(WIP) feature: add pipe sizing to dhn analysis
This commit is contained in:
parent
ad2d0a86d5
commit
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23
main.py
23
main.py
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@ -25,6 +25,7 @@ from scripts.pv_feasibility import pv_feasibility
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import matplotlib.pyplot as plt
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from scripts.district_heating_network.district_heating_network_creator import DistrictHeatingNetworkCreator
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from scripts.district_heating_network.road_processor import road_processor
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from scripts.district_heating_network.district_heating_factory import DistrictHeatingFactory
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base_path = Path(__file__).parent
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dir_manager = DirectoryManager(base_path)
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@ -61,7 +62,7 @@ UsageFactory('nrcan', city).enrich()
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WeatherFactory('epw', city).enrich()
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# EnergyPlus workflow
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# energy_plus_workflow(city, energy_plus_output_path)
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energy_plus_workflow(city, energy_plus_output_path)
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roads_file = road_processor(location[1], location[0], 0.001)
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@ -69,5 +70,21 @@ dhn_creator = DistrictHeatingNetworkCreator(geojson_file_path, roads_file)
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network_graph = dhn_creator.run()
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for node_id, attrs in network_graph.nodes(data=True):
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print(f"Node {node_id} has attributes: {dict(attrs)}")
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DistrictHeatingFactory(
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city,
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network_graph,
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60,
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40,
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0.8
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).enrich()
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DistrictHeatingFactory(
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city,
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network_graph,
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60,
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40,
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0.8
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).sizing()
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for u, v, attributes in network_graph.edges(data=True):
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print(f"Edge between {u} and {v} with attributes: {attributes}")
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@ -1,20 +1,26 @@
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import networkx as nx
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import logging
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import CoolProp as CP
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import math
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class DistrictHeatingFactory:
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"""
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DistrictHeatingFactory class
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"""
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def __init__(self, city, graph):
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def __init__(self, city, graph, supply_temperature, return_temperature, simultaneity_factor):
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self._city = city
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self._network_graph = graph
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self._supply_temperature = supply_temperature
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self._return_temperature = return_temperature
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self.simultaneity_factor = simultaneity_factor
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self.fluid = "Water"
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def enrich(self):
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"""
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Enrich the network graph nodes with attributes from the city buildings.
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Enrich the network graph nodes with the whole building object from the city buildings.
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"""
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for node in self._network_graph.nodes(data=True):
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node_id, node_attrs = node
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if node_attrs.get('type') == 'building':
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@ -22,11 +28,52 @@ class DistrictHeatingFactory:
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building_found = False
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for building in self._city.buildings:
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if building.name == building_name:
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building_attrs = vars(building)
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for attr, value in building_attrs.items():
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if attr not in self._network_graph.nodes[node_id]:
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self._network_graph.nodes[node_id][attr] = value
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self._network_graph.nodes[node_id]['building_obj'] = building
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building_found = True
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break
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if not building_found:
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logging.error(msg=f"Building with name '{building_name}' not found in city.")
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logging.error(msg=f"Building with name '{building_name}' not found in city.")
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def sizing(self):
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"""
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Calculate the diameter of the pipes in the district heating network.
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"""
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for node in self._network_graph.nodes(data=True):
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node_id, node_attrs = node
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if node_attrs.get('type') == 'building':
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heating_peak_load = node_attrs.get('heating_peak_load') # Adjusted key to match your data
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print(heating_peak_load['year'][0])
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if heating_peak_load:
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# Calculate peak mass flow rate
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peak_mass_flow_rate = heating_peak_load['year'][0] / CP.PropsSI('C',
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'T',
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(
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self._supply_temperature +
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self._return_temperature
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) / 2,
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'P',
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101325,
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self.fluid)
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print(peak_mass_flow_rate)
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# Calculate density of the fluid
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density = CP.PropsSI('D', # 'D' for density
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'T',
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(
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self._supply_temperature +
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self._return_temperature
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) / 2,
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'P',
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101325,
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self.fluid)
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# Set the design velocity (V)
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velocity = 0.9 # m/s
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# Calculate the diameter (D)
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D = math.sqrt((4 * peak_mass_flow_rate) / (density * velocity * math.pi)) # m
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print(D)
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# Find the edge connected to the building node
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for neighbor in self._network_graph.neighbors(node_id):
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if not self._network_graph.nodes[neighbor].get('type') == 'building': # Ensure it's a pipe connection
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self._network_graph.edges[node_id, neighbor]['diameter'] = D
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logging.info(f"Diameter for edge ({node_id}, {neighbor}) set to {D} meters.")
