The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
Open accessAbstract
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.
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Cites
Is disaggregation the holy grail of energy efficiency? The case of electricity
Carrie Armel, K., Gupta, A., Shrimali, G., & Albert, A. (2013) · Energy Policy · doi:10.1016/j.enpol.2012.08.062 · Cited by 595
Cited by
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
Kelly, J., & Knottenbelt, W. (2015) · Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments · doi:10.1145/2821650.2821672 · Cited by 891
Self-citationNILMTK: an open source toolkit for non-intrusive load monitoring
Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt, W., Rogers, A., Singh, A., & Srivastava, M. (2014) · Proceedings of the 5th international conference on Future energy systems · doi:10.1145/2602044.2602051 · Cited by 502
Self-citationAtitallah, S. B., Driss, M., Boulila, W., & Ghézala, H. B. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100303 · Cited by 375
Transfer Learning for Non-Intrusive Load Monitoring
D'Incecco, M., Squartini, S., & Zhong, M. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2938068 · Cited by 330
Forecasting Energy Consumption of a Public Building Using Transformer and Support Vector Regression
Huang, J., & Kaewunruen, S. (2023) · Energies · doi:10.3390/en16020966 · Cited by 38
Metadata
- Authors
- Kelly, J., & Knottenbelt, W.
- Venue
- Scientific Data
- Published
- 2015-03-31
- Volume
- 2
- Issue
- 1
- Pages
- 150007-150007
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W1996944908
- Cited by
- 1,086
- References
- 1
- Type
- data paper
- Language
- en
- Open access
- gold
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