Interannual Electricity Demand Calculator: Country-Level Time Series Based on Weather

An open-source workflow from Karlsruhe Institute of Technology generates artificial electricity consumption time series for European countries. The model, originally developed at the University of Groningen, uses a regression correlating demand to temperature and disaggregates daily estimates into hourly profiles. Output is compatible with the PyPSA-Eur electricity system model.

Use Cases

• Generate synthetic electricity demand time series for energy system modeling based on weather data.
• Analyze the impact of interannual weather variability on electricity consumption patterns based on the temperature regression model.
• Create input data for the PyPSA-Eur open-source electricity system model based on the compatible output format.
• Study the effect of national holidays on electricity demand profiles based on the integrated holiday data.

Strengths

• The model has been validated against historical electricity demand data from the ENTSO-E transparency platform.
• The workflow generates hourly consumption profiles, providing higher temporal resolution than the original daily version.
• The code is released under an MIT license, allowing for open use and modification.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Code originally developed by Lieke van der Most (University of Groningen) for the EU renewable energy modelling framework, adapted by Martha Frysztacki (Karlsruhe Institute of Technology).

Collection Method

Workflow calculates daily demand via regression, then disaggregates to hourly using profiles from the Open Power System Database.

Geography

Country-level coverage for Europe is suggested by the use of European weather data and holiday files.