Compare commits
No commits in common. "a_gabald" and "main" have entirely different histories.
Binary file not shown.
124
clusters.py
124
clusters.py
|
|
@ -1,124 +0,0 @@
|
|||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.decomposition import PCA
|
||||
from sklearn.cluster import KMeans
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
def extract_building_data(scenario, demand_types):
|
||||
"""
|
||||
Extracts energy demand data for each building from the scenario dictionary.
|
||||
|
||||
Args:
|
||||
scenario (dict): Scenario dictionary containing building data.
|
||||
demand_types (list): List of demand types to extract.
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: DataFrame with buildings as rows and demand types as columns.
|
||||
"""
|
||||
data = []
|
||||
building_ids = []
|
||||
|
||||
for building_id, demand_data in scenario.items():
|
||||
building_row = []
|
||||
for demand_type in demand_types:
|
||||
building_row.extend(demand_data[demand_type])
|
||||
data.append(building_row)
|
||||
building_ids.append(building_id)
|
||||
|
||||
return pd.DataFrame(data, index=building_ids,
|
||||
columns=[f"{demand_type}_{i}" for demand_type in demand_types for i in range(8760)])
|
||||
|
||||
# Plot the data
|
||||
print('test')
|
||||
def cluster_buildings(scenario, demand_types, n_clusters=4,n_components=2):
|
||||
"""
|
||||
Clusters buildings based on their energy demand.
|
||||
|
||||
Args:
|
||||
scenario (dict): Scenario dictionary containing building data.
|
||||
demand_types (list): List of demand types to use for clustering.
|
||||
n_clusters (int): Number of clusters to form.
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: DataFrame with building IDs and their corresponding cluster labels.
|
||||
"""
|
||||
# Extract building data
|
||||
building_data = extract_building_data(scenario, demand_types)
|
||||
|
||||
# Standardize the data
|
||||
scaler = StandardScaler()
|
||||
building_data_scaled = scaler.fit_transform(building_data)
|
||||
|
||||
# Optional: Dimensionality reduction with PCA
|
||||
pca = PCA(n_components)
|
||||
building_data_pca = pca.fit_transform(building_data_scaled)
|
||||
|
||||
# Clustering with K-Means
|
||||
kmeans = KMeans(n_clusters=n_clusters, random_state=42)
|
||||
kmeans.fit(building_data_pca)
|
||||
|
||||
# Assign cluster labels to buildings
|
||||
clusters = kmeans.labels_
|
||||
|
||||
# Create a DataFrame with building IDs and cluster labels
|
||||
clustered_buildings = pd.DataFrame({
|
||||
'building_id': building_data.index,
|
||||
'cluster': clusters
|
||||
})
|
||||
|
||||
return clustered_buildings
|
||||
|
||||
|
||||
# Example usage
|
||||
scenario_data = scenario['efficient with PV+4Pipe+DHW'] # Use 'baseline' or 'efficient with PV'
|
||||
demand_types = [
|
||||
'heating_consumption_kWh'
|
||||
]
|
||||
|
||||
# Cluster buildings
|
||||
clustered_buildings = cluster_buildings(scenario_data, demand_types, n_clusters=4,n_components=20)
|
||||
|
||||
# Visualize clusters
|
||||
plt.figure(figsize=(10, 6))
|
||||
plt.scatter(clustered_buildings['building_id'], clustered_buildings['cluster'], c=clustered_buildings['cluster'],
|
||||
cmap='viridis')
|
||||
plt.xlabel('Building ID')
|
||||
plt.ylabel('Cluster')
|
||||
plt.title('Building Clusters Based on Energy Demand')
|
||||
plt.colorbar(label='Cluster')
|
||||
# Save the plot
|
||||
plt.savefig(os.path.join(output_path, 'clusters.png'))
|
||||
plt.close()
|
||||
|
||||
# Plot the data
|
||||
print('test')
|
||||
|
||||
|
||||
|
||||
output_path_clusters = output_path / 'clustered_buildings_4.xlsx'
|
||||
clustered_buildings.to_excel(output_path_clusters, index=False)
|
||||
|
||||
#clusters made in QGIS
|
||||
clusters_path=output_path/ "clusters" / 'updated_buildings_with_clusters.geojson'
|
||||
|
||||
