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Commit 6bdc06c1 authored by Jonas Brucksch's avatar Jonas Brucksch
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typo fixes and new running file

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input_files/models/prosumer_models/building_types_aachen/running_77_2022.csv 0 → 100644
+ 200
− 0
View file @ 6bdc06c1
,ID_Building,reference_el_demand,reference_heat_demand,reference_hot_water,profile_el,profile_th,Building_type,name_building
0,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_0
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2,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_2
3,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_3
4,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_4
5,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_5
6,E_0,9979.480577,27156.03132,1075.905206,g4,GHA,0,E_0_Q_77_6
7,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_7
8,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_8
9,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_9
10,A_21_PV28.0,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_PV28.0_Q_77_10
11,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_11
12,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_12
13,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_13
14,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_14
15,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_15
16,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_16
17,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_17
18,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_18
19,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_19
20,A_5_PV8.0,10739.11627,43228.86614,3111.886186,h0,MFH,1,A_5_PV8.0_Q_77_20
21,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_21
22,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_22
23,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_23
24,A_11,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_Q_77_24
25,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_25
26,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_26
27,L_0,4160.278216,11840.43691,468.9450257,l2,GGB,0,L_0_Q_77_27
28,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_28
29,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_29
30,A_13,6666.867267,26836.57626,1931.865866,h0,MFH,1,A_13_Q_77_30
31,A_11,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_Q_77_31
32,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_32
33,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_33
34,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_34
35,AC_0,9386.775092,47801.37623,2003.879256,h0+g1,GMF,0,AC_0_Q_77_35
36,A_4,54611.4143,219830.893,15824.81291,h0,MFH,1,A_4_Q_77_36
37,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_37
38,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_38
39,A_13,6666.867267,26836.57626,1931.865866,h0,MFH,1,A_13_Q_77_39
40,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_40
41,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_41
42,A_11,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_Q_77_42
43,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_43
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51,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_51
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54,A_21_PV28.0_STR,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_PV28.0_STR_Q_77_54
55,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_55
56,D_0,4108.428458,11693.41854,74.68525454,l0,GMK,0,D_0_Q_77_56
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59,A_13,6666.867267,26836.57626,1931.865866,h0,MFH,1,A_13_Q_77_59
60,A_11,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_Q_77_60
61,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_61
62,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_62
63,A_4,54611.4143,219830.893,15824.81291,h0,MFH,1,A_4_Q_77_63
64,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_64
65,AC_0_PV30.0,7450.526532,45015.35711,1084.741535,h0+g1,GMF,0,AC_0_PV30.0_Q_77_65
66,A_27_PV4.0,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_PV4.0_Q_77_66
67,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_67
68,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_68
69,A_11,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_Q_77_69
70,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_70
71,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_71
72,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_72
73,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_73
74,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_74
75,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_75
76,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_76
77,E_0,9979.480577,27156.03132,1075.905206,g4,GHA,0,E_0_Q_77_77
78,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_78
79,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_79
80,A_16_STR,67895.81632,273305.4641,19674.2495,h0,MFH,1,A_16_STR_Q_77_80
81,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_81
82,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_82
83,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_83
84,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_84
85,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_85
86,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_86
87,A_19,8523.514643,34310.26021,2469.868732,h0,MFH,1,A_19_Q_77_87
88,N_0,16602.35612,29318.56517,1312.394883,g1,GBD,0,N_0_Q_77_88
89,A_5_PV4.0,10739.11627,43228.86614,3111.886186,h0,MFH,1,A_5_PV4.0_Q_77_89
90,A_16,67895.81632,273305.4641,19674.2495,h0,MFH,1,A_16_Q_77_90
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113,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_113
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175,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_175
176,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_176
177,E_0,9979.480577,27156.03132,1075.905206,g4,GHA,0,E_0_Q_77_177
178,A_19,8523.514643,34310.26021,2469.868732,h0,MFH,1,A_19_Q_77_178
179,AC_0,13259.27221,53373.41447,3842.154699,h0+g1,GMF,0,AC_0_Q_77_179
180,A_22,13329.62261,53656.60053,3862.540216,h0,MFH,1,A_22_Q_77_180
181,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_181
182,A_4_STR,54611.4143,219830.893,15824.81291,h0,MFH,1,A_4_STR_Q_77_182
183,A_11_STR,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_STR_Q_77_183
184,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_184
185,A_13,6666.867267,26836.57626,1931.865866,h0,MFH,1,A_13_Q_77_185
186,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_186
187,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_187
188,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_188
189,A_22,13329.62261,53656.60053,3862.540216,h0,MFH,1,A_22_Q_77_189
190,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_190
191,A_11,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_Q_77_191
192,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_192
193,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_193
194,A_4,54611.4143,219830.893,15824.81291,h0,MFH,1,A_4_Q_77_194
195,A_5,10739.11627,43228.86614,3111.886186,h0,MFH,1,A_5_Q_77_195
196,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_196
197,A_4,54611.4143,219830.893,15824.81291,h0,MFH,1,A_4_Q_77_197
198,A_14_PV84.0_STR,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_PV84.0_STR_Q_77_198
runme.py
+ 26
− 25
View file @ 6bdc06c1
...@@ -34,12 +34,13 @@ ref_yearly_elec_demand = input_profiles['elec_demand_htw'].sum()*t_step # in kW ...@@ -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_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 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) building_types = pd.read_csv(inputpath_dataframe)
# Create a new column in building_types to store the index of the closest value # Create a new column in building_types to store the index of the closest value
building_types['closest_index'] = building_types.apply( 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) axis=1)
# 'config_path': path to global configurations like prices, injection prices, emission costs, etc. # '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]: ...@@ -53,39 +54,39 @@ for i in building_types.index[0:10]:
# scale profiles based on the reference data # scale profiles based on the reference data
# ELECTRICITY # 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)] 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 input_profiles['elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])] = scaled_heat_demand
# ROOM HEATING # ROOM HEATING
scaled_heat_demand = building_types['Thermal_demand'][i] / ref_yearly_rh_demand * input_profiles['room_heating_sfh_passive'] 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 input_profiles['room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_Building'])] = scaled_heat_demand
# HOT WATER # HOT WATER
scaled_hot_water_demand = building_types['HotWater_demand'][i] / ref_yearly_rh_demand * input_profiles['dhw_shf_passive'] 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 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', 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']), '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']), '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']), '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']), 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_Building']),
'pv_roof': ['pv_factors_aachen_1'], 'pv_roof': ['pv_factors_aachen_1'],
'heat_pump': 'temperature_1'}} 'heat_pump': 'temperature_1'}}
elif 'PV' in building_types.loc[i,'ID_building']: 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', 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']), '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']), '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']), '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']), 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_Building']),
'pv_roof': ['pv_factors_aachen_1']}} 'pv_roof': ['pv_factors_aachen_1']}}
else: 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', 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']), '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']), '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']), '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'])}} 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])}}
num=0 num=0
......
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