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@ -6,8 +6,8 @@
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2024-07-31T21:38:47.230085Z",
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"start_time": "2024-07-31T21:38:47.206748Z"
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"end_time": "2024-08-02T12:57:37.696565Z",
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"start_time": "2024-08-02T12:57:36.171119Z"
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}
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},
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"source": [
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@ -39,13 +39,13 @@
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"import numpy as np"
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],
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"outputs": [],
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"execution_count": 110
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"execution_count": 1
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-07-31T21:38:47.861524Z",
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"start_time": "2024-07-31T21:38:47.843529Z"
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"end_time": "2024-08-02T12:57:39.137773Z",
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"start_time": "2024-08-02T12:57:39.125428Z"
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}
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},
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"cell_type": "code",
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@ -68,13 +68,13 @@
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],
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"id": "7d895f0e4ec2b851",
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"outputs": [],
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"execution_count": 111
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-07-31T21:39:02.661096Z",
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"start_time": "2024-07-31T21:38:48.727802Z"
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"end_time": "2024-08-02T12:57:55.109576Z",
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"start_time": "2024-08-02T12:57:40.139063Z"
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}
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},
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"cell_type": "code",
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@ -90,18 +90,18 @@
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"WindowsPath('C:/Users/ab_reza/Majid/Concordia/Repositories/energy_system_modelling_workflow/input_files/output_buildings.geojson')"
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]
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},
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"execution_count": 112,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 112
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-07-31T21:39:02.992446Z",
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"start_time": "2024-07-31T21:39:02.663422Z"
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"end_time": "2024-08-02T12:57:56.302754Z",
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"start_time": "2024-08-02T12:57:55.998185Z"
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}
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},
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"cell_type": "code",
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@ -115,26 +115,26 @@
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],
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"id": "c03ae7cae09d4b21",
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"outputs": [],
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"execution_count": 113
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"execution_count": 4
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-07-31T21:39:03.340164Z",
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"start_time": "2024-07-31T21:39:02.993466Z"
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"end_time": "2024-08-02T12:57:57.451734Z",
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"start_time": "2024-08-02T12:57:57.072693Z"
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}
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},
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"cell_type": "code",
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"source": "ConstructionFactory('nrcan', city).enrich()",
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"id": "c7d73638802e40d9",
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"outputs": [],
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"execution_count": 114
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"execution_count": 5
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-07-31T21:39:04.079698Z",
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"start_time": "2024-07-31T21:39:03.342163Z"
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"end_time": "2024-08-02T12:57:59.217403Z",
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"start_time": "2024-08-02T12:57:58.188682Z"
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}
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},
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"cell_type": "code",
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@ -150,26 +150,26 @@
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]
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}
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],
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"execution_count": 115
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"execution_count": 6
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-07-31T21:39:04.648022Z",