with open(clusters_path, 'r') as f:
|
||||
clusters = json.load(f)
|
||||
|
||||
def extract_cluster(key, clusters):
|
||||
|
||||
for idx, feature in enumerate(clusters['features']):
|
||||
if str(feature['properties']['id']) == str(key):
|
||||
cluster=feature['properties']['Cluster_ID']
|
||||
|
||||
return cluster
|
||||
cluster_dic={}
|
||||
scenario_data = scenario['efficient with PV+4Pipe+DHW']
|
||||
for key, data in scenario_data.items():
|
||||
cluster_dic[key]=extract_cluster(key, clusters)
|
||||
|
||||
#
|
||||
# cluster_df = pd.DataFrame.from_dict(cluster_dic, orient='index')
|
||||
# cluster_df.to_csv(output_path/'clusters.csv')
|
||||
16
comparing.py
16
comparing.py
|
|
@ -52,6 +52,14 @@ percentage_data = {
|
|||
1690: {"type1_%": 0.94, "type2_%": 0.06, "type3_%": 0, "roof_area": 2189.732519, "total_floor_area": 28466.52275},
|
||||
1691: {"type1_%": 0.75, "type2_%": 0.25, "type3_%": 0, "roof_area": 3159.077523, "total_floor_area": 31590.77523},
|
||||
}
|
||||
# Define the demand types to be plotted
|
||||
demand_types = [
|
||||
'heating_demand_kWh',
|
||||
'cooling_demand_kWh',
|
||||
'domestic_hot_water_heat_demand_kWh',
|
||||
'appliances_electrical_demand_kWh',
|
||||
'lighting_electrical_demand_kWh'
|
||||
]
|
||||
|
||||
# # Function to extract demand data from the GeoJSON
|
||||
# def extract_demand_data(geojson_data, demand_type, period_start, period_end):
|
||||
|
|
@ -114,14 +122,6 @@ percentage_data = {
|
|||
# plot_comparison(selected_buildings, baseline_summer, efficient_summer, range(period_start_summer, period_end_summer), f'One Week in Summer - Comparison - {demand_type}')
|
||||
#
|
||||
|
||||
# Define the demand types to be plotted
|
||||
demand_types = [
|
||||
'heating_demand_kWh',
|
||||
'cooling_demand_kWh',
|
||||
'domestic_hot_water_heat_demand_kWh',
|
||||
'appliances_electrical_demand_kWh',
|
||||
'lighting_electrical_demand_kWh'
|
||||
]
|
||||
|
||||
# Function to extract and sum demand data from the GeoJSON
|
||||
def extract_and_sum_demand_data(geojson_data, demand_type, period_start, period_end):
|
||||
|
|
|
|||
|
|
@ -810,37 +810,37 @@ class Building(CityObject):
|
|||
Get total electricity produced onsite in J
|
||||
return: dict
|
||||
"""
|
||||
# orientation_losses_factor = {cte.MONTH: {'north': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
# 'east': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
# 'south': [2.137931, 1.645503, 1.320946, 1.107817, 0.993213, 0.945175,
|
||||
# 0.967949, 1.065534, 1.24183, 1.486486, 1.918033, 2.210526],
|
||||
# 'west': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]},
|
||||
# cte.YEAR: {'north': [0],
|
||||
# 'east': [0],
|
||||
# 'south': [1.212544],
|
||||
# 'west': [0]}
|
||||
# }
|
||||
#
|
||||
# # Add other systems whenever new ones appear
|
||||
# if self.energy_systems is None:
|
||||
# return self._onsite_electrical_production
|
||||
# for energy_system in self.energy_systems:
|
||||
# for generation_system in energy_system.generation_systems:
|
||||
# if generation_system.system_type == cte.PHOTOVOLTAIC:
|
||||
# if generation_system.electricity_efficiency is not None:
|
||||
# _efficiency = float(generation_system.electricity_efficiency)
|
||||
# else:
|
||||
# _efficiency = 0
|
||||
# self._onsite_electrical_production = {}
|
||||
# for _key in self.roofs[0].global_irradiance.keys():
|
||||
# _results = [0 for _ in range(0, len(self.roofs[0].global_irradiance[_key]))]
|
||||
# for surface in self.roofs:
|
||||
# if _key in orientation_losses_factor:
|
||||
# _results = [x + y * _efficiency * surface.perimeter_area
|
||||
# * surface.solar_collectors_area_reduction_factor * z
|
||||
# for x, y, z in zip(_results, surface.global_irradiance[_key],
|
||||
# orientation_losses_factor[_key]['south'])]
|
||||
