Reinhardt, Andreas ; Baumann, Peter ; Burgstahler, D. ; Hollick, Matthias ; Chonov, H. ; Werner, Marc ; Steinmetz, R. (2012)
On the Accuracy of Appliance Identification Based on Distributed Load Metering Data.
Conference or Workshop Item, Bibliographie
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 ; Baumann, Peter ; Burgstahler, D. ; Hollick, Matthias ; Chonov, H. ; Werner, Marc ; Steinmetz, R. |
| Type of entry: | Bibliographie |
| Title: | On the Accuracy of Appliance Identification Based on Distributed Load Metering Data. |
| Language: | German |
| Date: | October 2012 |
| Book Title: | Proceedings of the 2nd IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT) |
| 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. |
| 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 |
| Identification Number: | TUD-CS-2012-0172 |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Sichere Mobile Netze LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt |
| Date Deposited: | 31 Dec 2016 11:08 |
| Last Modified: | 10 Jun 2021 06:12 |
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