@inproceedings{ kelly_uk-dale:_2014,
  title = {'{UK}-{DALE}': A dataset recording {UK} Domestic Appliance-Level Electricity demand and whole-house demand},
  shorttitle = {`{UK}-{DALE}'},
  url = {http://arxiv.org/abs/1404.0284},
  abstract = {Many countries are rolling out smart electricity meters. These measure a home's total power demand. However, research into consumer behaviour suggests that consumers are best able to improve their energy efficiency when provided with itemised, appliance-by-appliance consumption information. Energy disaggregation is a computational technique for estimating appliance-by-appliance energy consumption from a whole-house meter signal. To conduct research on disaggregation algorithms, researchers require data describing not just the aggregate demand per building but also the `ground truth' demand of individual appliances. We present `{UK}-{DALE}': an open-access dataset from the {UK} recording Domestic Appliance-Level Electricity at a sample rate of 16 {kHz} for the whole-house and at 1/6 Hz for individual appliances. This is the first open access {UK} dataset at this temporal resolution. We recorded from four homes, one of which was recorded for 499 days, the longest duration we are aware of for similar datasets. We also describe the low-cost, open-source, wireless system we built for collecting our dataset.},
  urldate = {2014-04-17TZ},
  booktitle = {{arXiv}:1404.0284 [cs]},
  author = {Kelly, Jack and Knottenbelt, William},
  month = {April},
  year = {2014},
  keywords = {Computer Science - Other Computer Science}
}

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