Detection of Electric Vehicles and Photovoltaic Systems in Smart Meter Data

Martin Neubert, Oliver Gnepper, Oliver Mey and André Schneider
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Martin Neubert: Fraunhofer IIS/EAS, Fraunhofer Institute for Integrated Circuits, Division Engineering of Adaptive Systems, 01187 Dresden, Germany
Oliver Gnepper: Fraunhofer IIS/EAS, Fraunhofer Institute for Integrated Circuits, Division Engineering of Adaptive Systems, 01187 Dresden, Germany
Oliver Mey: Fraunhofer IIS/EAS, Fraunhofer Institute for Integrated Circuits, Division Engineering of Adaptive Systems, 01187 Dresden, Germany
André Schneider: Fraunhofer IIS/EAS, Fraunhofer Institute for Integrated Circuits, Division Engineering of Adaptive Systems, 01187 Dresden, Germany

Energies, 2022, vol. 15, issue 13, 1-15

Abstract: In the course of the switch to renewable energy sources, there is a shift from a few large energy sources (power plants) to a large number of small, distributed energy sources (e.g., photovoltaic systems) and energy storage devices (e.g., electric vehicles). This results in the need to know and identify these energy sources and sinks as soon as new devices are installed, in order to ensure grid stability. This paper presents an approach to identify energy sources and energy storage in smart meter data, using photovoltaic systems and electric vehicles as examples. For this purpose, the Pecan Street dataset is used, which has been extended by charging processes from the ACN dataset. The presented approach comprises a combination of a Convolutional Neural Network and a Multilayer Perceptron, which decides separately, on the basis of the smart meter data of a household, whether an electric vehicle and a photovoltaic system are present. It is shown that the combination of both classifiers achieves accuracy of 90.50% in the case of electric vehicle detection and 96.37% in the case of photovoltaic systems. It is also shown that the power levels lower than 0 kW in the case of the photovoltaic system and higher than 5 kW in the case of the electric vehicles have the largest influence on the output of the Multilayer Perceptron branch, which uses the power balance distribution as input.

Keywords: classification; machine learning; smart meter data; data fusion; electric vehicle; photovoltaic system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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