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"start_time": "2024-07-31T21:39:04.081700Z"
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"end_time": "2024-08-02T12:58:03.733492Z",
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"start_time": "2024-08-02T12:58:03.184816Z"
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}
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},
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"cell_type": "code",
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"source": "WeatherFactory('epw', city).enrich()",
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"id": "f66c01cb42c33b64",
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"outputs": [],
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"execution_count": 116
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"execution_count": 7
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-07-31T21:40:39.688386Z",
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"start_time": "2024-07-31T21:39:04.650024Z"
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"end_time": "2024-08-02T12:59:40.634144Z",
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"start_time": "2024-08-02T12:58:05.175534Z"
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}
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},
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"cell_type": "code",
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@ -183,18 +183,18 @@
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"exporting:\n",
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" idf exported...\n",
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"\r\n",
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"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",
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"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_6bc439.idf\r\n",
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"\n"
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]
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}
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],
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"execution_count": 117
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"execution_count": 8
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-07-31T21:53:14.440222Z",
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"start_time": "2024-07-31T21:53:10.290860Z"
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"end_time": "2024-08-02T13:07:12.099372Z",
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"start_time": "2024-08-02T13:07:07.642747Z"
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}
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},
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"cell_type": "code",
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@ -211,194 +211,42 @@
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"network_graph = dhn_creator.run()"
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],
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"id": "8403846b0831b51d",
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"outputs": [
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{
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"ename": "AttributeError",
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"evalue": "'Graph' object has no attribute 'building_names'",
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"output_type": "error",
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"traceback": [
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"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[1;31mAttributeError\u001B[0m Traceback (most recent call last)",
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"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",
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"\u001B[1;31mAttributeError\u001B[0m: 'Graph' object has no attribute 'building_names'"
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]
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}
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],
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"execution_count": 121
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"outputs": [],
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"execution_count": 9
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-07-31T21:43:05.521839Z",
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"start_time": "2024-07-31T21:43:05.503748Z"
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}
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},
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"metadata": {},
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"cell_type": "code",
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"source": [
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"for node_id, attrs in network_graph.nodes(data=True):\n",
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" print(f\"Node {node_id} has attributes: {list(attrs.keys())}\")"
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],
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"id": "9c4c32ed4a5b5434",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Node (-73.70263014634182, 45.52966550204674) has attributes: []\n",
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"Node (-73.70252245592799, 45.52959782722166) has attributes: []\n",
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"Node (-73.70277983402246, 45.52975956880018) has attributes: []\n",
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"Node (-73.70292834674622, 45.52985289718704) has attributes: []\n",
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"Node (-73.70299601156968, 45.52989541912497) has attributes: []\n",
