity-technology mapping and initial inferences are validated, the time- and energy-use inferences could be done in real time. This is attractive for service providers in the Smart Home market to provide value-added feedback to users, and we plan to develop this proposition further.

2. Research potential: Time at home is experienced in terms of activities. National time-use statistics show aggregate patterns collected in time diaries, but such diaries are expensive and error-prone. Our method provides an alternative for generating daily time-use profiles of a subset of energy-using activities, which can then be compared with national time-use statistics to identify variability, or to segment households.

3. Improving home energy management systems: Automatic activity recognition is an important enabler of home automation and effective home energy management systems. With large-scale roll-outs of Smart Meters, identifying domestic activities using Smart Meter data is very attractive as it does not require any additional sensors, using only available data collected for energy monitoring and billing.

Key implications for usersEnergy consumption can be broken down and linked to domestic activities to enable activity-itemised energy feedback. This is a more meaningful and informative approach to feedback than conventional energy or cost-based methods. Household users can find out and learn:

• When and for how long do activities occur each day? How do they vary daily?

• How consistent are the occurrences and durations of activities over time?

• How does understanding activities help energy management? Which activities are the high energy users?

Challenges, limitations and future research

This work faced numerous challenges, with three significant stumbling blocks:

1. Using mixed methods, as well as quantitative and qualitative data, requires a multi-disciplinary research team with frequent interactions both within the team and between the team and households. This had implications for researcher time and skills, and incentives to support households’ ongoing commitment to the research.

2. The household interviews and time diaries were hard to connect to the real-time data, because energy profiles are inevitably restricted to energy-using activities. ICT-related activities were also uncertain because any given device could be used for many different activities. These uncertainties are further exacerbated with mobile, battery-powered devices such as smart phones, tablets, laptops.

3. Neither heating nor lighting are activities as such, but as energy-intensive services they cannot be omitted from activity-centric accounts of everyday life generated by real-time energy data. It is difficult to apportion energy used for heating and lighting to the activities they enable. Including heating and lighting as separate energy services in addition to the set of inferred activities was the simplest way to avoid missing energy data.

Our goal for future work is to develop a scalable method for the mass market to accompany the Smart Meter rollout. We plan to develop self-completion instruments that are less resource intensive and intrusive, including:

• activity-based questionnaires that can be administered remotely or as part of a Smart Meter installation,

• home surveys completed by households or carried out by Smart Meter installers with the households’ consent

• information on appliance-use patterns generated by self-report questionnaires

• self-validation of the activity inferences, either in close to real-time, or by post-processing inference data

For more details on this research see: Stankovic, L., Wilson, C., Liao, J., Stankovic, V., Hauxwell-Baldwin, R., Murray, D. and., Coleman, M. (2015). Understanding domestic

appliance use through their linkages to common activities. 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL), August 2015,

Lucerne-Horw, Switzerland, August 2015.


TOPIC 6

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RETROFIT ADVICE AND THE REFIT HOME ENERGY REPORT


WHAT WE DIDWe used a multi-stage co-design process to explore retrofit advice and the design of home energy reports. Each household had three home visits from the project design team, and took part in a range of activities relating to energy, smart data and retrofit decision-making.

The first visit involved a Green Deal assessment for each household and a discussion of the process and resulting retrofit advice. The following two visits captured feedback on iterations of the personalised REFIT Home Energy Report. The first iteration was based on energy data; and the second, on adding appliance-level disaggregation, comparisons to similar REFIT households, inside and outside temperatures, and occupancy readings. The personalised energy reports were based on the data logged by the project over a one-year period.

We explored each household’s retrofit intentions using a card sorting activity. After every iteration of advice, each household sorted 42 retrofit energy saving measures into 10 pre-defined categories, giving reasons for each intention. Data collected during these visits included audio recordings (later transcribed), confidence ratings, card-sort outcomes and photographs.

