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Blind Non-intrusive Appliance Load Monitoring using Graph-based Signal Processing

Citation Author(s):
Bochao Zhao, Lina Stankovic
Submitted by:
Vladimir Stankovic
Last updated:
23 February 2016 - 1:44pm
Document Type:
Presentation Slides
Document Year:
2015
Event:
Presenters:
Vladimir Stankovic
 

With ongoing massive smart energy metering
deployments, disaggregation of household's total energy consumption down to individual appliances using purely software tools, aka. non-intrusive appliance load monitoring (NALM),
has generated increased interest. However, despite the fact that
NALM was proposed over 30 years ago, there are still many
open challenges. Indeed, the majority of approaches require
training and are sensitive to appliance changes requiring
regular re-training. In this paper, we tackle this challenge by
proposing a "blind" NALM approach that does not require any
training. The main idea is to build upon an emerging field of
graph-based signal processing to perform adaptive thresholding, signal clustering and feature matching. Using two datasets of active power measurements with 1min and 8sec resolution,
we demonstrate the effectiveness of the proposed method using
a state-of-the-art NALM approaches as benchmarks.

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