diff --git a/input_files/models/prosumer_models/building_types_aachen/running_77_2022.csv b/input_files/models/prosumer_models/building_types_aachen/running_77_2022.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b4305cda643428f232adba9d306f5f2d1585266a
--- /dev/null
+++ b/input_files/models/prosumer_models/building_types_aachen/running_77_2022.csv
@@ -0,0 +1,200 @@
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diff --git a/runme.py b/runme.py
index d1e27436a5836bc178e67dd2056b0e2afb7be6b2..c56dfbf0975886929da536f3a3c262da030676c6 100644
--- a/runme.py
+++ b/runme.py
@@ -34,12 +34,13 @@ ref_yearly_elec_demand = input_profiles['elec_demand_htw'].sum()*t_step # in kW
ref_yearly_rh_demand = input_profiles['room_heating_sfh_passive'].sum()*t_step # in kWh
ref_yearly_dhw_demand = input_profiles['dhw_shf_passive'].sum()*t_step # in kWh
-inputpath_dataframe = r'input_files\models\prosumer_models\building_types_aachen\prosumer_type_configs'
+inputpath_dataframe = r'input_files\models\prosumer_models\building_types_aachen\running_77_2022.csv'
+#inputpath_dataframe = 'input_files/models/district_models/quarter_extraction_df_Q_0.csv'
building_types = pd.read_csv(inputpath_dataframe)
# Create a new column in building_types to store the index of the closest value
building_types['closest_index'] = building_types.apply(
- lambda row: np.abs(row['Electricity_demand'] - ref_yearly_elec_demand).idxmin(),
+ lambda row: np.abs(row['reference_el_demand'] - ref_yearly_elec_demand).idxmin(),
axis=1)
# 'config_path': path to global configurations like prices, injection prices, emission costs, etc.
@@ -53,39 +54,39 @@ for i in building_types.index[0:10]:
# scale profiles based on the reference data
# ELECTRICITY
- scaled_heat_demand = building_types['Electricity_demand'][i] / ref_yearly_elec_demand[index_ref_elec_demand] * input_profiles['ref_elec_demand_id_' + str(index_ref_elec_demand)]
- input_profiles['elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building'])] = scaled_heat_demand
+ scaled_heat_demand = building_types['reference_el_demand'][i] / ref_yearly_elec_demand[index_ref_elec_demand] * input_profiles['ref_elec_demand_id_' + str(index_ref_elec_demand)]
+ input_profiles['elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])] = scaled_heat_demand
# ROOM HEATING
- scaled_heat_demand = building_types['Thermal_demand'][i] / ref_yearly_rh_demand * input_profiles['room_heating_sfh_passive']
- input_profiles['room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_building'])] = scaled_heat_demand
+ scaled_heat_demand = building_types['reference_heat_demand'][i] / ref_yearly_rh_demand * input_profiles['room_heating_sfh_passive']
+ input_profiles['room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_Building'])] = scaled_heat_demand
# HOT WATER
- scaled_hot_water_demand = building_types['HotWater_demand'][i] / ref_yearly_rh_demand * input_profiles['dhw_shf_passive']
- input_profiles['hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_building'])] = scaled_hot_water_demand
+ scaled_hot_water_demand = building_types['reference_hot_water'][i] / ref_yearly_rh_demand * input_profiles['dhw_shf_passive']
+ input_profiles['hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])] = scaled_hot_water_demand
- if 'PV' in building_types.loc[i,'ID_building'] and 'HP' in building_types.loc[i, 'ID_building']:
+ if 'PV' in building_types.loc[i,'ID_Building'] and 'HP' in building_types.loc[i, 'ID_Building']:
- prosumer_dict[building_types.loc[i,'ID_building']]= {'config_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i,'ID_building']) + '/config.csv',
- 'topology_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i,'ID_building']),
- 'profiles':{'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building']),
- 'therm_cns': 'room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_building']),
- 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_building']),
+ prosumer_dict[building_types.loc[i,'name_building']]= {'config_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i,'ID_Building']) + '/config.csv',
+ 'topology_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i,'ID_Building']),
+ 'profiles':{'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building']),
+ 'therm_cns': 'room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_Building']),
+ 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_Building']),
'pv_roof': ['pv_factors_aachen_1'],
'heat_pump': 'temperature_1'}}
- elif 'PV' in building_types.loc[i,'ID_building']:
- prosumer_dict[building_types.loc[i, 'ID_building']] = {'config_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i, 'ID_building']) + '/config.csv',
- 'topology_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i, 'ID_building']),
- 'profiles': {'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building']),
- 'therm_cns': 'room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_building']),
- 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_building']),
+ elif 'PV' in building_types.loc[i,'ID_Building']:
+ prosumer_dict[building_types.loc[i, 'name_building']] = {'config_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i, 'ID_Building']) + '/config.csv',
+ 'topology_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i, 'ID_Building']),
+ 'profiles': {'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building']),
+ 'therm_cns': 'room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_Building']),
+ 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_Building']),
'pv_roof': ['pv_factors_aachen_1']}}
else:
- prosumer_dict[building_types.loc[i, 'ID_building']] = {'config_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i, 'ID_building']) + '/config.csv',
- 'topology_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i, 'ID_building']),
- 'profiles': {'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building']),
- 'therm_cns': 'room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_building']),
- 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_building'])}}
+ prosumer_dict[building_types.loc[i, 'name_building']] = {'config_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i, 'ID_Building']) + '/config.csv',
+ 'topology_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(building_types.loc[i, 'ID_Building']),
+ 'profiles': {'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building']),
+ 'therm_cns': 'room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_Building']),
+ 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])}}
num=0