heat_demand_timeseries

• daily
• idp_pool
• service_sector

class EgonEtragoHeatCts(**kwargs)

Bases: sqlalchemy.orm.decl_api.Base

bus_id

p_set

scn_name

class EgonEtragoTimeseriesIndividualHeating(**kwargs)

Bases: sqlalchemy.orm.decl_api.Base

bus_id

dist_aggregated_mw

scenario

class EgonIndividualHeatingPeakLoads(**kwargs)

Bases: sqlalchemy.orm.decl_api.Base

building_id

scenario

w_th

class EgonTimeseriesDistrictHeating(**kwargs)

Bases: sqlalchemy.orm.decl_api.Base

area_id

dist_aggregated_mw

scenario

class HeatTimeSeries(dependencies)

Bases: egon.data.datasets.Dataset

Chooses heat demand profiles for each residential and CTS building

This dataset creates heat demand profiles in an hourly resoultion. Time series for CTS buildings are created using the SLP-gas method implemented in the demandregio disagregator with the function export_etrago_cts_heat_profiles() and stored in the database. Time series for residential buildings are created based on a variety of synthetical created individual demand profiles that are part of DataBundle. This method is desribed within the functions and in this publication:

C. Büttner, J. Amme, J. Endres, A. Malla, B. Schachler, I. Cußmann, Open modeling of electricity and heat demand curves for all residential buildings in Germany, Energy Informatics 5 (1) (2022) 21. doi:10.1186/s42162-022-00201-y.

Dependencies

• DataBundle

• DemandRegio

• HeatDemandImport

• DistrictHeatingAreas

• Vg250

• ZensusMvGridDistricts

• hh_demand_buildings_setup

• WeatherData

Resulting tables

• demand.egon_timeseries_district_heating is created and filled

• demand.egon_etrago_heat_cts is created and filled

• demand.egon_heat_timeseries_selected_profiles is created and filled

• demand.egon_daily_heat_demand_per_climate_zone is created and filled

• boundaries.egon_map_zensus_climate_zones is created and filled

name: str = 'HeatTimeSeries'

version: str = '0.0.12'

calulate_peak_load(df, scenario)

create_district_heating_profile(scenario, area_id)

Create a heat demand profile for a district heating grid.

The created heat demand profile includes the demands of households and the service sector.

Parameters

• scenario (str) – The name of the selected scenario.

• area_id (int) – The index of the selected district heating grid.

Returns

pd.DataFrame – An hourly heat demand timeseries in MW for the selected district heating grid.

create_district_heating_profile_python_like(scenario='eGon2035')

Creates profiles for all district heating grids in one scenario. Similar to create_district_heating_profile but faster and needs more RAM. The results are directly written into the database.

Parameters

scenario (str) – Name of the selected scenario.

Returns

None.

create_individual_heat_per_mv_grid(scenario='eGon2035', mv_grid_id=1564)

create_individual_heating_peak_loads(scenario='eGon2035')

create_individual_heating_profile_python_like(scenario='eGon2035')

create_timeseries_for_building(building_id, scenario)

Generates final heat demand timeseries for a specific building

Parameters

• building_id (int) – Index of the selected building

• scenario (str) – Name of the selected scenario.

Returns

pandas.DataFrame – Hourly heat demand timeseries in MW for the selected building

district_heating(method='python')

export_etrago_cts_heat_profiles()

Export heat cts load profiles at mv substation level to etrago-table in the database

Returns

None.

individual_heating_per_mv_grid(method='python')

individual_heating_per_mv_grid_100(method='python')

individual_heating_per_mv_grid_2035(method='python')

individual_heating_per_mv_grid_tables(method='python')

metadata()

store_national_profiles()