(WIP) feature: add pipe sizing to dhn analysis

This commit is contained in:
Majid Rezaei 2024-08-01 11:39:05 -04:00
parent cf117e9896
commit d8e5e12e8e
3 changed files with 347 additions and 322 deletions

23
main.py
View File

@ -25,6 +25,7 @@ from scripts.pv_feasibility import pv_feasibility
import matplotlib.pyplot as plt
from scripts.district_heating_network.district_heating_network_creator import DistrictHeatingNetworkCreator
from scripts.district_heating_network.road_processor import road_processor
from scripts.district_heating_network.district_heating_factory import DistrictHeatingFactory
base_path = Path(__file__).parent
dir_manager = DirectoryManager(base_path)
@ -61,7 +62,7 @@ UsageFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()
# EnergyPlus workflow
# energy_plus_workflow(city, energy_plus_output_path)
energy_plus_workflow(city, energy_plus_output_path)
roads_file = road_processor(location[1], location[0], 0.001)
@ -69,5 +70,21 @@ dhn_creator = DistrictHeatingNetworkCreator(geojson_file_path, roads_file)
network_graph = dhn_creator.run()
for node_id, attrs in network_graph.nodes(data=True):
print(f"Node {node_id} has attributes: {dict(attrs)}")
DistrictHeatingFactory(
city,
network_graph,
60,
40,
0.8
).enrich()
DistrictHeatingFactory(
city,
network_graph,
60,
40,
0.8
).sizing()
for u, v, attributes in network_graph.edges(data=True):
print(f"Edge between {u} and {v} with attributes: {attributes}")

View File

@ -1,14 +1,21 @@
import networkx as nx
import logging
import CoolProp as CP
import math
class DistrictHeatingFactory:
"""
DistrictHeatingFactory class
"""
def __init__(self, city, graph):
def __init__(self, city, graph, supply_temperature, return_temperature, simultaneity_factor):
self._city = city
self._network_graph = graph
self._supply_temperature = supply_temperature
self._return_temperature = return_temperature
self.simultaneity_factor = simultaneity_factor
self.fluid = "Water"
def enrich(self):
"""
@ -29,4 +36,48 @@ class DistrictHeatingFactory:
building_found = True
break
if not building_found:
logging.error(msg=f"Building with name '{building_name}' not found in city.")
logging.error(msg=f"Building with name '{building_name}' not found in city.")
def sizing(self):
"""
Calculate the diameter of the pipes in the district heating network.
"""
for node in self._network_graph.nodes(data=True):
node_id, node_attrs = node
if node_attrs.get('type') == 'building':
heating_peak_load = node_attrs.get('heating_peak_load') # Adjusted key to match your data
print(heating_peak_load['year'][0])
if heating_peak_load:
# Calculate peak mass flow rate
peak_mass_flow_rate = heating_peak_load['year'][0] / CP.PropsSI('C',
'T',
(
self._supply_temperature +
self._return_temperature
) / 2,
'P',
101325,
self.fluid)
print(peak_mass_flow_rate)
# Calculate density of the fluid
density = CP.PropsSI('D', # 'D' for density
'T',
(
self._supply_temperature +
self._return_temperature
) / 2,
'P',
101325,
self.fluid)
# Set the design velocity (V)
velocity = 0.9 # m/s
# Calculate the diameter (D)
D = math.sqrt((4 * peak_mass_flow_rate) / (density * velocity * math.pi)) # m
print(D)
# Find the edge connected to the building node
for neighbor in self._network_graph.neighbors(node_id):
if not self._network_graph.nodes[neighbor].get('type') == 'building': # Ensure it's a pipe connection
self._network_graph.edges[node_id, neighbor]['diameter'] = D
logging.info(f"Diameter for edge ({node_id}, {neighbor}) set to {D} meters.")

