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Detection of Electric Vehicles and Photovoltaic Systems in Smart Meter Data

Author

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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)

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.

Suggested Citation

  • Martin Neubert & Oliver Gnepper & Oliver Mey & André Schneider, 2022. "Detection of Electric Vehicles and Photovoltaic Systems in Smart Meter Data," Energies, MDPI, vol. 15(13), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4922-:d:856264
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    References listed on IDEAS

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    1. Verma, Anoop & Asadi, Ali & Yang, Kai & Tyagi, Satish, 2015. "A data-driven approach to identify households with plug-in electrical vehicles (PEVs)," Applied Energy, Elsevier, vol. 160(C), pages 71-79.
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    Cited by:

    1. Jansen, Malte & Gross, Rob & Staffell, Iain, 2024. "Quantitative evidence for modelling electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).

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