Description: Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology

PI: Dr Steven Firth, Loughborough University

Fund: £1.5m BuildTEDDI

Project lifespan: May 2012 to April 2015




REFIT was an ambitious, highly interdisciplinary research project with the long-term aim of creating a step-change in uptake rates of retrofit technology measures in UK homes. The project brought together a diverse research team with internationally renowned expertise in buildings, energy, ICT, people and design. REFIT studied the Smart Home concept and its ability to provide personalised, valued, tailored and trustworthy information on building retrofit, energy efficiency and on-site renewable technology options for UK homes.


The REFIT project comprised five Work Packages (WPs). Each Work Package focused on a particular aspect of the Smart Home concept. Much of the new empirical data generated and analysed by the REFIT project was from a field trial of 20 smart homes, carried out as part of WP 2.

WP 1: Data analytic tools for Smart Home data streams; WP 2: Smart Home Field Trial; WP 3: User engagement with smart home technologies; WP 4: Definition of value propositions and user engagement; WP 5: Integration of results and retrofit decision support tool development.


Based on the field trial of 20 smart homes, the REFIT project developed a range of approaches and tools for analysing disaggregated electricity consumption, for modelling the thermal performance of buildings, for understanding how smart home technologies are domesticated, and for using smart home data and insights to engage households in energy-efficient retrofits.

Findings from the REFIT project have been published in a series of journal articles and conference papers, and provide answers to the following questions:

On disaggregation and information processing:

• How can signal information processing turn low resolution smart meter data into meaningful information?

• Can we develop consistently accurate and practical non-intrusive disaggregation solutions from low-resolution smart meter one-dimensional data?

On time-use and energy intensity of domestic activities:

• How much of a household's total electricity consumption is accounted for by activities such as cooking, and energy services like refrigeration?

• Can we generalise when energy-intensive activities occur within households of similar occupancy?

• Which are the largest energy-consuming activities, and what are the implications for demand management and feedback?

On the adoption and use of smart home technologies:

• How and why do households adopt, try out, integrate, or reject smart home technologies?

• How do smart home technologies affect the control that households have over their domestic environment and their everyday lives?

On public perceptions of the benefits and risks of smart home technologies:

• What are the main perceived benefits and risks of smart home technologies among UK homeowners?

• To what extent is industry marketing and development of smart home technologies consistent with homeowners' expectations?

Key outputs

T. Hargreaves, C. Wilson and R. Hauxwell-Baldwin (2017). "Learning to live in a smart home." Building Research & Information, 2017. DOI: 10.1080/09613218.2017.1286882

C. Wilson, T. Hargreaves and R. Hauxwell-Baldwin (2017). "Benefits and risks of smart home technologies." Energy Policy, 2017, pp. 72-83. DOI: 10.1016/j.enpol.2016.12.047 (and also reported in Nature Energy at doi:10.1038/nenergy.2017.13)

L. Stankovic, V. Stankovic, J. Liao and C. Wilson (2016). “Measuring the energy intensity of domestic activities from smart meter data,” Applied Energy, 2016, pp. 565-1580. DOI: 10.1016/j.apenergy.2016.09.087

K. He, L. Stankovic, J. Liao and V. Stankovic (2016). “Non-intrusive load disaggregation using graph signal processing,” IEEE Transactions on Smart Grid, 2016, DOI: 10.1109/TSG.2016.2598872

D. Murray, J. Liao, L. Stankovic, and V. Stankovic (2016). "Understanding usage patterns of electric kettle and energy saving potential", Applied Energy, 2016, pp. 231-242. DOI:10.1016/j.apenergy.2016.03.038

B. Zhao, L. Stankovic and V. Stankovic (2016). "On a training-less solution for on-intrusive appliance load monitoring using graph signal processing", IEEE Access, 2016, pp. 1784-1799. DOI:10.1109/ACCESS.2016.2557460

C. Wilson, T. Hargreaves and R. Hauxwell-Baldwin (2016). "Perceptions of Smart Homes: A Market Survey", BEHAVE 2016: 4th European Conference on Behaviour and Energy Efficiency, Coimbra, Portugal.

S.K. Firth, F. Fouchal, T. Kane, V. Dimitriou, and T. Hassan (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.

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


Dataset: REFIT Electrical Load Measurements (Cleaned)

Dataset: UK Homeowner Survey: Perceptions of Smart Home Benefits and Risks

Dataset: REFIT Smart Home Interviews

Academic partners

Loughborough University

Dr Steven Firth

Prof Tarek Hassan

Dr Andrew May

Dr Val Mitchell

Dr Tom Kane

Dr Michael Coleman

Vanda Dimitriou

Stuart Cockbill

University of East Anglia

Dr Charlie Wilson

Dr Tom Hargreaves

Strathclyde University

Dr Vladimir Stankovic

Dr Lina Stankovic

Dr Jing Liao

David Murray

Commercial partners

Adapt Commercial




Green Energy Options

IBM UK Labs National Instruments National

Refurbishment Centre

RWE Effizienz GmbH