In REFIT we aim to produce a series of open-access reports, journal and conference publications to maximise the project’s impact.
The REFIT project finished in October 2015. This is the final report of the project, published in March 2016, which details the methods and findings of the work.
The project has published a number of peer-reviewed academic journal papers both during and after the project’s lifetime.
‘Heating behaviour in English homes: An assessment of indirect calculation methods’ by T. Kane, S.K. Firth, T.M. Hassan and V. Dimitriou. Published in April 2017 in Energy and Buildings. DOI: 10.1016/j.enbuild.2017.04.059
- A comprehensive dataset for studying heating in homes has been collected and made publically available.
- Internal room air temperatures in all major rooms, radiator surface temperatures, heating fuel consumption and building survey data are analysed for 20 real-world homes over a five month period.
- An assessment of seven different heating behaviour indirect calculation methods drawn from the literature unites progress in the field to date and provides a clear direction for further research tools.
‘Learning to live in a smart home’ by T. Hargreaves, C. Wilson and R. Hauxwell-Baldwin. Published in February 2017 in Building Research & Information, special issue: Bringing users into building energy performance. DOI: 10.1080/09613218.2017.1286882
- Smart homes promise to significantly enhance domestic comfort, convenience, security and leisure whilst simultaneously reducing energy use through optimized home energy management. Their ability to achieve these multiple aims rests fundamentally on how they are used by householders, yet very little is currently known about this topic. The few studies that have explored the use of smart homes have tended to focus on special-interest groups and be quite short-term. This paper reports on new in-depth qualitative data that explore the domestication of a range of smart home technologies in 10 households participating in a nine-month field trial. Four core themes emerge: (1) smart home technologies are both technically and socially disruptive; (2) smart homes require forms of adaptation and familiarization from householders that can limit their use; (3) learning to use smart home technologies is a demanding and time-consuming task for which there is currently very little support available; and (4) there is little evidence that smart home technologies will generate substantial energy savings and, indeed, there is a risk that they may generate forms of energy intensification. The paper concludes by discussing the implications of these findings for policy, design and further research.
‘An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study’ by D. Murray, L. Stankovic and V. Stankovic. Published in January 2017 in Scientific Data. DOI: 10.1038/sdata.2016.122
- Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data.
‘Benefits and risks of smart home technologies’ by C. Wilson, T. Hargreaves and R. Hauxwell-Baldwin. Published in January 2017 in Energy Policy.
DOI: 10.1016/j.enpol.2016.12.047 (and also reported in Nature Energy at doi:10.1038/nenergy.2017.13)
- Representative national survey of prospective smart home users.
- Comparative analysis of three datasets to analyse perceived benefits and risks of smart home technologies.
- Distinctive characteristics identified of early adopters who seed market growth.
- Comparison of user perceptions with industry marketing.
- Detailed policy recommendations to support energy benefits of smart home technologies.
‘Measuring the energy intensity of domestic activities from smart meter data’ by L. Stankovic, V. Stankovic, J. Liao and C. Wilson. Published in October 2016 in Applied Energy. DOI: 10.1016/j.apenergy.2016.09.087
- Innovative method linking appliance usage and energy use with domestic activities.
- Inferring the energy and time use profile of activities based on smart meter data.
- Standardised metrics quantifying energy intensity + temporal routines of activities.
- Insights from analysing electricity consumption through the lens of activities.
‘Non-intrusive load disaggregation using graph signal processing’ by K. He, L. Stankovic, J. Liao and V. Stankovic. Published in 2016 in IEEE Transactions on Smart Grid. DOI: 10.1109/TSG.2016.2598872
- With the large-scale roll-out of smart metering worldwide, there is a growing need to account for the individual contribution of appliances to the load demand. In this paper, we design a Graph signal processing (GSP)-based approach for nonintrusive appliance load monitoring (NILM), i.e., disaggregation of total energy consumption down to individual appliances used. Leveraging piecewise smoothness of the power load signal, two GSP-based NILM approaches are proposed. The first approach, based on total graph variation minimization, searches for a smooth graph signal under known label constraints. The second approach uses the total graph variation minimizer as a starting point for further refinement via simulated annealing. The proposed GSP-based NILM approach aims to address the large training overhead and associated complexity of conventional graph-based methods through a novel event-based graph approach. Simulation results using two datasets of real house measurements demonstrate the competitive performance of the GSP-based approaches with respect to traditionally used Hidden Markov Model-based and Decision Tree-based approaches.
