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feature/at
| Author | SHA1 | Date | |
|---|---|---|---|
| 00e2a9d9bf |
@ -29,7 +29,7 @@ residential_systems_percentage = {'system 1 gas': 15,
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||||
'system 8 electricity': 35}
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residential_new_systems_percentage = {
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'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 100,
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||||
'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
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||||
'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
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||||
'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
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'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
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@ -43,7 +43,7 @@ residential_new_systems_percentage = {
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'Central Hydronic Ground and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
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'Central Hydronic Water and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
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'Central Hydronic Water and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
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'Grid Tied PV System': 0,
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'Grid Tied PV System': 100,
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'system 1 gas': 0,
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'system 1 gas grid tied pv': 0,
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'system 1 electricity': 0,
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@ -70,12 +70,12 @@ non_residential_systems_percentage = {'system 1 gas': 0,
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'system 1 electricity': 0,
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'system 2 gas': 0,
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'system 2 electricity': 0,
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'system 3 and 4 gas': 39,
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'system 3 and 4 electricity': 36,
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'system 3 and 4 gas': 50,
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'system 3 and 4 electricity': 0,
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'system 5 gas': 0,
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'system 5 electricity': 0,
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'system 6 gas': 13,
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'system 6 electricity': 12,
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'system 6 gas': 50,
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'system 6 electricity': 0,
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'system 8 gas': 0,
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'system 8 electricity': 0}
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@ -449,19 +449,19 @@
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<generation_system_id>12</generation_system_id>
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<name>Photovoltaic Module</name>
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<system_type>photovoltaic</system_type>
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<model_name>445MS</model_name>
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<model_name>CS7N650MS</model_name>
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<manufacturer>Canadian Solar</manufacturer>
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<nominal_electricity_output>332</nominal_electricity_output>
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<electricity_efficiency>0.201</electricity_efficiency>
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<nominal_electricity_output>487</nominal_electricity_output>
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<electricity_efficiency>0.209</electricity_efficiency>
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<nominal_ambient_temperature>20</nominal_ambient_temperature>
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<nominal_cell_temperature>40</nominal_cell_temperature>
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<nominal_cell_temperature>41</nominal_cell_temperature>
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<nominal_radiation>800</nominal_radiation>
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<standard_test_condition_cell_temperature>25</standard_test_condition_cell_temperature>
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<standard_test_condition_radiation>1000</standard_test_condition_radiation>
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<standard_test_condition_maximum_power>445</standard_test_condition_maximum_power>
