heat_demand_timeseries¶
-
class
EgonEtragoHeatCts
(**kwargs)[source]¶ Bases:
sqlalchemy.ext.declarative.api.Base
-
bus_id
¶
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p_set
¶
-
scn_name
¶
-
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class
EgonEtragoTimeseriesIndividualHeating
(**kwargs)[source]¶ Bases:
sqlalchemy.ext.declarative.api.Base
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bus_id
¶
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dist_aggregated_mw
¶
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scenario
¶
-
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class
EgonIndividualHeatingPeakLoads
(**kwargs)[source]¶ Bases:
sqlalchemy.ext.declarative.api.Base
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building_id
¶
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scenario
¶
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w_th
¶
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class
EgonTimeseriesDistrictHeating
(**kwargs)[source]¶ Bases:
sqlalchemy.ext.declarative.api.Base
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area_id
¶
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dist_aggregated_mw
¶
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scenario
¶
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class
HeatTimeSeries
(dependencies)[source]¶ 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 ofDataBundle
. 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
- Resulting tables
demand.egon_timeseries_district_heating
is created and filleddemand.egon_etrago_heat_cts
is created and filleddemand.egon_heat_timeseries_selected_profiles
is created and filleddemand.egon_daily_heat_demand_per_climate_zone
is created and filledboundaries.egon_map_zensus_climate_zones
is created and filled
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name
= 'HeatTimeSeries'¶
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version
= '0.0.7'¶
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create_district_heating_profile
(scenario, area_id)[source]¶ Create heat demand profile for district heating grid including demands of households and service sector.
Parameters: - scenario (str) – Name of the selected scenario.
- area_id (int) – Index of the selected district heating grid
Returns: df (pandas,DataFrame) – Hourly heat demand timeseries in MW for the selected district heating grid
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create_district_heating_profile_python_like
(scenario='eGon2035')[source]¶ 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.
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create_timeseries_for_building
(building_id, scenario)[source]¶ 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