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
Zusammenfassung: |
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.
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Lizenzbestimmungen: |
CC BY 4.0 Unported - https://creativecommons.org/licenses/by/4.0/
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Publikationstyp: |
Article |
Publikationsstatus: |
publishedVersion |
Erstveröffentlichung: |
2021 |
Schlagwörter (englisch): |
energy system modelling, household load profile, neural network, end-uses, consumer behavior, cross-country, open data
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Fachliche Zuordnung (DDC): |
620 | Ingenieurwissenschaften und Maschinenbau
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