Details
| Original language | English |
|---|---|
| Article number | 2167 |
| Journal | ENERGIES |
| Volume | 14 |
| Issue number | 8 |
| Publication status | Published - 13 Apr 2021 |
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
- Consumer behavior, Cross-country, End-uses, Energy system modelling, Household load profile, Neural network, Open data
ASJC Scopus subject areas
- Energy(all)
- Renewable Energy, Sustainability and the Environment
- Energy(all)
- Fuel Technology
- Energy(all)
- Energy Engineering and Power Technology
- Energy(all)
- Energy (miscellaneous)
- Mathematics(all)
- Control and Optimization
- Engineering(all)
- Electrical and Electronic Engineering
Sustainable Development Goals
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In: ENERGIES, Vol. 14, No. 8, 2167, 13.04.2021.
Research output: Contribution to journal › Article › Research › peer review
}
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 -