View File

@ -6,8 +6,8 @@
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2024-07-31T21:38:47.230085Z",
"start_time": "2024-07-31T21:38:47.206748Z"
"end_time": "2024-08-01T12:43:35.084315Z",
"start_time": "2024-08-01T12:43:33.536769Z"
}
},
"source": [
@ -39,13 +39,13 @@
"import numpy as np"
],
"outputs": [],
"execution_count": 110
"execution_count": 1
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T21:38:47.861524Z",
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}
},
"cell_type": "code",
@ -68,13 +68,13 @@
],
"id": "7d895f0e4ec2b851",
"outputs": [],
"execution_count": 111
"execution_count": 2
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T21:39:02.661096Z",
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"start_time": "2024-08-01T12:43:45.041772Z"
}
},
"cell_type": "code",
@ -90,18 +90,18 @@
"WindowsPath('C:/Users/ab_reza/Majid/Concordia/Repositories/energy_system_modelling_workflow/input_files/output_buildings.geojson')"
]
},
"execution_count": 112,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": 112
"execution_count": 3
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T21:39:02.992446Z",
"start_time": "2024-07-31T21:39:02.663422Z"
"end_time": "2024-08-01T12:44:33.071702Z",
"start_time": "2024-08-01T12:44:32.746218Z"
}
},
"cell_type": "code",
@ -115,26 +115,26 @@
],
"id": "c03ae7cae09d4b21",
"outputs": [],
"execution_count": 113
"execution_count": 5
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T21:39:03.340164Z",
"start_time": "2024-07-31T21:39:02.993466Z"
"end_time": "2024-08-01T12:44:34.428791Z",
"start_time": "2024-08-01T12:44:34.009166Z"
}
},
"cell_type": "code",
"source": "ConstructionFactory('nrcan', city).enrich()",
"id": "c7d73638802e40d9",
"outputs": [],
"execution_count": 114
"execution_count": 6
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T21:39:04.079698Z",
"start_time": "2024-07-31T21:39:03.342163Z"
"end_time": "2024-08-01T12:44:36.477528Z",
"start_time": "2024-08-01T12:44:35.383721Z"
}
},
"cell_type": "code",
@ -150,26 +150,26 @@
]
}
],
"execution_count": 115
"execution_count": 7
},
{
"metadata": {
"ExecuteTime": {
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"start_time": "2024-07-31T21:39:04.081700Z"
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"start_time": "2024-08-01T12:44:39.174614Z"
}
},
"cell_type": "code",
"source": "WeatherFactory('epw', city).enrich()",
"id": "f66c01cb42c33b64",
"outputs": [],
"execution_count": 116
"execution_count": 8
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T21:40:39.688386Z",
"start_time": "2024-07-31T21:39:04.650024Z"
"end_time": "2024-08-01T12:46:23.835862Z",
"start_time": "2024-08-01T12:44:40.404507Z"
}
},
"cell_type": "code",
@ -183,18 +183,18 @@
"exporting:\n",
" idf exported...\n",
"\r\n",
"C:/EnergyPlusV23-2-0\\energyplus.exe --weather C:\\Users\\ab_reza\\Majid\\Concordia\\Repositories\\energy_system_modelling_workflow\\hub\\data\\weather\\epw\\CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw --output-directory C:\\Users\\ab_reza\\Majid\\Concordia\\Repositories\\energy_system_modelling_workflow\\out_files\\energy_plus_outputs --idd C:\\Users\\ab_reza\\Majid\\Concordia\\Repositories\\energy_system_modelling_workflow\\hub\\exports\\building_energy\\idf_files\\Energy+.idd --expandobjects --readvars --output-prefix Laval_ C:\\Users\\ab_reza\\Majid\\Concordia\\Repositories\\energy_system_modelling_workflow\\out_files\\energy_plus_outputs\\Laval_602570.idf\r\n",
"C:/EnergyPlusV23-2-0\\energyplus.exe --weather C:\\Users\\ab_reza\\Majid\\Concordia\\Repositories\\energy_system_modelling_workflow\\hub\\data\\weather\\epw\\CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw --output-directory C:\\Users\\ab_reza\\Majid\\Concordia\\Repositories\\energy_system_modelling_workflow\\out_files\\energy_plus_outputs --idd C:\\Users\\ab_reza\\Majid\\Concordia\\Repositories\\energy_system_modelling_workflow\\hub\\exports\\building_energy\\idf_files\\Energy+.idd --expandobjects --readvars --output-prefix Laval_ C:\\Users\\ab_reza\\Majid\\Concordia\\Repositories\\energy_system_modelling_workflow\\out_files\\energy_plus_outputs\\Laval_2285fe.idf\r\n",
