Nonintrusive appliance load monitoring: Review and outlook

By Michael Zeifman, Kurt Roth. IEEE transactions on consumer electronics, 2011

834 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/da826d0a7a900dfb1b91266adf40bf63b3ef564e

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Citations in the corpus

Revealing Household Characteristics from Smart Meter Data (2014, background): “This information allows in turn to provide detailed consumption feedback to the households [14, 15, 16].”
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, background): “A few review articles focused on non-intrusive load monitoring (NILM), such as different disaggregation algorithms [17] and the evaluation criteria [18].”
REDD : A Public Data Set for Energy Disaggregation Research (2011, background): “A recent review of numerous existing techniques for energy disaggregation can be found in [18].”
Bayesian nonparametric hidden semi-Markov models (2012, background): “The power disaggregation problem has a rich history (Zeifman and Roth, 2011) with many proposed approaches for a variety of problem specifications.”
Is disaggregation the holy grail of energy efficiency? The case of electricity (2013, methodology): “…events, the fraction of total energy explained, the difference in estimated and true power draw of a given appliance, classification accuracy, fraction of explained energy of each appliance, appliance-wise fraction of load duration, and fraction of " on " intervals missed (Zeifman & Roth, 2011).”
The ECO data set and the performance of non-intrusive load monitoring algorithms (2014, methodology): “, combinatorial or probabilistic), utilize different learning methods, or rely on different data granularities have been proposed in the literature [32, 33].”
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…”
BLUED : A Fully Labeled Public Dataset for Event-Based Non-Intrusive Load Monitoring Research (2012, background): “A good review of the existing approaches can be found in [12].”
Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey (2012, methodology): “Hence, in order to achieve medium or higher rate sampling of the electrical signals, researchers have started to developed their own prototypes for experimental evaluations as discussed in [5,9,11].”