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On the Accuracy of Appliance Identification Based on Distributed Load Metering Data.

Reinhardt, Andreas and Baumann, Peter and Burgstahler, D. and Hollick, Matthias and Chonov, H. and Werner, Marc and Steinmetz, R. (2012):
On the Accuracy of Appliance Identification Based on Distributed Load Metering Data.
In: Proceedings of the 2nd IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT), [Conference or Workshop Item]

Abstract

Dynamic load management, i.e., allowing electricity utilities to remotely turn electric appliances in households on or off, represents a key element of the smart grid. Appliances should however only be disconnected from mains when no negative side effects, e.g., loss of data or thawing food, are incurred thereby. This motivates the use of appliance identification techniques, which determine the type of an attached appliance based on the continuous sampling of its power consumption. While various implementations based on different sampling resolutions have been presented in existing literature, the achievable classification accuracies have rarely been analyzed. We address this shortcoming and evaluate the accuracy of appliance identification based on the characteristic features of traces collected during the 24 hours of a day. We evaluate our algorithm using more than 1,000 traces of different electrical appliances' power consumptions. The results show that our approach can identify most of the appliances at high accuracy.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Reinhardt, Andreas and Baumann, Peter and Burgstahler, D. and Hollick, Matthias and Chonov, H. and Werner, Marc and Steinmetz, R.
Title: On the Accuracy of Appliance Identification Based on Distributed Load Metering Data.
Language: German
Abstract:

Dynamic load management, i.e., allowing electricity utilities to remotely turn electric appliances in households on or off, represents a key element of the smart grid. Appliances should however only be disconnected from mains when no negative side effects, e.g., loss of data or thawing food, are incurred thereby. This motivates the use of appliance identification techniques, which determine the type of an attached appliance based on the continuous sampling of its power consumption. While various implementations based on different sampling resolutions have been presented in existing literature, the achievable classification accuracies have rarely been analyzed. We address this shortcoming and evaluate the accuracy of appliance identification based on the characteristic features of traces collected during the 24 hours of a day. We evaluate our algorithm using more than 1,000 traces of different electrical appliances' power consumptions. The results show that our approach can identify most of the appliances at high accuracy.

Title of Book: Proceedings of the 2nd IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT)
Uncontrolled Keywords: Secure Things;domestic appliances;smart power grids;appliance identification;distributed load metering data;dynamic load management;electrical appliance power consumptions;electricity utilities;smart grid;thawing food;time 24 hour;Accuracy;Computers;Feature extraction
Divisions: 20 Department of Computer Science > Sichere Mobile Netze
LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt
LOEWE > LOEWE-Zentren
20 Department of Computer Science
LOEWE
Date Deposited: 31 Dec 2016 11:08
Identification Number: TUD-CS-2012-0172
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