‘Understanding usage patterns of electric kettle and energy saving potential’ by D. Murray, J. Liao, L. Stankovic, and V. Stankovic. Published in March 2016 in Applied Energy. DOI:10.1016/j.apenergy.2016.03.038
- Time-of-use analysis to motivate kettle usage and consumption prediction.
- Identification of households whose kettle usage and consumption is outside the norm.
- Mathematical model to estimate water volume from consumed power measurements only.
- Quantification of energy savings if a household uses its kettle more efficiently.
- Kettle usage and demand prediction using an Adaptive Neuro Fuzzy Inference System.
‘Data-driven Simple Thermal Models: The Importance of the Parameter Estimates’ by Published in December 2015 in Energy Procedia. DOI: 10.1016/j.egypro.2015.11.322
- A simple 1st order data-driven lumped parameter model of a domestic building is developed to explore the effect of using different model parameter values in the model outputs. The adequacy of the Ordinary Least Square estimation technique is explored. Results show that an improved fit to the measured data can be achieved by varying the initial model parameter values of capacitance (up to 78%), resistance (-46%) and effective window area (-59%). This highlights the importance of having a reference set of parameters based on the known physical characteristics of the building. Finally, the model residuals are deemed appropriate to inform the decision making process for further model development.
Hargreaves, T., Hauxwell-Baldwin, R., Coleman, M., Wilson, C., Stankovic, L., Stankovic, V., Murray, D., Liao, J., Kane, T., Firth, S.K. and Hassan, T., 2015. Smart homes, control and energy management: How do smart home technologies influence control over energy use and domestic life? Paper presented at the European Council for an Energy Efficient Economy (ECEEE) 2015 Summer Study, Toulon/Hyères, France, June 2015.
Kane, T., Firth, S.K., Fouchal, F., Dimitriou, V., Hassan, T., Mitchell, V., Cockbill, S., May, A., Liao, J., Stankovic, L., Murray, D., Stankovic, V. and Wilson, C. Supporting retrofit decisions using smart meter data – a multi-disciplinary approach. Paper presented at the European Council for an Energy Efficient Economy (ECEEE) 2015 Summer Study, Toulon/Hyères, France, June 2015.
Wilson, C., Stankovic, L., Stankovic, V., Liao, J., Coleman, M., Hauxwell-Baldwin, R., Kane, T., Firth, S.K. and Hassan, T., 2015. Identifying the time profile of everyday activities in the home using smart meter data. Paper presented at the European Council for an Energy Efficient Economy (ECEEE) 2015 Summer Study, Toulon/Hyères, France, June 2015.
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, 14-17 June 2015.
Oliveira, L., May, A., Mitchell, V., Coleman, M., Kane, T., Firth, S. 2015. Pre-installation challenges: classifying barriers to the introduction of smart home technology. Third International Conference on ICT for Sustainability – ICT4S 2015, 7th-9th September, Copenhagen – DK
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.
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, 23-24 June 2014.
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.
Liao, J., Elafoudi, G., Stankovic, L. and Stankovic, V., 2014. Power disaggregation for low-sampling rate data. 2nd International Non-intrusive Appliance Load Monitoring Workshop, Austin, TX, June 2014.
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. CIB W78 2013 The 30th International Conference on Applications of IT in the AEC Industry “Move Towards Smart Buildings: Infrastructures and Cities”, Beijing, China, 9-12 October 2013.
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.
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. Annual Conference of the Royal Geographical Society and Institute of British Geographers (RGS-IBS), London, UK, 26-28 August 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., 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.