"\n"
]
}
],
"execution_count": 117
"execution_count": 9
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T21:53:14.440222Z",
"start_time": "2024-07-31T21:53:10.290860Z"
"end_time": "2024-08-01T12:46:39.090697Z",
"start_time": "2024-08-01T12:46:34.525200Z"
}
},
"cell_type": "code",
@ -211,32 +211,20 @@
"network_graph = dhn_creator.run()"
],
"id": "8403846b0831b51d",
"outputs": [
{
"ename": "AttributeError",
"evalue": "'Graph' object has no attribute 'building_names'",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mAttributeError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[121], line 11\u001B[0m\n\u001B[0;32m 8\u001B[0m dhn_creator \u001B[38;5;241m=\u001B[39m DistrictHeatingNetworkCreator(geojson_file_path, roads_file)\n\u001B[0;32m 10\u001B[0m network_graph \u001B[38;5;241m=\u001B[39m dhn_creator\u001B[38;5;241m.\u001B[39mrun()\n\u001B[1;32m---> 11\u001B[0m \u001B[43mnetwork_graph\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbuilding_names\u001B[49m\n",
"\u001B[1;31mAttributeError\u001B[0m: 'Graph' object has no attribute 'building_names'"
]
}
],
"execution_count": 121
"outputs": [],
"execution_count": 10
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T21:43:05.521839Z",
"start_time": "2024-07-31T21:43:05.503748Z"
"end_time": "2024-08-01T12:46:42.598664Z",
"start_time": "2024-08-01T12:46:42.585722Z"
}
},
"cell_type": "code",
"source": [
"for node_id, attrs in network_graph.nodes(data=True):\n",
" print(f\"Node {node_id} has attributes: {list(attrs.keys())}\")"
" print(f\"Node {node_id} has attributes: {dict(attrs)}\")"
],
"id": "9c4c32ed4a5b5434",
"outputs": [
@ -244,161 +232,149 @@
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}
],
"execution_count": 119
"execution_count": 11
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T21:29:21.717811Z",
"start_time": "2024-07-31T21:29:21.697811Z"
"end_time": "2024-08-01T12:47:28.367869Z",
"start_time": "2024-08-01T12:47:28.352828Z"
}
},
"cell_type": "code",
"source": [
"from scripts.district_heating_network.district_heating_factory import DistrictHeatingFactory\n",
"\n",
"DistrictHeatingFactory(city=city, graph=network_graph)"
"DistrictHeatingFactory(city=city, graph=network_graph).enrich()"
],
"id": "25e14bd5433e3d95",
"outputs": [
{
"ename": "TypeError",
"evalue": "__init__() got an unexpected keyword argument 'graph'",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[94], line 3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mscripts\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mdistrict_heating_network\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mdistrict_heating_factory\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m DistrictHeatingFactory\n\u001B[1;32m----> 3\u001B[0m \u001B[43mDistrictHeatingFactory\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcity\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcity\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgraph\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnetwork_graph\u001B[49m\u001B[43m)\u001B[49m\n",
"\u001B[1;31mTypeError\u001B[0m: __init__() got an unexpected keyword argument 'graph'"
]
}
],
"execution_count": 94
"outputs": [],
"execution_count": 13
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T21:18:46.818842Z",
"start_time": "2024-07-31T21:18:46.799573Z"
"end_time": "2024-08-01T12:47:33.445388Z",
"start_time": "2024-08-01T12:47:33.425843Z"
}
},
"cell_type": "code",