Key scientific insightsInterest in energy consumption information

Householders were keen to discuss their energy use - encouraged by a participative process, visually engaging materials, data being presented to them that showed their patterns of use, and simple graphics that conveyed key concepts rather than multiple levels of detail. Householders were unaware of the relative costs of electricity and gas, and quite surprised that the per unit energy costs of electricity were much more than those of gas. It became apparent to them that heating a single room with a fan heater was not necessarily much cheaper than turning on the whole house gas central heating. As found previously, householders had little understanding of what a KWh is, but were interested in the individual running costs of appliances, and the baseline electricity load.

The patterns of use of energy over time were only meaningful to households when they could be put into context. Additional information was needed such as occupancy, outdoor temperatures, and behaviour. For example, being out all day affects or could affect energy use. Trends over time were interesting, particularly if they could be related to key changes that had taken place. Comparisons of their use with other similar households were useful, but these had to include both type of house and nature of the household, to ensure they were similar.

There was also interest in simple ‘what if’ scenarios. These could include cost comparisons for using appliances and heating differently, eg. running the dishwasher and washing machine overnight on a dual tariff, cost of heating a single room with a fan heater vs. having the gas central heating on, costs of higher or lower thermostat temperatures, or the impact of using a wood burner.

Impacts on households’ decision-making plansAlthough there was interest in personalised energy consumption information (see above), there was only limited impact on energy-related retrofit intentions. It was clear that simply providing more information to householders (even in a highly engaging manner) did not prompt all households to consider retrofit. Even for energy-aware households, who are committed to reducing energy use, there are a range of barriers which limit the energy-related retrofit

that they are willing to undertake. These include: upfront cost and cost/payback times, disruption (eg. underfloor heating and insulation), lack of understanding (eg. with microheat generation and ground source heat pump), and measures which would be difficult to apply to their particular house.

Where changes in energy-related retrofit plans did occur, this came about through the increased awareness that the householders gained. For example, one householder installed solar PV during the course of the project – they had initially been certain that it was too expensive, based on the Green Deal figures, but were prompted by a mailshot to obtain more accurate costs.

The detailed personalised information provided to the homeowners had a mixed impact in terms of confidence. In some cases it served to increase homeowner confidence in the actions they had already taken or plans they were making. An example here is the decision to replace appliances or boilers with more energy-efficient ones only when the existing one broke. In other cases, detailed information prompted questions from the householders about what the data was showing, apparent anomalies in the data, and what these actually meant.

Key policy or industry implicationsHouseholders need simple messages which are meaningful, and integrate data from various sources. For example, showing when the heating comes on combined with occupancy data, or looking at trends over time, or comparisons with an equivalent household.

It is crucial to tap into ‘windows of opportunity’ or ‘trigger points’ to overcome barriers to energy-related retrofit. For example, piggybacking energy efficiency retrofit onto other renovation works (eg. underfloor insulation before floors are laid); being ready to replace appliances with more energy-efficient ones, rather than having to make rushed purchases; or encouraging householders to think about energy efficiency when moving home.

The government also needs to consider the differing requirements of cost vs. carbon savings, and how householder requirements relate to global carbon reduction targets. Retrofit decisions are not a ‘one-off’ or instant activity – rather they take place over an extended period of time. Householders therefore need support for long term awareness-raising and learning.

Key implications for usersFrom a householder perspective, the key implication is that they want simple information that conveys the meaning that they are looking for. Rather than providing more detail with a narrow set of data, users want data brought together so they can identify the reasons for their energy consumption, plus simple messages that guide future actions. For example, providing households with an insight into how much energy is being used to heat their home when it is not occupied.

As many users would not be interested in detailed analysis, a ‘dashboard’ type presentation would raise awareness of the breadth of information available. Those who are interested could drill down into the data.

If ‘smartness’ is incorporated into energy efficient measures, then householders would value being told when ‘something is wrong’ or where exceptions are occurring, rather than being given a constant flow of information. Householders generally want to know when to act rather than having to process additional information.