# self._onsite_electrical_production[_key] = _results
|
||||
orientation_losses_factor = {cte.MONTH: {'north': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
'east': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
'south': [2.137931, 1.645503, 1.320946, 1.107817, 0.993213, 0.945175,
|
||||
0.967949, 1.065534, 1.24183, 1.486486, 1.918033, 2.210526],
|
||||
'west': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]},
|
||||
cte.YEAR: {'north': [0],
|
||||
'east': [0],
|
||||
'south': [1.212544],
|
||||
'west': [0]}
|
||||
}
|
||||
|
||||
# Add other systems whenever new ones appear
|
||||
if self.energy_systems is None:
|
||||
return self._onsite_electrical_production
|
||||
for energy_system in self.energy_systems:
|
||||
for generation_system in energy_system.generation_systems:
|
||||
if generation_system.system_type == cte.PHOTOVOLTAIC:
|
||||
if generation_system.electricity_efficiency is not None:
|
||||
_efficiency = float(generation_system.electricity_efficiency)
|
||||
else:
|
||||
_efficiency = 0
|
||||
self._onsite_electrical_production = {}
|
||||
for _key in self.roofs[0].global_irradiance.keys():
|
||||
_results = [0 for _ in range(0, len(self.roofs[0].global_irradiance[_key]))]
|
||||
for surface in self.roofs:
|
||||
if _key in orientation_losses_factor:
|
||||
_results = [x + y * _efficiency * surface.perimeter_area
|
||||
* surface.solar_collectors_area_reduction_factor * z
|
||||
for x, y, z in zip(_results, surface.global_irradiance[_key],
|
||||
orientation_losses_factor[_key]['south'])]
|
||||
self._onsite_electrical_production[_key] = _results
|
||||
return self._onsite_electrical_production
|
||||
|
||||
@property
|
||||
|
|
|
|||
|
|
@ -1410,8 +1410,8 @@
|
|||
<demand>cooling</demand>
|
||||
</demands>
|
||||
<components>
|
||||
<generation_id>25</generation_id>
|
||||
<generation_id>17</generation_id>
|
||||
<generation_id>23</generation_id>
|
||||
<generation_id>16</generation_id>
|
||||
</components>
|
||||
</system>
|
||||
<system>
|
||||
|
|
|
|||
664
main.py
664
main.py
|
|
@ -14,14 +14,13 @@ import subprocess
|
|||
from hub.imports.results_factory import ResultFactory
|
||||
from hub.imports.energy_systems_factory import EnergySystemsFactory
|
||||
from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
|
||||
from scripts.solar_angles import CitySolarAngles
|
||||
import hub.helpers.constants as cte
|
||||
from hub.exports.exports_factory import ExportsFactory
|
||||
from scripts.pv_sizing_and_simulation import PVSizingSimulation
|
||||
import pandas as pd
|
||||
from scripts.solar_angles import CitySolarAngles
|
||||
from scripts.radiation_tilted import RadiationTilted
|
||||
|
||||
import geopandas as gpd
|
||||
import json
|
||||
|
||||
#%% # -----------------------------------------------
|
||||
# Specify the GeoJSON file path
|
||||
#%% # -----------------------------------------------
|
||||
|
|
@ -42,7 +41,7 @@ cost_analysis_output_path.mkdir(parents=True, exist_ok=True)
|
|||
#%% # -----------------------------------------------
|
||||
|
||||
geojson_file_path_baseline = output_path / 'updated_buildings_with_all_data_baseline.geojson'
|
||||
geojson_file_path_2024 = output_path / 'updated_buildings_with_all_data_2024.geojson'
|
||||
geojson_file_path_2024 = output_path / 'updated_buildings_with_all_data.geojson'
|
||||
with open(geojson_file_path_baseline , 'r') as f:
|
||||
building_type_data = json.load(f)
|
||||
with open(geojson_file_path_2024, 'r') as f:
|
||||
|
|
@ -55,14 +54,47 @@ city = GeometryFactory('geojson',
|
|||
year_of_construction_field='year_built',
|
||||
function_field='building_type',
|
||||
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
|
||||
#%%----------------------------------------------
|
||||
#%%-----------------------------------------------
|
||||
# Enrich city data
|