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"Node (-73.70304798829301, 45.52992808234479) has attributes: []\n",
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"Node (-73.70315317772048, 45.52999418549968) has attributes: []\n",
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"Node (-73.70322951375971, 45.530042156604246) has attributes: []\n",
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"Node (-73.70334527410391, 45.53011490273612) has attributes: []\n",
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"Node (-73.70388612860485, 45.530454786598085) has attributes: []\n",
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"Node (-73.70321670301797, 45.53098320823811) has attributes: []\n",
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"Node (-73.70309371940914, 45.53090572804479) has attributes: []\n",
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"Node (-73.70336752508702, 45.53107818505422) has attributes: []\n",
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"Node (-73.70300302780161, 45.53115122842582) has attributes: []\n",
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"Node (-73.70298632291501, 45.53083806779961) has attributes: []\n",
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"Node (-73.70284664272657, 45.53075006869057) has attributes: []\n",
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"Node (-73.70282694240179, 45.530737657402696) has attributes: []\n",
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"Node (-73.70268296446567, 45.530646950694454) has attributes: []\n",
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"Node (-73.70262035905371, 45.53060750902034) has attributes: []\n",
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"Node (-73.70250974072788, 45.53053781900757) has attributes: []\n",
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"Node (-73.70248122664219, 45.530519855013075) has attributes: []\n",
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"Node (-73.70237692791034, 45.53045414637121) has attributes: []\n",
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"Node (-73.70241425825014, 45.52952983362164) has attributes: []\n",
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"Node (-73.70258909924681, 45.53147671471601) has attributes: []\n",
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"Node (-73.70246956317335, 45.531401341489406) has attributes: []\n",
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"Node (-73.70281850395438, 45.53162108764596) has attributes: []\n",
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"Node (-73.70235595692806, 45.53165968576366) has attributes: []\n",
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"Node (-73.70235908646175, 45.53133168062488) has attributes: []\n",
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"Node (-73.70226538550632, 45.5312725976791) has attributes: []\n",
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"Node (-73.7022262934011, 45.531247948232114) has attributes: []\n",
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"Node (-73.70218216283965, 45.53122012179686) has attributes: []\n",
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"Node (-73.7020876584622, 45.53116053225497) has attributes: []\n",
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"Node (-73.70208089954498, 45.53115627043355) has attributes: []\n",
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"Node (-73.70195718026818, 45.531078259496624) has attributes: []\n",
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"Node (-73.7019336727694, 45.53106343689135) has attributes: []\n",
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"Node (-73.70183972286668, 45.53100419697237) has attributes: []\n",
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"Node (-73.70182154258106, 45.53099273343045) has attributes: []\n",
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"Node (-73.70170504466955, 45.530919275910655) has attributes: []\n",
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"Node (-73.70169068527439, 45.5309102216234) has attributes: []\n",
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"Node (-73.70191018896638, 45.53200952628766) has attributes: []\n",
|
||||
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]
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}
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||||
],
|
||||
"execution_count": 119
|
||||
"outputs": [],
|
||||
"execution_count": null,
|
||||
"source": "",
|
||||
"id": "fb74382fc70b6933"
|
||||
},
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||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-07-31T21:29:21.717811Z",
|
||||
"start_time": "2024-07-31T21:29:21.697811Z"
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"cell_type": "code",
|
||||
"outputs": [],
|
||||
"execution_count": null,
|
||||
"source": "",
|
||||
"id": "9898d47485af4e62"
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"cell_type": "code",
|
||||
"outputs": [],
|
||||
"execution_count": null,
|
||||
"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
|
||||
"id": "1230e418a231389d"
|
||||
},
|
||||
{
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-07-31T21:18:46.818842Z",
|
||||
"start_time": "2024-07-31T21:18:46.799573Z"
|
||||
"end_time": "2024-08-02T13:08:04.652085Z",
|
||||
"start_time": "2024-08-02T13:08:04.639087Z"
|
||||
}
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||||
},
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"cell_type": "code",
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||||
|
|
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|
||||
