Time series classification is a fundamental problem when applied to energy distribution issues. In this paper, the authors propose a solution for the detection of electric energy theft (as well as of electric energy anomalies) by introducing a novel time series classification. Data obtained via actual measurements in industrial sites were employed. Our approach was based on the training of a DNN to recognize whether a measurement of a single-day energy profile were subject to any anomaly. Our proposed method was tested and experimentally validated against the results of accepted benchmarks. The outcomes clearly indicate that the performance of our methodology does outperform the other architectures employed as a benchmark, considering the accuracy and its standard deviation.

Deep Neural Networks for Electric Energy Theft and Anomaly Detection in the Distribution Grid / Ceschini, A.; Rosato, A.; Succetti, F.; Di Luzio, F.; Mitolo, M.; Araneo, R.; Panella, M.. - (2021), pp. 1-5. (Intervento presentato al convegno 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 tenutosi a Bari; Italy) [10.1109/EEEIC/ICPSEurope51590.2021.9584796].

Deep Neural Networks for Electric Energy Theft and Anomaly Detection in the Distribution Grid

Ceschini A.;Rosato A.;Succetti F.;Di Luzio F.;Araneo R.;Panella M.
2021

Abstract

Time series classification is a fundamental problem when applied to energy distribution issues. In this paper, the authors propose a solution for the detection of electric energy theft (as well as of electric energy anomalies) by introducing a novel time series classification. Data obtained via actual measurements in industrial sites were employed. Our approach was based on the training of a DNN to recognize whether a measurement of a single-day energy profile were subject to any anomaly. Our proposed method was tested and experimentally validated against the results of accepted benchmarks. The outcomes clearly indicate that the performance of our methodology does outperform the other architectures employed as a benchmark, considering the accuracy and its standard deviation.
2021
21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021
Deep learning; deep neural networks; electric energy theft; long short-term memory networks; machine learning; time series classification
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Deep Neural Networks for Electric Energy Theft and Anomaly Detection in the Distribution Grid / Ceschini, A.; Rosato, A.; Succetti, F.; Di Luzio, F.; Mitolo, M.; Araneo, R.; Panella, M.. - (2021), pp. 1-5. (Intervento presentato al convegno 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 tenutosi a Bari; Italy) [10.1109/EEEIC/ICPSEurope51590.2021.9584796].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1630054
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