||||
#%% # ----------------------------------------------
|
||||
#%% # -----------------------------------------------
|
||||
ConstructionFactory('nrcan', city).enrich()
|
||||
UsageFactory('nrcan', city).enrich()
|
||||
WeatherFactory('epw', city).enrich()
|
||||
# #energy plus is not going to be processed here, as demand has been obtained before
|
||||
# energy_plus_workflow(city)
|
||||
#%%-----------------------------------------------
|
||||
# """SRA"""
|
||||
#%% # -----------------------------------------------
|
||||
ExportsFactory('sra', city, sra_output_path).export()
|
||||
sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
|
||||
subprocess.run(['sra', str(sra_path)])
|
||||
ResultFactory('sra', city, sra_output_path).enrich()
|
||||
solar_angles = CitySolarAngles(city.name,
|
||||
city.latitude,
|
||||
city.longitude,
|
||||
tilt_angle=45,
|
||||
surface_azimuth_angle=180).calculate
|
||||
for building in city.buildings:
|
||||
ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
|
||||
RadiationTilted(building,
|
||||
solar_angles,
|
||||
tilt_angle=45,
|
||||
ghi=ghi).enrich()
|
||||
# building_names = []
|
||||
# for building in city.buildings:
|
||||
# building_names.append(building.name)
|
||||
#
|
||||
# df = pd.DataFrame(columns=building_names)
|
||||
# df1 = pd.DataFrame(columns=building_names)
|
||||
# print('test')
|
||||
# for building in city.buildings:
|
||||
# # if building.name in selected_buildings_list:
|
||||
# df[f'{building.name}'] = building.roofs[0].global_irradiance[cte.HOUR]
|
||||
# df1[f'{building.name}'] = building.roofs[0].global_irradiance_tilted[cte.HOUR]
|
||||
#
|
||||
# df.to_csv('solar_radiation_horizontal_selected_buildings.csv')
|
||||
# df1.to_csv('solar_radiation_tilted_selected_buildings.csv')
|
||||
|
||||
#%% # -----------------------------------------------
|
||||
#"""Enrich city with geojson file data"""
|
||||
#%% # -----------------------------------------------
|
||||
|
|
@ -137,616 +169,50 @@ print('test')
|
|||
#%% # -----------------------------------------------
|
||||
|
||||
for building in city.buildings:
|
||||
building.energy_systems_archetype_name = 'system 1 electricity'
|
||||
building.energy_systems_archetype_name = 'system 1 electricity pv'
|
||||
|
||||
|
||||
EnergySystemsFactory('montreal_custom', city).enrich()
|
||||
|
||||
def baseline_to_dict(building):
|
||||
# for building in city.buildings:
|
||||
# energy_systems = building.energy_systems
|
||||
# for energy_system in energy_systems:
|
||||
# generation_units = energy_system.generation_systems
|
||||
# if cte.HEATING in energy_system.demand_types:
|
||||
# for generation_unit in generation_units:
|
||||
# generation_unit.heat_efficiency = 0.96
|
||||
def to_dict(building, total_floor_area):
|
||||
return {
|
||||
'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_consumption[cte.HOUR]],
|
||||
'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_consumption[cte.HOUR]],
|
||||
'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_consumption[cte.HOUR]],
|
||||
'roof_area': building.floor_area,
|
||||
'total_floor_area': total_floor_area,
|
||||
'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.energy_consumption_breakdown[cte.HOUR]],
|
||||
'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_demand[cte.HOUR]],
|
||||
'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
|
||||
'appliances_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
|
||||
'lighting_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]]
|
||||
}
|
||||
buildings_dic={}
|
||||
for building in city.buildings:
|
||||
buildings_dic[building.name]=baseline_to_dict(building)
|
||||
scenario={}
|
||||
|
||||
scenario['baseline']=buildings_dic
|
||||
print("Scenario 1: Baseline is performed successfully")
|
||||
|
||||