"Node (-73.70178207574742, 45.53033147043354) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70279118480165, 45.53007116190442) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70290386342012, 45.53015742711493) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70199360008198, 45.529972641218336) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.7032815855412, 45.52978985115031) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70166271484868, 45.53063422765041) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.7015006171488, 45.530550593136034) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70265213028476, 45.529962782747816) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.7029326957311, 45.53056979610127) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70166661687237, 45.5297928936099) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70193452736822, 45.53043505670828) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70320906423977, 45.53033165241546) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70242433058544, 45.531020523149344) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70229173916934, 45.53104634226288) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70164581777142, 45.53024975981883) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70181323564402, 45.52988517687263) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70207977647193, 45.53050710203167) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70180201572698, 45.53073366018695) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70260551746348, 45.53038579346295) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.7015368490746, 45.531520903846236) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70277909755795, 45.530494359508104) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.7016306503588, 45.531601992190964) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.703188128229, 45.531634438129004) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70225201894137, 45.5306050266003) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70250211711432, 45.53079519337939) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70143287673753, 45.53147394391961) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.7015564456529, 45.52971249323039) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70213321668199, 45.530060293550356) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70205098392802, 45.53092949418992) has attributes: ['type', 'name']\n",
|
||||
"Node (-73.70273955351598, 45.53092005042424) has attributes: ['type', 'name']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": 80
|
||||
"execution_count": 13
|
||||
},
|
||||
{
|
||||
"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": "2024-08-02T12:47:52.738098Z",
|
||||
"start_time": "2024-08-02T12:47:52.701565Z"
|
||||
}
|
||||
},
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"import math\n",
|
||||
"import logging\n",
|
||||
"supply_temperature = 60\n",
|
||||
"return_temperature = 40\n",
|
||||
"\n",
|
||||
"for node in network_graph.nodes(data=True):\n",
|
||||
" node_id, node_attrs = node\n",
|
||||
" if node_attrs.get('type') == 'building':\n",
|
||||
" heating_peak_load = node_attrs.get('heating_peak_load') # Adjusted key to match your data\n",
|
||||
" print(heating_peak_load['year'][0])\n",
|
||||
" # if heating_peak_load:\n",
|
||||
" # # Calculate peak mass flow rate\n",
|
||||
" # peak_mass_flow_rate = heating_peak_load['year'][0] / CP.PropsSI('C',\n",
|
||||
" # 'T',\n",
|
||||
" # (\n",
|
||||
" # supply_temperature +\n",
|
||||
" # return_temperature\n",
|
||||
" # ) / 2,\n",
|
||||
" # 'P',\n",
|
||||
" # 101325,\n",
|
||||
" # fluid)\n",
|
||||
" # print(peak_mass_flow_rate)\n",
|
||||
" # # Calculate density of the fluid\n",
|
||||
" # density = CP.PropsSI('D', # 'D' for density\n",
|
||||
" # 'T',\n",
|
||||
" # (\n",
|
||||
" # supply_temperature +\n",
|
||||
" # return_temperature\n",
|
||||
" # ) / 2,\n",
|
||||
" # 'P',\n",
|
||||
" # 101325,\n",
|
||||
" # fluid)\n",
|
||||
" # \n",
|
||||
" # # Set the design velocity (V)\n",
|
||||
" # velocity = 0.9 # m/s\n",
|
||||
" # \n",
|
||||
" # # Calculate the diameter (D)\n",
|
||||
" # D = math.sqrt((4 * peak_mass_flow_rate) / (density * velocity * math.pi)) # m\n",
|
||||
" # print(D)\n",
|
||||
" # # Find the edge connected to the building node\n",
|
||||
" # for neighbor in network_graph.neighbors(node_id):\n",
|
||||
" # if not network_graph.nodes[neighbor].get('type') == 'building': # Ensure it's a pipe connection\n",
|
||||
" # network_graph.edges[node_id, neighbor]['diameter'] = D\n",
|
||||
" # logging.info(f\"Diameter for edge ({node_id}, {neighbor}) set to {D} meters.\")\n"
|
||||
],
|
||||
"id": "84557dd2ba1070e0",
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "TypeError",
|
||||
"evalue": "'NoneType' object is not subscriptable",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
||||
"\u001B[1;31mTypeError\u001B[0m Traceback (most recent call last)",
|
||||
"Cell \u001B[1;32mIn[28], line 10\u001B[0m\n\u001B[0;32m 8\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m node_attrs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mtype\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbuilding\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m 9\u001B[0m heating_peak_load \u001B[38;5;241m=\u001B[39m node_attrs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mheating_peak_load\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;66;03m# Adjusted key to match your data\u001B[39;00m\n\u001B[1;32m---> 10\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[43mheating_peak_load\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43myear\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m[\u001B[38;5;241m0\u001B[39m])\n",
|
||||
"\u001B[1;31mTypeError\u001B[0m: 'NoneType' object is not subscriptable"
|
||||
]
|
||||
}
|
||||
],
|
||||
"execution_count": 28
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
|
|
@ -648,7 +519,7 @@
|
|||
"outputs": [],
|
||||
"execution_count": null,
|
||||
"source": "",
|
||||
"id": "f7c0742941b4f2d1"
|
||||
"id": "11ebedbf06ddfe38"
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user