An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study

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30 Citations (Scopus)

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.
LanguageEnglish
Article number160122
Pages1-12
Number of pages12
JournalScientific Data
Volume4
DOIs
Publication statusPublished - 5 Jan 2017

Fingerprint

Longitudinal Study
longitudinal study
Smart meters
energy
Smart Home
Interval
Energy
Missing Data
automation
Automation
Labels
Monitoring
monitoring
Feedback
Necessary
Longitudinal study
Household
demand
time
Model

Keywords

  • smart metering
  • appliances
  • energy services
  • domestic energy consumption
  • UK longitudinal study
  • REFIT Dataset

Cite this

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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.",
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