Challenges, limitations and future researchThis part of the research faced a number of challenges. Although recruited based on demographics and levels of technological awareness, the REFIT households were inherently more engaged in energy saving than most households. This limited the scope for acting on retrofit advice, since several energy-related retrofit measures had already been taken by the householders.

A key feature was to work personally with all of the households to help them understand their energy use and best courses of action. They often expressed desires for complex combinations of data (eg. cost-based experiments comparing differing types of heating methods) that would be very time consuming to process and generate on an individual, personalised basis. The team did not always have the time to process the data to explain all patterns of use and

apparent anomalies.

Although this type of research approach is very time consuming, involving multiple home visits to collect vast amounts of both quantitative and qualitative data, it does provide rich insights. To build on these insights, future research could:

• Look at the impact of energy data on householders’ retrofit plans over time.

• Develop and test clear indicators such as ‘comfort’ and cost, instead of kWh and temperatures.

• Integrate different types of Smart Meter and Smart Home data streams to explain why energy use varies over time.

• Apply creative, participative co-design approaches to improve support for retrofit decision making.

• Employ similar participative principles to develop more meaningful energy audits, which will increase engagement by households.

• Understand the needs for energy related services, and the opportunities provided by wide-scale Smart Meter rollout.

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For more details on this research see: Kane, T., Cockbill, S., May, A., Mitchell, V., Wilson, C., Dimitriou, V., Liao, J., Murray, D., Stankovic, L., Stankovic, V., Fouchal, F., Hassan,

T.M. and Firth, S.K. (2015). Supporting retrofit decisions using Smart Meter data: a multi-disciplinary approach. Proceedings of the European Council for an Energy Efficient

Economy (ECEEE) 2015 Summer Study, Toulon/Hyères, France. June 2015.


TOPIC 7

Smart Home data opens up new opportunities for helping householders to understand energy use

EVALUATING ENERGY SAVINGS USING SMART HOME DATA


WHAT WE DIDData collected by Smart Home sensors presents an opportunity for a step change in how we understand energy use at home. Currently, steady state modelling techniques are commonly used to rate the energy performance of homes and assess the potential for energy savings from measures like cavity wall insulation.

This work aimed to develop new techniques to evaluate the energy savings resulting from energy efficiency upgrade in dwellings using Smart Home and Smart Meter data.

We identified seven steps that are needed to accurately evaluate energy savings using Smart Home data. We are now developing and assessing the benefits of three different modelling methods:

• multiple regression modelling – using statistical tools to look at the effect of changing different factors at the same time

• steady state energy modelling – where some parameters are assumed to be fixed

• lumped parameter modelling – where the world is simplified to a number of entities that approximate to more complex systems

Key scientific insightsCurrently no standard method exists to evaluate energy efficiency measures in homes. Usually, an initial prediction of potential energy savings resulting from energy efficiency measures is made using a steady state modelling technique such as the UK’s Standard Assessment Procedure (SAP). This uses basic assumptions about the heat loss from the home and how occupants use their heating, and the initial prediction is prone to error. Often little or no post-installation monitoring takes place, so the success of energy efficiency installations is rarely evaluated.

As Smart Home and Smart Meter data become more widely available, we will have the chance to evaluate energy savings at scale. However, this is no simple task as the external weather conditions during the baseline and post-installation periods will differ. Also installing energy efficiency measures has been shown to change occupant behaviour, which again invalidates any simple comparison. This work aims to develop a method to evaluate energy savings based on Smart Home data collected in 20 homes.

