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This is the slides by Tianyi Chen, Xin Wang, G. B. Giannakis.

Plug-meters benefit many grid and building-level energy management applications like automated load control and load scheduling. However, installing and maintaining large and/orlong term deployments of such meters requires assignment and updating of the identity (labels) of electrical loads connected to them. Although the literature on electricity disaggregation and appliance identification is extensive, there is no consensus on the generalizability of the proposed solutions, especially with respect to the features that are extracted from voltage and current measurements.

In the presentation, I will present our research on how to analyze the likelihood of cyber data attacks to power systems.

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