TY - JOUR

T1 - A cross-country model for end-use specific aggregated household load profiles

AU - Schlemminger, Marlon

AU - Niepelt, Raphael

AU - Brendel, Rolf

N1 - Funding Information: Funding: This work was supported by the Ministry of Science and Culture of Lower-Saxony, which we gratefully acknowledge. The publication of this article was funded by the Open Access fund of Leibniz Universität Hannover.

PY - 2021/4/13

Y1 - 2021/4/13

N2 - 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.

AB - 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.

KW - Consumer behavior

KW - Cross-country

KW - End-uses

KW - Energy system modelling

KW - Household load profile

KW - Neural network

KW - Open data

UR - http://www.scopus.com/inward/record.url?scp=85106346699&partnerID=8YFLogxK

U2 - 10.3390/en14082167

DO - 10.3390/en14082167

M3 - Article

AN - SCOPUS:85106346699

VL - 14

JO - ENERGIES

JF - ENERGIES

SN - 1996-1073

IS - 8

M1 - 2167

ER -