In order to evaluate the success of an energy efficiency installation, we need a prediction of what the energy consumption would have been with no intervention. The image below illustrates some of the challenges. Energy consumption is monitored in a dwelling before an energy efficiency measure is installed (Step 1). An energy model is constructed for the baseline period (Step 2), which can be calibrated using the measured data (Step 3). The model is then used to predict future energy use following an energy saving intervention (Step 4) and to predict future energy use assuming that the intervention has not taken place (Step 5). Collecting data following an actual intervention (Step 6) will enable actual energy savings to be calculated and compared with earlier predictions. Finally, further developments to the technique are needed to account for changes in occupant behaviour and to improve initial modelling assumptions (Step 7).

For more details on this research see: Firth S.K., Fouchal, F., Kane, T., Dimitriou, V. and Hassan, T. (2013). Decision support systems for domestic retrofit provision using

Smart Home data streams. Proceedings of CIB W78 2013: Move towards Smart Buildings, Infrastructure and Cities, Beijing, China, October 2013; and Dimitriou, V., Firth, S.K.,

Hassan, T., Kane, T. and Fouchal, F. (2014). Developing suitable models for domestic buildings with Smart Home controls. Proceedings of Building Simulation and Optimisation

2014, UCL, London, 23-24 June 2014


Evaluating an energy-saving intervention with a simple regression model

Key implications for usersCurrently installers or manufacturers of energy efficiency products have no robust method for claiming actual ‘in use’ energy savings. Consequently estimated payback times are inaccurate, which reduces consumer confidence in energy efficiency measures. Our planned modelling technique will enable projects to be tested fully and allow successful promotion of high quality products. It will also ensure that installers do a good job - as it will allow householders to quickly identify when energy improvements are underperforming.

Key implications for usersIn the chart above, with a single energy-efficiency upgrade, we found that the intervention saved nearly 20% of heating energy use. However, using a simple single-term model it is impossible to know how much of this saving is related to other variables, like heating durations or thermostat settings. Further work is being carried out to determine whether heating practices have changed and if improvements to the initial model assumptions are required for a robust method.

Challenges, limitations and future researchThe regression models used in this research rely on a very detailed dataset. High-resolution room temperature, radiator surface temperature, energy consumption and external weather conditions are all required. Gathering data this detailed is extremely time-consuming and expensive, so it is hard to do for a large dataset. This means it is only possible to assess the success of the models in the case study homes – a much larger study would be required to assess statistical significance and identify trends between homes with similar characteristics.

All models use assumptions and some systematic error is inevitable, so we are now working to quantify the error related to the use of our new models. Further work is required using a much larger sample of homes, with data collected before and after installing energy efficiency measures, so we can fully assess the potential for regression-based models. The process involved in making these seven steps is relatively complex, and the modelling method we used needs to be adapted using data measured in the home.

TOPIC 8

1 Monitor base line Smart Home data – Gas and electricity consumption, room temperatures etc.

2 Predict energy use for baseline period

3 Amend model to align with measured data

4 Predict energy use for a period after energy efficiency improvement

5 Predict energy use without improvements to calculate expected energy savings

6 Continue monitoring to assess the actual energy savings and the success of installation

7 Develop modelling technique to account for changes to occupant behaviour and improve initial modelling assumptions

FUTURE SERVICE PROVISION


WHAT WE DIDThis work aimed to develop a range of energy-based service propositions by working creatively and iteratively with householders with smart technologies, including energy metering, installed in their homes.

The first stage was an assessment of the Green Deal from a service-design perspective, focusing on the user experience and the Green Deal’s effectiveness for supporting energy-related retrofit decisions.

A series of householder visits were then undertaken, where the data from the Smart Home were discussed with the homeowners. This helped them understand their energy usage, and their energy-related behaviour.

We then worked through a creative process with the homeowners, using a series of cards that represented the data that could be collected in a ‘smart’ home (eg. gas, electricity consumption, occupancy, room and external temperatures, etc). Householders considered individual data sources, and combinations of sources, to identify ‘things they could be told’ about their house and ‘things that could be done for them’. Mock-ups were used to prompt discussion of how you might interact with a service, and different ‘lenses’ were used to enable consideration from an individual, household or wider-community perspective.