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<standard_test_condition_maximum_power>650</standard_test_condition_maximum_power>
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<cell_temperature_coefficient>0.35</cell_temperature_coefficient>
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<width>2.01</width>
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<height>1.048</height>
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<width>2.4</width>
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<height>1.3</height>
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<distribution_systems/>
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<energy_storage_systems/>
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</pv_generation_component>
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@ -39,7 +39,11 @@ class ComnetUsageParameters:
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comnet_archetype_usages = []
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building_functions = building.function.split('_')
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for function in building_functions:
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usages.append(function.split('-'))
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try:
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float(function.split('-')[0])
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usages.append(function.split('-', maxsplit=1))
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except ValueError:
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usages.append([function])
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for usage in usages:
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comnet_usage_name = Dictionaries().hub_usage_to_comnet_usage[usage[-1]]
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try:
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@ -39,7 +39,11 @@ class NrcanUsageParameters:
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comnet_archetype_usages = []
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building_functions = building.function.split('_')
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for function in building_functions:
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usages.append(function.split('-'))
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try:
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float(function.split('-')[0])
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usages.append(function.split('-', maxsplit=1))
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except ValueError:
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usages.append([function])
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for usage in usages:
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usage_name = Dictionaries().hub_usage_to_nrcan_usage[usage[-1]]
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try:
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715
input_files/1.geojson
Normal file
715
input_files/1.geojson
Normal file
@ -0,0 +1,715 @@
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|
||||
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|
||||
[
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||||
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||||
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||||
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|
||||
[
|
||||
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||||
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|
||||
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|
||||
[
|
||||
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||||
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|
||||
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|
||||
[
|
||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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||||
[
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||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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||||
[
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||||
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||||
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||||
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|
||||
[
|
||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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|
||||
[
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||||
-73.62236219082739,
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||||
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||||
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|
||||
[
|
||||
-73.62224229077214,
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||||
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||||
],
|
||||
[
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||||
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||||
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||||
