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A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles

Author

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  • Marlon Schlemminger

    (Department Solar Energy, Leibniz University Hannover, Appelstr. 2, 30167 Hannover, Germany)

  • Raphael Niepelt

    (Department Solar Energy, Leibniz University Hannover, Appelstr. 2, 30167 Hannover, Germany
    Institute for Solar Energy Research Hamelin (ISFH), Am Ohrberg 1, 31860 Emmerthal, Germany)

  • Rolf Brendel

    (Department Solar Energy, Leibniz University Hannover, Appelstr. 2, 30167 Hannover, Germany
    Institute for Solar Energy Research Hamelin (ISFH), Am Ohrberg 1, 31860 Emmerthal, Germany)

Abstract

End-use specific residential electricity load profiles are of interest for energy system modelling that requires future load curves or demand-side management. We present a model that is applicable across countries to predict consumption on a regional and national scale, using openly available data. The model uses neural networks (NNs) to correlate measured consumption from one country (United Kingdom) with weather data and daily profiles of a mix of human activity and device specific power profiles. We then use region-specific weather data and time-use surveys as input for the trained NNs to predict unscaled electric load profiles. The total power profile consists of the end-use household load profiles scaled with real consumption. We compare the model’s results with measured and independently simulated profiles of various European countries. The NNs achieve a mean absolute error compared with the average load of 6.5 to 33% for the test set. For Germany, the standard deviation between the simulation, the standard load profile H0, and measurements from the University of Applied Sciences Berlin is 26.5%. Our approach reduces the amount of input data required compared with existing models for modelling region-specific electricity load profiles considering end-uses and seasonality based on weather parameters. Hourly load profiles for 29 European countries based on four historical weather years are distributed under an open license.

Suggested Citation

  • Marlon Schlemminger & Raphael Niepelt & Rolf Brendel, 2021. "A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles," Energies, MDPI, vol. 14(8), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2167-:d:535322
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    References listed on IDEAS

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    Cited by:

    1. Peacock, Malcolm & Fragaki, Aikaterini & Matuszewski, Bogdan J, 2023. "The impact of heat electrification on the seasonal and interannual electricity demand of Great Britain," Applied Energy, Elsevier, vol. 337(C).
    2. Lohr, C. & Schlemminger, M. & Peterssen, F. & Bensmann, A. & Niepelt, R. & Brendel, R. & Hanke-Rauschenbach, R., 2022. "Spatial concentration of renewables in energy system optimization models," Renewable Energy, Elsevier, vol. 198(C), pages 144-154.

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