As a final stage, we did workshops with technology specialists and a broad household group to refine the ideas.

Key scientific insightsIntroducing Smart Meters and other in-house technologies generates a vast amount of data that can be used to provide a range of services to households. In particular, integrating different types of data, and data from different households, provides insights into understanding how households use energy. This understanding can impact on energy use, and give insights about how energy supply and management might be different in the future.

Two key sets of findings came out of this work. First, a set of ten service propositions that are based on Smart Home data:

1 Appliance Performance Monitoring - Using disaggregated electricity consumption to identify when individual appliances are failing or becoming less efficient.

2 Safety and Security Alerting - Using various sensors in and around the home to monitor and alert when safety and/or security is being breached.

3 Optimising Design of Heating Systems - Using temperature and energy sensors to analyse heating performance and recommend design changes to heating systems.

4 Optimising Control of Heating Systems - This service would use sensors such as room temperature, occupancy, and activity levels, to identify how well heating system control is set up, and how it can be optimised to balance comfort and cost.

5 Understanding Appliance Consumption - Detailed monitoring of consumption by appliances, and comparing this to similar households, to highlight where consumption is ‘normal’, and where easy cost savings should be possible.

6 Community-based Services - Sharing information on energy consumption within a local community, in order to help members of the community (eg. vulnerable isolated individuals) to learn from collective experience, and promote bulk energy buying or generation in a community.

7 Utility Cost Comparison - Detailed monitoring of energy usage and direct comparison of actual costs of various energy suppliers, leading to recommendations or automatic and dynamic switching of energy supplier.

8 Training and Awareness Raising - To target three key groups to change attitudes: older households who may have time and motivation to consider energy usage and their lifestyles, younger adults who are just starting to live independently, and school children to instil early messages. Capitalise on windows of opportunity to bring about changes in energy-related behaviour.

9 Energy supply and demand management - This would help householders manage their energy supply and demand by taking into account the times of day, or days of the week where energy is cheap or free (eg. through micro-generation) and helping to schedule appliance use accordingly.

10 Green Deal ‘Plus’ - To build on the advice that was given through the Green Deal, by incorporating an energy audit aspect – with a householder-consultation process – to provide more contextually relevant advice to the householder.

The second set of results related to using creative, participative processes (often called ‘co-design’) with householders to help them understand complex and intangible problems and assist in the design of possible solutions. The unique aspect of this study was collecting personal energy usage and other data, and then working with homeowners to determine how this data can be transformed and presented to them in a way that enables them to understand it. Several things contributed to the success of this work:

• Using priming materials to help people think about and convey what their homes mean to them, how they aspire to live and what they would like to improve

• Continually and actively involving the households in our design process and keeping them immersed in the complex and intangible problem space so they were regularly thinking about key issues and possible solutions

• Viewing the householders as ‘experts of their own lived experiences’ - experiences that we must tap into if we are to design better retrofit advice and future services

• Using a range of creative visual prompts and activities to help people grasp and understand intangible or complex concepts

• Bringing different householders with different experiences together with technology specialists, to help us design and develop service concepts


A creative process helps homeowners generate future service propositions

Key policy or industry implicationsWork to develop services for householders needs to involve the householders. We need to ‘design with them’, rather than ‘for them’. Co-design techniques are very useful. Developing services solely from a perspective of what is technically feasible is unlikely to result in services that meet the needs of users, and provide them with new possibilities.

Key implications for usersAbove all, any services developed for users need to be conceptually simple, and not based around providing more and more detailed information. The ‘value’ to the individual is key – rather than just providing more convenience, any service should provide new possibilities that were previously not available.

Challenges, limitations and future researchMaking sense of the wealth of data available from Smart Homes and using it to develop viable services will require input from multiple stakeholders and a lot of work. The emphasis needs to be on creating ‘meaning’ for individuals or groups, and this is challenging due to the variety of personal perspectives. What is important to one household may be unimportant or not relevant to another. Personalisation is key. New services must also be viable, and hence new business models are required to enable success.