|
||||
|
||||
del city
|
||||
del buildings_dic
|
||||
del building_type_data
|
||||
|
||||
#%%-----------------------------------------------
|
||||
# Scenario 2
|
||||
#%% # -----------------------------------------------
|
||||
|
||||
# Create city object from GeoJSON file
|
||||
city = GeometryFactory('geojson',
|
||||
path=geojson_file_path_2024,
|
||||
height_field='maximum_roof_height',
|
||||
year_of_construction_field='year_built',
|
||||
function_field='building_type',
|
||||
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
|
||||
#%%-----------------------------------------------
|
||||
# Enrich city data
|
||||
#%% # -----------------------------------------------
|
||||
ConstructionFactory('nrcan', city).enrich()
|
||||
UsageFactory('nrcan', city).enrich()
|
||||
WeatherFactory('epw', city).enrich()
|
||||
|
||||
enrich_buildings_with_geojson_data (building_type_data_2024, city)
|
||||
|
||||
def to_dict(building,hourly_pv):
|
||||
return {
|
||||
'heating_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.heating_consumption[cte.HOUR]],
|
||||
'cooling_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.cooling_consumption[cte.HOUR]],
|
||||
'domestic_hot_water_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.domestic_hot_water_consumption[cte.HOUR]],
|
||||
'appliances_consumption_kWh':[x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.appliances_electrical_demand[cte.HOUR]],
|
||||
'lighting_consumption_kWh': [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in building.lighting_electrical_demand[cte.HOUR]],
|
||||
'hourly_pv_kWh': [x /(cte.WATTS_HOUR_TO_JULES * 1000) for x in hourly_pv]
|
||||
}
|
||||
buildings_dic={}
|
||||
|
||||
|
||||
for building in city.buildings:
|
||||
building.energy_systems_archetype_name = 'system 1 electricity pv'
|
||||
|
||||
EnergySystemsFactory('montreal_custom', city).enrich()
|
||||
# #%%-----------------------------------------------
|
||||
# # """SRA"""
|
||||
# #%% # -----------------------------------------------
|
||||
ExportsFactory('sra', city, output_path).export()
|
||||
sra_path = (output_path / f'{city.name}_sra.xml').resolve()
|
||||
subprocess.run(['sra', str(sra_path)])
|
||||
ResultFactory('sra', city, output_path).enrich()
|
||||
solar_angles = CitySolarAngles(city.name,
|
||||
city.latitude,
|
||||
city.longitude,
|
||||
tilt_angle=45,
|
||||
surface_azimuth_angle=180).calculate
|
||||
df = pd.DataFrame()
|
||||
df.index = ['yearly lighting (kWh)', 'yearly appliance (kWh)', 'yearly heating (kWh)', 'yearly cooling (kWh)',
|
||||
'yearly dhw (kWh)', 'roof area (m2)', 'used area for pv (m2)', 'number of panels', 'pv production (kWh)']
|
||||
for building in city.buildings:
|
||||
ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
|
||||
pv_sizing_simulation = PVSizingSimulation(building,
|
||||
solar_angles,
|
||||
tilt_angle=45,
|
||||
module_height=1,
|
||||
module_width=2,
|
||||
ghi=ghi)
|
||||
pv_sizing_simulation.pv_output()
|
||||
yearly_lighting = building.lighting_electrical_demand[cte.YEAR][0] / 1000
|
||||
yearly_appliance = building.appliances_electrical_demand[cte.YEAR][0] / 1000
|
||||
yearly_heating = building.heating_demand[cte.YEAR][0] / (3.6e6 * 3)
|
||||
yearly_cooling = building.cooling_demand[cte.YEAR][0] / (3.6e6 * 4.5)
|
||||
yearly_dhw = building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1000
|
||||
roof_area = building.roofs[0].perimeter_area
|
||||
used_roof = pv_sizing_simulation.available_space()
|
||||
number_of_pv_panels = pv_sizing_simulation.total_number_of_panels
|
||||
yearly_pv = building.onsite_electrical_production[cte.YEAR][0] / (3.6e6)
|
||||
hourly_pv = building.onsite_electrical_production[cte.HOUR]
|
||||
df[f'{building.name}'] = [yearly_lighting, yearly_appliance, yearly_heating, yearly_cooling, yearly_dhw, roof_area,