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"Node (-73.70213321668199, 45.530060293550356) has attributes: ['type', 'name', '_name', '_level_of_detail', '_surfaces', '_type', '_city_object_lower_corner', '_city_object_upper_corner', '_detailed_polyhedron', '_simplified_polyhedron', '_min_x', '_min_y', '_min_z', '_max_x', '_max_y', '_max_z', '_centroid', '_volume', '_external_temperature', '_ground_temperature', '_global_horizontal', '_diffuse', '_direct_normal', '_sensors', '_neighbours', '_beam', '_city', '_households', '_basement_heated', '_attic_heated', '_terrains', '_year_of_construction', '_function', '_average_storey_height', '_storeys_above_ground', '_floor_area', '_roof_type', '_internal_zones', '_thermal_zones_from_internal_zones', '_shell', '_aliases', '_cold_water_temperature', '_heating_demand', '_cooling_demand', '_lighting_electrical_demand', '_appliances_electrical_demand', '_domestic_hot_water_heat_demand', '_heating_consumption', '_cooling_consumption', '_domestic_hot_water_consumption', '_distribution_systems_electrical_consumption', '_onsite_electrical_production', '_eave_height', '_energy_systems', '_systems_archetype_name', '_grounds', '_roofs', '_walls', '_internal_walls', '_ground_walls', '_attic_floors', '_interior_slabs', '_domestic_hot_water_peak_load', '_fuel_consumption_breakdown', '_pv_generation']\n",
"Node (-73.70205098392802, 45.53092949418992) has attributes: ['type', 'name', '_name', '_level_of_detail', '_surfaces', '_type', '_city_object_lower_corner', '_city_object_upper_corner', '_detailed_polyhedron', '_simplified_polyhedron', '_min_x', '_min_y', '_min_z', '_max_x', '_max_y', '_max_z', '_centroid', '_volume', '_external_temperature', '_ground_temperature', '_global_horizontal', '_diffuse', '_direct_normal', '_sensors', '_neighbours', '_beam', '_city', '_households', '_basement_heated', '_attic_heated', '_terrains', '_year_of_construction', '_function', '_average_storey_height', '_storeys_above_ground', '_floor_area', '_roof_type', '_internal_zones', '_thermal_zones_from_internal_zones', '_shell', '_aliases', '_cold_water_temperature', '_heating_demand', '_cooling_demand', '_lighting_electrical_demand', '_appliances_electrical_demand', '_domestic_hot_water_heat_demand', '_heating_consumption', '_cooling_consumption', '_domestic_hot_water_consumption', '_distribution_systems_electrical_consumption', '_onsite_electrical_production', '_eave_height', '_energy_systems', '_systems_archetype_name', '_grounds', '_roofs', '_walls', '_internal_walls', '_ground_walls', '_attic_floors', '_interior_slabs', '_domestic_hot_water_peak_load', '_fuel_consumption_breakdown', '_pv_generation']\n",
"Node (-73.70273955351598, 45.53092005042424) has attributes: ['type', 'name', '_name', '_level_of_detail', '_surfaces', '_type', '_city_object_lower_corner', '_city_object_upper_corner', '_detailed_polyhedron', '_simplified_polyhedron', '_min_x', '_min_y', '_min_z', '_max_x', '_max_y', '_max_z', '_centroid', '_volume', '_external_temperature', '_ground_temperature', '_global_horizontal', '_diffuse', '_direct_normal', '_sensors', '_neighbours', '_beam', '_city', '_households', '_basement_heated', '_attic_heated', '_terrains', '_year_of_construction', '_function', '_average_storey_height', '_storeys_above_ground', '_floor_area', '_roof_type', '_internal_zones', '_thermal_zones_from_internal_zones', '_shell', '_aliases', '_cold_water_temperature', '_heating_demand', '_cooling_demand', '_lighting_electrical_demand', '_appliances_electrical_demand', '_domestic_hot_water_heat_demand', '_heating_consumption', '_cooling_consumption', '_domestic_hot_water_consumption', '_distribution_systems_electrical_consumption', '_onsite_electrical_production', '_eave_height', '_energy_systems', '_systems_archetype_name', '_grounds', '_roofs', '_walls', '_internal_walls', '_ground_walls', '_attic_floors', '_interior_slabs', '_domestic_hot_water_peak_load', '_fuel_consumption_breakdown', '_pv_generation']\n"
]
}
],
"execution_count": 80
"execution_count": 14
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T20:23:57.446448Z",
"start_time": "2024-07-31T20:23:57.431469Z"
"end_time": "2024-08-01T13:46:14.045488Z",
"start_time": "2024-08-01T13:46:14.025941Z"
}
},
"cell_type": "code",
"source": [
"for building in city.buildings:\n",
" print(building.name)"
"for node in network_graph.nodes(data=True):\n",
" node_id, node_attrs = node\n",
" if node_attrs.get('name') == '65418':\n",
" building_demand = node_attrs.get('_heating_demand')\n",
" print(building_demand[\"hour\"][1])"
],
"id": "5b96a042e349e0eb",
"id": "f7c0742941b4f2d1",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"65418\n",
