| Reference : Suspicious Electric Consumption Detection Based on Multi-Profiling Using Live Machine... |
| Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/22781 | |||
| Suspicious Electric Consumption Detection Based on Multi-Profiling Using Live Machine Learning | |
| English | |
Hartmann, Thomas  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Moawad, Assaad [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Fouquet, François [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Reckinger, Yves [Creos Luxembourg S.A.] | |
| Mouelhi, Tejeddine [iTrust consulting] | |
| Klein, Jacques [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >] | |
| Nov-2015 | |
| 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm) | |
| Yes | |
| No | |
| International | |
| 978-1-4673-8288-5 | |
| 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm) | |
| 02-11-2015 to 05-11-2015 | |
| IEEE Communications Society | |
| Miami | |
| FL 33132, USA | |
| [en] The transition from today’s electricity grid to the so-called smart grid relies heavily on the usage of modern information and communication technology to enable advanced features like two-way communication, an automated control of devices, and automated meter reading. The digital backbone of the smart grid opens the door for advanced collecting, monitoring, and processing of customers’ energy consumption data. One promising approach is the automatic detection of suspicious consumption values, e.g., due to physically or digitally manipulated data or damaged devices. However, detecting suspicious values in the amount of meter data is challenging, especially because
electric consumption heavily depends on the context. For instance, a customers energy consumption profile may change during vacation or weekends compared to normal working days. In this paper we present an advanced software monitoring and alerting system for suspicious consumption value detection based on live machine learning techniques. Our proposed system continuously learns context-dependent consumption profiles of customers, e.g., daily, weekly, and monthly profiles, classifies them and selects the most appropriate one according to the context, like date and weather. By learning not just one but several profiles per customer and in addition taking context parameters into account, our approach can minimize false alerts (low false positive rate). We evaluate our approach in terms of performance (live detection) and accuracy based on a data set from our partner, Creos Luxembourg S.A., the electricity grid operator in Luxembourg. | |
| http://hdl.handle.net/10993/22781 | |
| The research leading to this publication is supported by the National
Research Fund Luxembourg (grant 6816126) and Creos Luxembourg S.A. under the SnT-Creos partnership program. |
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