REDD : A Public Data Set for Energy Disaggregation Research

By J. Kolter, Matthew J. Johnson. 2011

1,445 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/db3ea129add716192f11d41e605c0020ee2488d7

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Citations in the corpus

Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types (2012, methodology): “In Section 4 we empirically evaluate our approach using REDD.”
The ECO data set and the performance of non-intrusive load monitoring algorithms (2014, methodology): “REDD: A public data set for energy disaggregation research.”
Sequence-to-point learning with neural networks for nonintrusive load monitoring (2016, methodology): “We report results on the UK-DALE (Kelly and Knottenbelt 2015b) and REDD (Kolter and Johnson 2011) data sets, which measured the domestic appliance-level energy consumption and whole-house energy usage of five UK houses and six US houses respectively.”
NILMTK: an open source toolkit for non-intrusive load monitoring (2014, methodology): “REDD: A public data set for energy disaggregation research.”
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation (2015, background): “The proportion of total energy correctly assigned is taken from [34].”
Bayesian nonparametric hidden semi-Markov models (2012, methodology): “…Core i7-920 CPUs, released Q4 2008), and a sequence with about 200 detected changepoints would resample each component chain in 0.1 seconds, Figure 16: Overall accuracy comparison between the EM-trained FHMM of (Kolter and Johnson, 2011), the factorial sticky HDP-HMM, and the factorial HDP-HSMM.”
Two Is Not Enough: Privacy Assessment of Aggregation Schemes in Smart Metering (2017, background): “, NAN) load profiles due to the experimental nature of energy consumption recording [31].”
AMPds: A public dataset for load disaggregation and eco-feedback research (2013, background): “The MIT Reference Energy Disaggregation Data Set or REDD [15] supplies high and low frequency readings specifically for residential load disaggregation for a short period of time (from a few weeks to a few months).”
Big IoT data mining for real-time energy disaggregation in buildings (2016, methodology): “Thus, to validate our proposed approach we have used a real-world database, namely The Reference Energy Disaggregation Dataset (REDD), described by Kolter and Johnson in [33].”
Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring (2016, methodology): “To support our contribution claims in Section I-A, we chose two low-frequency datasets: AMPds [12] and REDD [18].”
BLUED : A Fully Labeled Public Dataset for Event-Based Non-Intrusive Load Monitoring Research (2012, methodology): “For the non-event based approach, the Reference Energy Disaggregation Data Set REDD [6] has been released.”