"70816\n",
"73478\n",
"82649\n",
"84906\n",
"87241\n",
"87719\n",
"88675\n",
"88747\n",
"89061\n",
"89062\n",
"89251\n",
"91214\n",
"92337\n",
"92399\n",
"92520\n",
"92979\n",
"93149\n",
"95265\n",
"95266\n",
"95465\n",
"95704\n",
"96241\n",
"96579\n",
"96580\n",
"96930\n",
"96931\n",
"96996\n",
"96997\n",
"97648\n",
"98087\n",
"98666\n",
"98667\n",
"102035\n",
"103043\n",
"103740\n",
"103795\n",
"107302\n",
"108296\n",
"108297\n",
"109211\n",
"109305\n",
"109773\n",
"110561\n",
"110873\n",
"113368\n",
"116927\n",
"118062\n",
"118250\n",
"119143\n",
"120435\n",
"124177\n",
"125538\n",
"128322\n",
"129429\n",
"130498\n"
"10687074.91690603\n"
]
}
],
"execution_count": 75
"execution_count": 24
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-07-31T19:35:10.949715Z",
"start_time": "2024-07-31T19:35:09.846007Z"
"end_time": "2024-08-01T14:02:29.592771Z",
"start_time": "2024-08-01T14:02:29.549689Z"
}
},
"cell_type": "code",
"source": "",
"id": "2bb88967eb45bcec",
"source": [
"import CoolProp.CoolProp as CP\n",
"\n",
"# Define the fluid\n",
"fluid = 'Water'\n",
"\n",
"# Define the state properties\n",
"temperature = 300 # Temperature in Kelvin\n",
"pressure = 101325 # Pressure in Pascals (1 atm)\n",
"\n",
"# Calculate specific heat capacity at constant pressure (Cp)\n",
"cp = CP.PropsSI('C', 'T', temperature, 'P', pressure, fluid)\n",
"\n",
"print(f\"The specific heat capacity of water at {temperature} K and {pressure} Pa is {cp} J/kg.K\")"
],
"id": "954e9fbbfe3dbe87",
"outputs": [
{
"name": "stderr",
"name": "stdout",
"output_type": "stream",
"text": [
"IOPub data rate exceeded.\n",
"The Jupyter server will temporarily stop sending output\n",
"to the client in order to avoid crashing it.\n",
"To change this limit, set the config variable\n",
"`--ServerApp.iopub_data_rate_limit`.\n",
"\n",
"Current values:\n",
"ServerApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
"ServerApp.rate_limit_window=3.0 (secs)\n",
"\n"
"The specific heat capacity of water at 300 K and 101325 Pa is 4180.6357765560715 J/kg.K\n"
]
}
],
"execution_count": 52
"execution_count": 25
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2024-08-01T14:56:07.612947Z",
"start_time": "2024-08-01T14:56:07.582195Z"
}
},
"cell_type": "code",
"source": [
"from scripts.district_heating_network.district_heating_factory import DistrictHeatingFactory\n",
"DistrictHeatingFactory(city=city, graph=network_graph, supply_temperature=60, return_temperature=40, simultaneity_factor=0.8).sizing()"
],
"id": "84557dd2ba1070e0",
"outputs": [
{
"ename": "TypeError",
"evalue": "__init__() got an unexpected keyword argument 'supply_temperature'",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[27], line 2\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mscripts\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mdistrict_heating_network\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mdistrict_heating_factory\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m DistrictHeatingFactory\n\u001B[1;32m----> 2\u001B[0m \u001B[43mDistrictHeatingFactory\u001B[49m\u001B[43m(\u001B[49m\u001B[43mcity\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcity\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgraph\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnetwork_graph\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msupply_temperature\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m60\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreturn_temperature\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m40\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msimultaneity_factor\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0.8\u001B[39;49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39msizing()\n",
"\u001B[1;31mTypeError\u001B[0m: __init__() got an unexpected keyword argument 'supply_temperature'"
]
}
],
"execution_count": 27
},
{
"metadata": {},
@ -648,7 +605,7 @@
"outputs": [],
"execution_count": null,
"source": "",
"id": "f7c0742941b4f2d1"
"id": "11ebedbf06ddfe38"
}
],
"metadata": {