TOPIC 9


Published scientific papers

Altrabalsi, H., Liao, J., Stankovic, L. and Stankovic, V. (2014). A low-complexity energy disaggregation method: Performance and robustness. IEEE Symposium Series on Computational Intelligence (SSCI) Applications in Smart Grid, Orlando, FL, December 2014.

Badiei, A., Firth, S.K. and Fouchal, F. (2014). The role of Programmable TRVs for Space Heating Energy Demand Reduction in UK Homes. Proceedings of Building Simulation and Optimisation 2014, UCL, London, June 2014.

Coleman, M., Kane, T., Dimitriou, V., Firth, S.K., Hassan, T. and Liao, J. (2015). Utilizing Smart Home data to support the reduction of energy demand from space heating – insights from a UK field study. 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL), Lucerne-Horw, Switzerland, August 2015.

Dimitriou, V., Firth, S.K., Hassan, T., Kane, T. and Fouchal, F. (2014). Developing suitable models for domestic buildings with Smart Home controls. Proceedings of Building Simulation and Optimisation 2014, UCL, London, 23-24 June 2014.

Dimitriou, V., Firth, S.K., Hassan, T.M., Kane, T. and Coleman, M. (2015). Data-driven simple thermal models: The importance of the parameter estimates. Proceedings of the International Building Physics Conference (IBPC2015), Turin, Italy, June 2015.

Elafoudi, G., Stankovic, L. and Stankovic, V. (2014). Power disaggregation of domestic Smart Meter readings using Dynamic Time Warping. ISCCSP-2014 IEEE International Symposium on Communications, Control, and Signal Processing, Athens, Greece, May 2014.

Firth S.K., Fouchal, F., Kane, T., Dimitriou, V. and Hassan, T. (2013). Decision support systems for domestic retrofit provision using Smart Home data streams. Proceedings of CIB W78 2013: Move towards Smart Buildings, Infrastructure and Cities, Beijing, China, October 2013

Hargreaves, T., Wilson, C. and Hauxwell-Baldwin, R. (2013). Who uses Smart Homes? Representations of users by the Smart Home industry. European Society for Ecological Economics (ESEE) Annual Conference. Lille, France, June 2013.

Hargreaves, T., Wilson, C. and Hauxwell-Baldwin, R. (2013). Who uses Smart Home technologies? Representations of users by the Smart Home industry. Paper presented at the European Council for an Energy Efficient Economy (ECEEE) 2013 Summer Study, Toulon/Hyères, France, June 2013.

Hargreaves, T., Hauxwell-Baldwin, R., Coleman, M., Wilson, C., Stankovic, L., Stankovic, V., Liao, J., Kane, T., Hassan, T. and Firth, S. K. (2015). Smart Homes, control and energy management: How do Smart Home technologies influence control over energy use and domestic life? Proceedings of the European Council for an Energy Efficient Economy (ECEEE) 2015 Summer Study, Toulon/Hyères, France, June 2015.

Hauxwell-Baldwin, R., Hargreaves, T., and Wilson, C. (2013). Smart Homes for smart practices? Using technology biographies to understand how Smart Home technologies influence social practices. Paper presented at the Annual Conference of the Royal Geographical Society and Institute of British Geographers (RGS-IBS). London, UK, August 2013.

Kane, T., Cockbill, S., May, A., Mitchell, V., Wilson, C., Dimitriou, V., Liao, J., Murray, D., Stankovic, L., Stankovic, V., Fouchal, F., Hassan, T.M. and Firth, S.K. (2015). Supporting retrofit decisions using Smart Meter data: a multi-disciplinary approach. Proceedings of the European Council for an Energy Efficient Economy (ECEEE) 2015 Summer Study, Toulon/Hyères, France. June 2015.

