egon-data provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project eGon. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGon project website or its Github repository. egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data. The following data sets are part of the available data bundle: climate_zones_germany Climate zones in Germany source: Own representation based on DWD TRY climate zones License: Attribution 4.0 International (CC BY 4.0) emobility Data on eMobility mit_trip_data:
motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR). Reiner Lemoine Institut, June 2022 License: Attribution 4.0 International (CC BY 4.0) geothermal_potential Spatial distribution of deep geothermal potentials in Germany source: Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook License: Attribution 4.0 International (CC BY 4.0) household_electricity_demand_profiles Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.
The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.
The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh.
A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-37...
motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR). Reiner Lemoine Institut, June 2022 License: Attribution 4.0 International (CC BY 4.0) geothermal_potential Spatial distribution of deep geothermal potentials in Germany source: Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook License: Attribution 4.0 International (CC BY 4.0) household_electricity_demand_profiles Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.
The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.
The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh.
A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-37...