The REFIT project investigated the use of smart home technologies and their potential impact on household energy demand. As part of the REFIT project, a national survey was conducted to measure perceptions of smart homes. The survey instrument was developed and tested by the project team. The survey was implemented online during September - October 2015 by a market research company using a representative sample of UK homeowners. A total of 1054 responses were collected. The survey responses were coded and cleaned by the project team. Both survey instrument and cleaned response data are made available here. The REFIT project additionally ran a field trial of smart home technologies involving twenty households in Loughborough, UK, over a two year period from October 2013 - October 2015. Detailed qualitative data on participating households’ perceptions of smart home technologies are available separately (see Related resources - Related data collections). These data were collected as part of the REFIT project (`Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology’). The REFIT project ran from 2012 - 2015 as a consortium of three universities - Loughborough, Strathclyde and East Anglia - and ten industry stakeholders. The REFIT project was funded by the Engineering and Physical Sciences Research Council (EPSRC) through linked grants under the Transforming Energy Demand in Buildings through Digital Innovation (BuildTEDDI) funding programme. This dataset was collected under Grant Reference EP/K002430/1 to the University of East Anglia. Other linked grants to consortium members includes EP/K002457/1 (Loughborough) and EP/K002368/1 (Strathcldye). Further details on the REFIT project and related publications can be found at: http://www.refitsmarthomes.orgThermal efficiency retrofit options, appliance upgrades and on-site renewables represent a significant opportunity to deliver energy demand reductions to UK homes. The potential to reduce thermal heat losses through insulation and airtightness (in particular in pre-1980s housing), upgrade the household appliance stock (using the latest energy saving models) and integrated on-site renewables and microgeneration (developing a 'prosumer' culture and reducing energy bills) still remains largely unrealised. There are a number of challenges in providing advice for retrofit solutions to consumers which will promote behaviour change and influence purchasing decisions. Currently consumer information is based on standardised methodologies for nominal house types and the resulting predictions of energy savings have minimal resemblance to reality where the thermal efficiency of the dwelling, efficiency of heating system and appliances, occupancy, user behaviour and preferences will have a significant impact on the effectiveness and uptake of retrofit measures. One solution is to provide consumers with personalised, accurate and trustworthy predictions of energy saving measures which are calibrated and tailored to their dwelling and living patterns, presented in a format to engage and promote action. This proposal will facilitate a widespread uptake of retrofit measures in UK homes by implementing a holistic approach to providing consumers with personalised, tailored retrofit advice delivered using methods to maximise consumer engagement. Smart Home technology provides a unique opportunity to use real-time measurements, advanced data analytics, digital signal processing and communications techniques, novel visualisation, semantic web and cloud computing technologies to generate advice at different levels of abstraction for informed and justified decision making. The Smart Home concept is currently gaining significant momentum and new developments in open systems, simple use and installation features (ie plug and play), mobile access (ie Smart Phones) and connectivity have brought the concept to the attention of energy companies, ICT companies and appliance manufacturers. The IBM vision of a Smart(er) Home gives three characteristics: 1) Instrumented (sensors and automation of household activities); 2) Interconnected (communication between devices and wider networks - allowing remote access and control of devices); and 3) Intelligent ('the ability to make decisions based on data, leading to better outcomes'). Smart Homes provide consumers with more control over their homes and energy systems and, importantly, how their energy demand and costs can be reduced through interventions. This project brings together a multi-disciplinary team of building, ICT, energy, design and user experts to develop a personalised decision support platform for building envelope retrofits, heating system and appliance replacement purchases, and on-site renewables integration. This will deliver a step-change in the provision and accuracy of retrofit advice to UK householders leading to a low-energy and low-carbon future housing stock. The outcomes will be of benefit to: energy, ICT, embedded systems and telecommunication companies developing technology and business models for Smart Home services; consumers to lower their energy bills and improve the safety, security and comfort of their homes; building component, boiler and appliance manufacturers developing the next generation of low-energy products; and policy makers for new insights into innovative approaches to meeting the security, affordability and carbon reduction aspirations of the UK energy system.

The survey instrument was developed by the research team to measure: (A) general perceptions of the purpose, benefits and risks of smart home technologies, and general issues of consumer confidence; (B) perceived attributes of smart home technologies including how they are designed, how they are controlled, and the domestic activities for which they are most relevant. The survey instrument was structured in two parts. Part one contained socio-demographic questions and basic questions on smart home awareness used to screen respondents (see below). Part two contained detailed questions measuring smart home perceptions on a 5 point Likert scale (with 5 = strongly agree). All questions were developed, iteratively tested and refined for clarity and comprehensibility prior to implementation. The survey was implemented online by a market research company, SSI, using a respondent panel representative of the UK homeowner population. Survey responses were collected from 18 September - 14 October 2015. Screening questions were included to ensure that survey respondents (i) have at least a vague idea of what smart home technologies are, (ii) own their own home (with or without a mortgage), (iii) are >18 years old. A total of n=125 respondents were screened out for not meeting all three of these criteria. Quality checks were run to identify possible respondents with low cognitive engagement in the survey questions. Quality checks included: (i) straight line responses on blocks of questions; (ii) inappropriate or irrelevant open-ended responses revealing a lack of understanding of questions; (iii) contradictory responses on identical but inversely-framed questions; (iv) unrealistically fast survey completion times. A total of n=593 respondents were filtered out for failing two or more of these quality checks. The final sample comprised n=1025 respondents. The average survey completion was just under 7 minutes.