REFIT has published 4 open access datasets.
REFIT Electrical Load Measurements (Cleaned) (University of Strathclyde) – this dataset includes cleaned electrical consumption data in Watts for 20 households at aggregate and appliance level, timestamped and sampled at 8 second intervals.
UK Homeowner Survey: Perceptions of Smart Home Benefits and Risks (University of East Anglia) – this is a dataset of a national survey of 1,054 homes conducted to measure perceptions of smart homes
REFIT Smart Home Interviews (University of East Anglia) – this dataset contains qualitative data collected using semi-structured interviews and a structured survey at four time points during the REFIT field trial of smart home technologies which involved 20 households.
REFIT Smart Home dataset (Loughborough University) – This dataset includes building survey data, gas consumption, internal air temperature, local climate data and other sensor measurements for the 20 REFIT homes.
Frequently asked questions
How do I access the data?
Click on the links above to the dataset you wish to access. This will take you to the data repository where you can download the data.
Can I use the data in my work?
Yes, the datasets can be used in both commercial and non-commercial research. The licenses and citation requirements are given in the individual data repositories where the datasets are stored.
Is support available for using the datasets?
Yes, each dataset has a contact person who was involved with the collection and curation of the dataset. Please send an email to this person with your query.
How can I find out more information about how the data was collected?
More information is available in the REFIT Final Report and our journal papers, available on the Publications page.
How close are the homes to the Loughborough University weather station?
18 of the homes are within 3km of the weather station. The remaining 2 homes are within 20km of the weather station.
How do I plot the climate data?
This Python notebook gives instructions on how to plot the climate data in the REFIT Smart Home dataset.
I don’t like XML, how can the XML file be converted to table format?
This Python notebook gives instructions on how to convert the XML file into csv files for use in spreadsheets or relational databases.
Why does Building05 have no gas data recorded?
Building05 had a gas meter located in the basement. There was no mobile signal at the meter itself, so it wasn’t possible to record the gas data in this home.
This also happened in Building03 but in this case we were able to install a pulse logger to take the measurements (see the ‘manufacturer’ and ‘model’ attributes of the sensor on the gas meter in Buidling03 for details).
Why do the house IDs for the ‘REFIT Electrical Load Measurements’ and the ‘REFIT Smart Home dataset’ not match?
In these datasets, the relationship between the house IDs is:
- House 1 to House 13 (REFIT Electrical Load Measurements) -> Building01 to Building13 (REFIT Smart Home dataset)
- House 15 to House 21 (REFIT Electrical Load Measurements) -> Building14 to Building20 (REFIT Smart Home dataset)
What is the difference between the ‘FixedHeater’ and ‘Radiator’ elements in the XML file?
Fixed heaters represent those heating devices which are not portable (i.e. they are ‘fixed’) and are not supplied by a central heating system. Radiators represent hot water radiators that are supplied by a central heating system.
Does the building survey cover all items in a home, or is it a partial survey?
All boilers, cookers, fixed heaters, heat pumps, hot water cylinders, lights, meters, openings, persons, radiators, radiator valves, room thermostats, solar photovoltaic arrays, sensors and surfaces are recorded by the building survey. Partially survey items include:
- appliances – small and/or portable appliances may not have been recorded (e.g. mobile phone chargers, irons, lawn mowers)
- water outlets – only showers were recorded. Hot and colds taps in baths and sinks were not recorded
- plugs – only plugs which had an appliance sensor installed were recorded