Unsupervised Disaggregation of Low Frequency Power Measurements

By Hyungsul Kim, Manish Marwah, Martin F. Arlitt, G. Lyon, Jiawei Han. SDM, 2011

637 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/8067930534f6ab088f88c45665462a394fe33264

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Citations in the corpus

Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation (2012, methodology): “Recent work has begun to look at more complex inference procedures for the actual disaggregation task, including FHMMs (with block Gibbs sampling) (Kim et al., 2011) and sparse coding methods (Kolter et al., 2010).”
Bayesian nonparametric hidden semi-Markov models (2012, methodology): “Some recent work (Kim et al., 2010) has considered applying factorial HSMMs to the disaggregation problem using an EM algorithm; our work here is distinct in that (1) we do not use training data to learn device models but instead rely on simple prior information and learn the model details during in”
Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring (2016, methodology): “[11] used a combination of four different factorial HMM variants to provide an unsupervised learning technique.”
Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey (2012, methodology): “The non-power features such as duration and time of appliance usage along with their real power consumption are used to model device specific HMMs.”
REDD : A Public Data Set for Energy Disaggregation Research (2011, background): “Alternatively, some recent work has advocated unsupervised approaches that consider the whole home signal without labeling, and automatically separate different signals [7].”
Big IoT data mining for real-time energy disaggregation in buildings (2016, methodology): “sensors to identify the operational state of the appliances [12], motion sensors, the frequency of the appliance used [13], as well as time and appliance usage duration [13], [14].”
Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types (2012, methodology): “The second uses unsupervised disaggregation methods (Kim et al. 2011; Zeifman and Roth 2011; Kolter and Jaakkola 2012) in which no prior knowledge of the appliances is assumed, but which often require appliances to be manually labelled after the disaggregation process or assume knowledge of the…”
Smart meter privacy using a rechargeable battery: Minimizing the rate of information leakage (2011, result): “This is not necessarily so, as demonstrated in recent work [9].”
AMPds: A public dataset for load disaggregation and eco-feedback research (2013, background): “Recently researchers have focused on using smart meter data as a more realistic solution [3]–[9].”
The ECO data set and the performance of non-intrusive load monitoring algorithms (2014, background): “, 1 Hz) aggregate consumption data [3, 19, 22].”
NILMTK: an open source toolkit for non-intrusive load monitoring (2014, methodology): “Some disaggregation systems benefit by capturing these patterns (for example, the conditional factorial hidden Markov model (CFHMM) [11] can model the influence of time of day on appliance usage).”