1.
M Schlemminger
A cross-country model for end-use specific aggregated household load profiles Vortrag
Online Event, 20.05.2021, (Jahrestreffen Forschungsnetzwerk Energie – Systemanalyse).
@misc{Schlemminger2021b,
title = {A cross-country model for end-use specific aggregated household load profiles},
author = {M Schlemminger},
year = {2021},
date = {2021-05-20},
address = {Online Event},
note = {Jahrestreffen Forschungsnetzwerk Energie – Systemanalyse},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
2.
M Schlemminger; R Niepelt; R Brendel
A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles Artikel
In: Energies, Bd. 14, Nr. 8, 2021, ISSN: 1996-1073.
@article{Schlemminger2021,
title = {A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles},
author = {M Schlemminger and R Niepelt and R Brendel},
doi = {10.3390/en14082167},
issn = {1996-1073},
year = {2021},
date = {2021-04-02},
journal = {Energies},
volume = {14},
number = {8},
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.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.