]
|
||||
]
|
||||
]
|
||||
},
|
||||
"id": 107729,
|
||||
"properties": {
|
||||
"name": "05014001",
|
||||
"address": "rue Jean-Talon Ouest (MTL+MTR) 375",
|
||||
"function": 5499,
|
||||
"height": 16,
|
||||
"year_of_construction": 1931
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "Feature",
|
||||
"geometry": {
|
||||
"type": "Polygon",
|
||||
"coordinates": [
|
||||
[
|
||||
[
|
||||
-73.62412409067701,
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||||
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||||
],
|
||||
[
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||||
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||||
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||||
],
|
||||
[
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||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
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||||
[
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||||
-73.62375149120163,
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||||
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||||
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||||
[
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||||
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||||
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||||
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||||
[
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||||
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||||
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||||
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||||
[
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||||
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||||
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||||
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||||
[
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||||
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||||
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||||
],
|
||||
[
|
||||
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||||
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||||
],
|
||||
[
|
||||
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||||
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||||
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|
||||
[
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||||
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||||
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||||
]
|
||||
]
|
||||
]
|
||||
},
|
||||
"id": 115302,
|
||||
"properties": {
|
||||
"name": "03078079",
|
||||
"address": "rue Durocher (MTL) 7255",
|
||||
"function": 1000,
|
||||
"height": 21,
|
||||
"year_of_construction": 1981
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "Feature",
|
||||
"geometry": {
|
||||
"type": "Polygon",
|
||||
"coordinates": [
|
||||
[
|
||||
[
|
||||
-73.624056068251,
|
||||
45.529774883742355
|
||||
],
|
||||
[
|
||||
-73.62396269703619,
|
||||
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|
||||
],
|
||||
[
|
||||
-73.624466791505,
|
||||
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||||
],
|
||||
[
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||||
-73.62455917130055,
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||||
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||||
],
|
||||
[
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||||
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||||
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||||
]
|
||||
]
|
||||
]
|
||||
},
|
||||
"id": 123929,
|
||||
"properties": {
|
||||
"name": "03078303",
|
||||
"address": "rue Hutchison (MTL+OUT) 7300",
|
||||
"function": 9510,
|
||||
"height": 9,
|
||||
"year_of_construction": 1950
|
||||
}
|
||||
},
|
||||
{
|
||||
"type": "Feature",
|
||||
"geometry": {
|
||||
"type": "Polygon",
|
||||
"coordinates": [
|
||||
[
|
||||
[
|
||||
-73.62452499092255,
|
||||
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|
||||
],
|
||||
[
|
||||
-73.62448097226299,
|
||||
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|
||||
],
|
||||
[
|
||||
-73.6246354856489,
|
||||
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|
||||
],
|
||||
[
|
||||
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|
||||
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|
||||
],
|
||||
[
|
||||
-73.62470479138236,
|
||||
45.52966674649997
|
||||
],
|
||||
[
|
||||
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||||
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|
||||
],
|
||||
[
|
||||
-73.62445536849485,
|
||||
45.52959118278851
|
||||
],
|
||||
[
|
||||
-73.6243847903687,
|
||||
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|
||||
],
|
||||
[
|
||||
-73.62452499092255,
|
||||
45.529748746108915
|
||||
],
|
||||
[
|
||||
-73.6245255898649,
|
||||
45.52974864647372
|
||||
],
|
||||
[
|
||||
-73.62452549044556,
|
||||
45.52974884634636
|
||||
],
|
||||
[
|
||||
-73.62452499092255,
|
