The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes

By Jack Kelly, William John Knottenbelt. Scientific Data, 2014

991 citations recorded by the source corpus.

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. In this context, 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 five houses, one of which was recorded for 655 days, the longest duration we are aware of for any energy dataset at this sample rate. We also describe the low-cost, open-source, wireless system we built for collecting our dataset. Design Type(s) observation design • time series design Measurement Type(s) whole house energy consumption • appliance-by-appliance energy consumption Technology Type(s) mains electricity meter • plug-in individual appliance monitors Factor Type(s) Sample Characteristic(s) United Kingdom • building Design Type(s) observation design • time series design Measurement Type(s) whole house energy consumption • appliance-by-appliance energy consumption Technology Type(s) mains electricity meter • plug-in individual appliance monitors Factor Type(s) Sample Characteristic(s) United Kingdom • building Machine-accessible metadata file describing the reported data (ISA-Tab format)

Subfields: Building and Construction, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/6929f729194c6aba159d97921c77dc4077e40466

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Citations in the corpus

Neural NILM: Deep Neural Networks Applied to Energy Disaggregation (2015, methodology): “We used UK-DALE [23] as our source dataset.”
Sequence-to-point learning with neural networks for nonintrusive load monitoring (2016, background): “…and architectures for the NILM problem (Kolter and Jaakkola 2012; Parson et al. 2012; Pattem 2012; Zhao, Stankovic, and Stankovic 2015; Zhong, Goddard, and Sutton 2014; 2015; Batra, Singh, and Whitehouse 2016; Tabatabaei, Dick, and Xu 2017), but in our work these features are learned automatically.”