title: An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study.
abstract: 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.
authors.affiliation: Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK.
authors.forename: David
authors.initials: D
authors.lastname: Murray
authors.affiliation: Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK.
authors.forename: Lina
authors.initials: L
authors.lastname: Stankovic
authors.affiliation: Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Stankovic