||||
45.529748746108915
|
||||
]
|
||||
]
|
||||
]
|
||||
},
|
||||
"id": 128445,
|
||||
"properties": {
|
||||
"name": "03081995",
|
||||
"address": "avenue Ogilvy (MTL) 444",
|
||||
"function": 1000,
|
||||
"height": 10,
|
||||
"year_of_construction": 1955
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
3085
input_files/2.geojson
Normal file
3085
input_files/2.geojson
Normal file
File diff suppressed because it is too large
Load Diff
2515
input_files/3.geojson
Normal file
2515
input_files/3.geojson
Normal file
File diff suppressed because it is too large
Load Diff
6189
input_files/selected_buildings.geojson
Normal file
6189
input_files/selected_buildings.geojson
Normal file
File diff suppressed because it is too large
Load Diff
1
input_files/selected_buildings_1.geojson
Normal file
1
input_files/selected_buildings_1.geojson
Normal file
File diff suppressed because one or more lines are too long
132
main.py
132
main.py
@ -0,0 +1,132 @@
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
from building_modelling.ep_run_enrich import energy_plus_workflow
|
||||
from hub.helpers.dictionaries import Dictionaries
|
||||
from hub.imports.construction_factory import ConstructionFactory
|
||||
from hub.imports.energy_systems_factory import EnergySystemsFactory
|
||||
from hub.imports.geometry_factory import GeometryFactory
|
||||
from hub.imports.usage_factory import UsageFactory
|
||||
from hub.imports.weather_factory import WeatherFactory
|
||||
from energy_system_modelling_package import random_assignation
|
||||
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
|
||||
import hub.helpers.constants as cte
|
||||
import json
|
||||
base_path = Path(__file__).parent.resolve()
|
||||
input_files_path = base_path / 'input_files'
|
||||
input_files_path.mkdir(parents=True, exist_ok=True)
|
||||
output_files_path = base_path / 'out_files'
|
||||
output_files_path.mkdir(exist_ok=True, parents=True)
|
||||
energy_plus_output_path = output_files_path / 'energy_plus_outputs'
|
||||
energy_plus_output_path.mkdir(parents=True, exist_ok=True)
|
||||
geojson_path = input_files_path / 'selected_buildings.geojson'
|
||||
city = GeometryFactory(file_type='geojson',
|
||||
path=geojson_path,
|
||||
aliases_field=['address'],
|
||||
height_field='height',
|
||||
year_of_construction_field='year_of_construction',
|
||||
function_field='function',
|
||||
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
|
||||
for building in city.buildings:
|
||||
if Dictionaries().hub_function_to_nrcan_construction_function[building.function] == 'n/a':
|
||||
building.function = cte.WAREHOUSE
|
||||
ConstructionFactory('nrcan', city).enrich()
|
||||
UsageFactory('nrcan', city).enrich()
|
||||
WeatherFactory('epw', city).enrich()
|
||||
residential_buildings = []
|
||||
non_residential_buildings = []
|
||||
for building in city.buildings:
|
||||
if building.function == cte.RESIDENTIAL:
|
||||
residential_buildings.append(building)
|
||||
else:
|
||||
non_residential_buildings.append(building)
|
||||
# random_assignation.call_random(residential_buildings, random_assignation.residential_systems_percentage)
|
||||
# random_assignation.call_random(non_residential_buildings, random_assignation.non_residential_systems_percentage)
|
||||
# EnergySystemsFactory('montreal_future', city).enrich()
|
||||
base_results = pd.read_csv(output_files_path / 'results_summary.csv')
|
||||
energy_system_archetypes = base_results['energy_system_archetype_name'].to_list()
|
||||
for (i, building) in enumerate(city.buildings):
|
||||
building.energy_systems_archetype_name = energy_system_archetypes[i]
|
||||
EnergySystemsFactory('montreal_future', city).enrich()
|
||||
energy_plus_workflow(city, energy_plus_output_path)
|
||||
# hourly_simulation_results = {}
|
||||
# hourly_elec_consumption = pd.DataFrame()
|
||||
# hourly_lighting_appliance_consumption = pd.DataFrame()
|
||||
# for building in city.buildings:
|
||||
# hourly_electricity_consumption = HourlyElectricityDemand(building).calculate()
|
||||
# heating_electricity_consumption = building.heating_consumption[
|
||||
# cte.HOUR] if 'electricity' in building.energy_systems_archetype_name else [0] * len(
|
||||
# building.heating_demand[cte.HOUR])
|
||||
# dhw_electricity_consumption = building.domestic_hot_water_consumption[
|
||||
# cte.HOUR] if 'electricity' in building.energy_systems_archetype_name else [0] * len(
|
||||
# building.domestic_hot_water_heat_demand[cte.HOUR])
|
||||
# if building.cooling_consumption[cte.YEAR][0] == 0:
|
||||
# cooling_electricity_consumption = [building.cooling_demand[cte.HOUR][i] / (2 * 3.23) for i in
|
||||
# range(len(building.cooling_consumption[cte.HOUR]))]
|
||||
# hourly_electricity_consumption = [hourly_electricity_consumption[i] + cooling_electricity_consumption[i] for i in range(len(hourly_electricity_consumption))]
|
||||
# else:
|
||||
# cooling_electricity_consumption = building.cooling_consumption[cte.HOUR]
|
||||
# lighting = building.lighting_electrical_demand[cte.HOUR]
|
||||
# appliance = building.appliances_electrical_demand[cte.HOUR]
|
