dc.identifier.uri |
http://dx.doi.org/10.15488/11210 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/11296 |
|
dc.contributor.author |
Schlemminger, Marlon
|
|
dc.contributor.author |
Niepelt, Raphael
|
|
dc.contributor.author |
Brendel, Rolf
|
|
dc.date.accessioned |
2021-08-13T06:50:29Z |
|
dc.date.available |
2021-08-13T06:50:29Z |
|
dc.date.issued |
2021 |
|
dc.identifier.citation |
Schlemminger, M.; Niepelt, R.; Brendel, R.: A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles. In: Energies : open-access journal of related scientific research, technology development and studies in policy and management 14 (2021), Nr. 8, 2167. DOI: https://doi.org/10.3390/en14082167 |
|
dc.description.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. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Basel : MDPI |
|
dc.relation.ispartofseries |
Energies : open-access journal of related scientific research, technology development and studies in policy and management 14 (2021), Nr. 8 |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
energy system modelling |
eng |
dc.subject |
household load profile |
eng |
dc.subject |
neural network |
eng |
dc.subject |
end-uses |
eng |
dc.subject |
consumer behavior |
eng |
dc.subject |
cross-country |
eng |
dc.subject |
open data |
eng |
dc.subject.ddc |
620 | Ingenieurwissenschaften und Maschinenbau
|
ger |
dc.title |
A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles |
|
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
1996-1073 |
|
dc.relation.doi |
https://doi.org/10.3390/en14082167 |
|
dc.bibliographicCitation.issue |
8 |
|
dc.bibliographicCitation.volume |
14 |
|
dc.bibliographicCitation.firstPage |
2167 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|