Liao, J., Stankovic, L. and Stankovic, V., Elafoudi, G. (2014). Power disaggregation for low-sampling rate data. 2nd International Non-intrusive Appliance Load Monitoring Workshop, Austin, TX, June 2014.

Liao, J., Elafoudi, G., Stankovic, L. and Stankovic, V. (2014). Non-intrusive appliance load monitoring using low-resolution Smart Meter data. IEEE International Conference on Smart Grid Communications, Venice, Italy, November 2014.

Liao, J., Stankovic, L. and Stankovic, V., (2014). Detecting household activity patterns from Smart Meter data. IE-2014 10th IEEE International Conference on Intelligent Environments, Shanghai, China, July 2014.

Murray, D., Liao, J., Stankovic, L. and Stankovic, V. (2015). How to make efficient use of kettles: Understanding usage patterns and suggestions for new models. 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL), August 2015, Lucerne-Horw, Switzerland, August 2015.

Murray, D., Liao, J., Stankovic, L., Stankovic, V., Hauxwell-Baldwin, R., Wilson, C., Coleman, M., Kane, T, and Firth, S.K. (2015). A data management platform for personalised real-time energy feedback. 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL), August 2015, Lucerne-Horw, Switzerland, August 2015.

Oliveira, L., May, A., Mitchell, V., Coleman, M., Kane, T. and Firth, S.K. (2015). Pre-installation challenges: classifying barriers to the introduction of Smart Home technology. Third International Conference on ICT for Sustainability - ICT4S 2015, Copenhagen, September 2015

Seeam, A., Liao, J., Stankovic, L. and Stankovic, V. (2013). Improving Energy Efficiency with Smart Home Appliance Monitoring, Proceedings of EEDAL’13, Coimbra, Portugal, September 2013.

Stankovic, V., Liao, J. and Stankovic, L. (2014). A graph-based signal processing approach for low-rate energy disaggregation. IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, December 2014.

Stankovic, L., Wilson, C., Liao, J., Stankovic, V., Hauxwell-Baldwin, R., Murray, D. and., Coleman, M. (2015). Understanding domestic appliance use through their linkages to common activities. 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL), August 2015, Lucerne-Horw, Switzerland, August 2015.

Wilson, C., Hargreaves, T. and Hauxwell-Baldwin, R. (2015). Smart Homes and their users: a systematic analysis and key challenges. Personal and Ubiquitous Computing, Volume 19, Issue 2, Page 463-476.

Wilson, C., Stankovic, L., Stankovic, V., Liao, J., Coleman, M., Hauxwell-Baldwin, R., Kane, T., Hassan, T. and Firth, S. K. (2015). Identifying the time profile of everyday activities in the home using Smart Meter data. Proceedings of the European Council for an Energy Efficient Economy (ECEEE) 2015 Summer Study, Toulon/Hyères, France. June 2015.

Zhao, B., Stankovic, V. and Stankovic, L. (2015). Blind Non-intrusive Appliance Load Monitoring using Graph-based Signal Processing. IEEE Global Conference on Signal and Information Processing (GlobalSIP 2015): Symposium on Signal Processing Applications in Smart Buildings, Orlando, FL, USA, December 2015.

Other publications:

Wilson, C., Hargreaves, T., and Hauxwell-Baldwin, R. (2013). Using Smart Homes: themes, linkages and disconnects in research on Smart Homes and their users. Joint Science, Society and Sustainability (3S) Research Group and Tyndall Centre Working Paper: University of East Anglia, Norwich, UK. 3S Working Paper Series number 2013-23.

LIST OF REFIT OUTPUTS TO DATEFor the latest publication list please visit: www.refitsmarthomes.org

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SMART HOMES AND SAVING ENERGY...4 I SMART HOMES AND SAVING ENERGY SMART HOMES AND SAVING ENERGY I 5 EXECUTIVE SUMMARY The REFIT project explored how householders use and interact with