||||
#
|
||||
# results = pd.DataFrame()
|
||||
# results['heating_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in
|
||||
# heating_electricity_consumption]
|
||||
# results['cooling_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in
|
||||
# cooling_electricity_consumption]
|
||||
# results['dhw_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in
|
||||
# dhw_electricity_consumption]
|
||||
# results['lighting_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in lighting]
|
||||
#
|
||||
# results['appliance_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in appliance]
|
||||
# results['lighting_appliance_total_kWh'] = [(lighting[i] + appliance[i]) / (cte.WATTS_HOUR_TO_JULES * 1000)
|
||||
# for i in range(len(lighting))]
|
||||
# results['total_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in hourly_electricity_consumption]
|
||||
# results.to_csv(output_files_path / 'paper_results' / 'individual_building_simulation_results' / f'{building.name}.csv')
|
||||
# hourly_elec_consumption[f'{building.name}'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in hourly_electricity_consumption]
|
||||
# hourly_lighting_appliance_consumption[f'{building.name}'] = [(lighting[i] + appliance[i]) / (cte.WATTS_HOUR_TO_JULES * 1000)
|
||||
# for i in range(len(lighting))]
|
||||
#
|
||||
# hourly_elec_consumption.to_csv(output_files_path / 'paper_results' / 'total_hourly_electricity_consumption.csv')
|
||||
# hourly_lighting_appliance_consumption.to_csv(output_files_path / 'paper_results' / 'total_lighting_appliance.csv')
|
||||
building_names = [building.name for building in city.buildings]
|
||||
building_addresses = [building.aliases[0] for building in city.buildings]
|
||||
building_heights = [building.max_height for building in city.buildings]
|
||||
buildings_total_floor_areas = [building.thermal_zones_from_internal_zones[0].total_floor_area
|
||||
for building in city.buildings]
|
||||
buildings_roof_areas = [building.roofs[0].perimeter_area for building in city.buildings]
|
||||
building_energy_system_archetype_names = [building.energy_systems_archetype_name for building in city.buildings]
|
||||
building_yearly_heating_electricity_consumptions = []
|
||||
building_yearly_dhw_electricity_consumptions = []
|
||||
for building in city.buildings:
|
||||
if 'electricity' in building.energy_systems_archetype_name:
|
||||
building_yearly_heating_electricity_consumptions.append(building.heating_consumption[cte.YEAR][0] /
|
||||
(cte.WATTS_HOUR_TO_JULES * 1000))
|
||||
building_yearly_dhw_electricity_consumptions.append(building.domestic_hot_water_consumption[cte.YEAR][0] /
|
||||
(cte.WATTS_HOUR_TO_JULES * 1000))
|
||||
else:
|
||||
building_yearly_heating_electricity_consumptions.append(0)
|
||||
building_yearly_dhw_electricity_consumptions.append(0)
|
||||
|
||||
building_yearly_cooling_consumptions = []
|
||||
for building in city.buildings:
|
||||
if building.cooling_consumption[cte.YEAR][0] == 0:
|
||||
building_yearly_cooling_consumptions.append(building.cooling_demand[cte.YEAR][0] / (2 * 3.23 * cte.WATTS_HOUR_TO_JULES * 1000))
|
||||
else:
|
||||
building_yearly_cooling_consumptions.append(building.cooling_consumption[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000))
|
||||
building_yearly_lighting_demands = [building.lighting_electrical_demand[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000)
|
||||
for building in city.buildings]
|
||||
building_yearly_appliance_demands = [building.appliances_electrical_demand[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000)
|
||||
for building in city.buildings]
|
||||
summary = pd.DataFrame()
|
||||
summary['building_name'] = building_names
|
||||
summary['building_address'] = building_addresses
|
||||
summary['building_height_m'] = building_heights
|
||||
summary['total_floor_area_m2'] = buildings_total_floor_areas
|
||||
summary['roof_area_m2'] = buildings_roof_areas
|
||||
summary['energy_system_archetype_name'] = building_energy_system_archetype_names
|
||||
summary['yearly_heating_electricity_consumption_kWh'] = building_yearly_heating_electricity_consumptions
|
||||
summary['yearly_cooling_electricity_consumption_kWh'] = building_yearly_cooling_consumptions
|
||||
summary['yearly_dhw_electricity_consumption_kWh'] = building_yearly_dhw_electricity_consumptions
|
||||
summary['yearly_lighting_consumption_kWh'] = building_yearly_lighting_demands
|
||||
summary['yearly_appliance_consumption_kWh'] = building_yearly_appliance_demands
|
||||
summary.to_csv(output_files_path / 'yearly_results.csv', index=False)
|
||||
98
pv_athena.py
Normal file
98
pv_athena.py
Normal file
@ -0,0 +1,98 @@
|
||||
from pathlib import Path
|
||||
import subprocess
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from energy_system_modelling_package import random_assignation
|
||||
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_system_assessment import \
|
||||
PvSystemAssessment
|
||||
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_calculator import \
|
||||
SolarCalculator
|
||||
from hub.imports.energy_systems_factory import EnergySystemsFactory
|
||||
from hub.imports.geometry_factory import GeometryFactory
|
||||
from hub.helpers.dictionaries import Dictionaries
|
||||
from hub.imports.construction_factory import ConstructionFactory
|
||||
from hub.imports.usage_factory import UsageFactory
|
||||
from hub.imports.weather_factory import WeatherFactory
|
||||
from hub.imports.results_factory import ResultFactory
|
||||
from hub.exports.exports_factory import ExportsFactory
|
||||
import hub.helpers.constants as cte
|
||||
|
||||
# Define paths for input and output directories, ensuring directories are created if they do not exist
|
||||
base_path = Path(__file__).parent.resolve()
|
||||
input_files_path = base_path / 'input_files'
|
||||
input_files_path.mkdir(parents=True, exist_ok=True)
|
||||
output_files_path = base_path / 'out_files'
|
||||
output_files_path.mkdir(exist_ok=True, parents=True)
|
||||
energy_plus_output_path = output_files_path / 'energy_plus_outputs'
|
||||
energy_plus_output_path.mkdir(parents=True, exist_ok=True)
|
||||
geojson_path = input_files_path / 'selected_buildings.geojson'
|
||||
sra_output_path = output_files_path / 'sra_outputs'
|
||||
sra_output_path.mkdir(parents=True, exist_ok=True)
|
||||
pv_assessment_path = output_files_path / 'pv_outputs'
|
||||
pv_assessment_path.mkdir(parents=True, exist_ok=True)
|
||||
# Generate a GeoJSON file for city buildings based on latitude, longitude, and building dimensions
|
||||
|
||||
# Initialize a city object from the geojson file, mapping building functions using a predefined dictionary
|
||||
city = GeometryFactory(file_type='geojson',
|
||||
path=geojson_path,
|
||||
height_field='height',
|
||||
year_of_construction_field='year_of_construction',
|
||||
function_field='function',
|
||||
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
|
||||
# Enrich city data with construction, usage, and weather information specific to the location
|
||||
for building in city.buildings:
|
||||
if Dictionaries().hub_function_to_nrcan_construction_function[building.function] == 'n/a':
|
||||
building.function = cte.WAREHOUSE
|
||||
ConstructionFactory('nrcan', city).enrich()
|
||||
UsageFactory('nrcan', city).enrich()
|
||||
WeatherFactory('epw', city).enrich()
|
||||
# Execute the EnergyPlus workflow to simulate building energy performance and generate output
|
||||
# energy_plus_workflow(city, energy_plus_output_path)
|
||||
# Export the city data in SRA-compatible format to facilitate solar radiation assessment
|
||||
ExportsFactory('sra', city, sra_output_path).export()
|
||||
# Run SRA simulation using an external command, passing the generated SRA XML file path as input
|
||||
sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
|
||||
subprocess.run(['sra', str(sra_path)])
|
||||
# Enrich city data with SRA simulation results for subsequent analysis
|
||||
ResultFactory('sra', city, sra_output_path).enrich()
|
||||
# Assign PV system archetype name to the buildings in city
|
||||
random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
|
||||
# Enrich city model with Montreal future systems parameters
|
||||
EnergySystemsFactory('montreal_future', city).enrich()
|
||||
# # Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
|
||||
tilt_angle = 37
|
||||
solar_parameters = SolarCalculator(city=city,
|
||||
surface_azimuth_angle=180,
|
||||
tilt_angle=tilt_angle,
|
||||
standard_meridian=-75)
|
||||
solar_angles = solar_parameters.solar_angles # Obtain solar angles for further analysis
|
||||
solar_parameters.tilted_irradiance_calculator() # Calculate the solar radiation on a tilted surface
|
||||
# # PV modelling building by building
|
||||
# List of available PV modules ['RE400CAA Pure 2', 'RE410CAA Pure 2', 'RE420CAA Pure 2', 'RE430CAA Pure 2',
|
||||
# 'REC600AA Pro M', 'REC610AA Pro M', 'REC620AA Pro M', 'REC630AA Pro M', 'REC640AA Pro M']
|
||||
building_names = []
|
||||
hourly_pv_outputs = pd.DataFrame()
|
||||
for building in city.buildings:
|
||||
building_names.append(building.name)
|
||||
pv_modeller = PvSystemAssessment(building=building,
|
||||
pv_system=None,
|
||||
battery=None,
|
||||
tilt_angle=tilt_angle,
|
||||
solar_angles=solar_angles,
|
||||
pv_installation_type='rooftop',
|
||||
simulation_model_type='explicit',
|
||||
module_model_name='CS7N650MS',
|
||||
inverter_efficiency=0.95,
|
||||
system_catalogue_handler='montreal_future',
|
||||
roof_percentage_coverage=0.75,
|
||||
facade_coverage_percentage=0,
|
||||
csv_output=False,
|
||||
output_path=pv_assessment_path)
|
||||
pv_modeller.enrich()
|
||||
results = pv_modeller.results
|
||||
pv_output = [x / 1000 for x in results['total_hourly_pv_system_output_W']]
|
||||
hourly_pv_outputs[f'{building.name}'] = pv_output
|
||||
hourly_pv_outputs.to_csv(output_files_path / 'paper_results' / 'hourly_pv_outputs.csv')
|
||||
|
||||
|
||||
198
real_map_with_ids.html
Normal file
198
real_map_with_ids.html
Normal file
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user