|
||||
used_roof, number_of_pv_panels, yearly_pv]
|
||||
buildings_dic[building.name]=to_dict(building,hourly_pv)
|
||||
|
||||
# %%-----------------------------------------------
|
||||
# """South facing facades"""
|
||||
# %% # -----------------------------------------------
|
||||
# Function to convert radians to degrees
|
||||
import math
|
||||
def radians_to_degrees(radians):
|
||||
return radians * (180 / math.pi)
|
||||
# Step 1: Create the walls_id dictionary
|
||||
walls_id={}
|
||||
|
||||
for building in city.buildings:
|
||||
ids = {}
|
||||
for walls in building.walls:
|
||||
id=walls.id
|
||||
azimuth_degree=radians_to_degrees(float(walls.azimuth))
|
||||
if azimuth_degree>90.0 or azimuth_degree <float(-90.0):
|
||||
ids[id]= {
|
||||
'azimuth': azimuth_degree,
|
||||
'global_irradiance': walls.global_irradiance[cte.HOUR],
|
||||
'area': walls.perimeter_area
|
||||
}
|
||||
walls_id[building.name] = ids
|
||||
|
||||
# Step 2: Calculate pv_on_facade for each wall
|
||||
for building_id, ids in walls_id.items():
|
||||
for wall_id, wall_data in ids.items():
|
||||
if 'global_irradiance' in wall_data:
|
||||
ghi = [x / cte.WATTS_HOUR_TO_JULES/1000 for x in wall_data['global_irradiance']]
|
||||
wall_data['pv_on_facade'] = [x * 0.6 * wall_data['area']*0.22 for x in ghi]
|
||||
total_floor_area = 0
|
||||
for thermal_zone in building.thermal_zones_from_internal_zones:
|
||||
total_floor_area += thermal_zone.total_floor_area
|
||||
|
||||
|
||||
|
||||
|
||||
walls_dic = output_path / 'walls_id.json'
|
||||
with open(walls_dic , 'w') as json_file:
|
||||
json.dump(walls_id, json_file, indent=4)
|
||||
|
||||
import pandas as pd
|
||||
#### EXPORT
|
||||
# Convert walls_id dictionary to a DataFrame
|
||||
# Convert walls_id dictionary to DataFrames for static and hourly data
|
||||
# def convert_walls_id_to_dfs(walls_id):
|
||||
# static_data = {}
|
||||
# hourly_data = {}
|
||||
#
|
||||
# for building_id, ids in walls_id.items():
|
||||
# for wall_id, wall_data in ids.items():
|
||||
# # Static data
|
||||
# static_data[f"{building_id}_{wall_id}_azimuth"] = wall_data.get('azimuth', None)
|
||||
# static_data[f"{building_id}_{wall_id}_area"] = wall_data.get('area', None)
|
||||
#
|
||||
# if 'pv_on_facade' in wall_data:
|
||||
# hourly_data[f"{building_id}_{wall_id}_pv_on_facade"] = wall_data['pv_on_facade']
|
||||
#
|
||||
# # Create DataFrames
|
||||
# static_df = pd.DataFrame([static_data])
|
||||
# hourly_df = pd.DataFrame(hourly_data)
|
||||
#
|
||||
# return static_df, hourly_df
|
||||
# for building in city.buildings:
|
||||
# print(building.heating_demand[cte.YEAR][0] / 3.6e6)
|
||||
# print(building.name)
|
||||
# total_floor_area = 0
|
||||
# for thermal_zone in building.thermal_zones_from_internal_zones:
|
||||
# total_floor_area += thermal_zone.total_floor_area
|
||||
# print(building.heating_demand[cte.YEAR][0] / (3.6e6 * total_floor_area))
|
||||
|
||||
|
||||
# output_path_walls_id_dic =output_path / 'walls_id_data.xlsx'
|
||||
#
|
||||
# static_df, hourly_df = convert_walls_id_to_dfs(walls_id)
|
||||
# with pd.ExcelWriter(output_path_walls_id_dic) as writer:
|
||||
# static_df.to_excel(writer, sheet_name='Static Data', index=False)
|
||||
# hourly_df.to_excel(writer, sheet_name='Hourly Data', index=False)
|
||||
|
||||
# print(f"Data successfully exported to {output_path}")
|
||||
# # Save the DataFrame to an Excel file
|
||||
|
||||
|
||||
|
||||
df.to_csv(output_path / 'pv.csv')
|
||||
|
||||
scenario['efficient with PV']=buildings_dic
|
||||
print("Scenario 2: efficient with PV run successfully")
|
||||
|
||||
# #%%-----------------------------------------------
|
||||
# # Scenario 3
|
||||
# #%% # -----------------------------------------------
|
||||
#
|
||||
# for building in city.buildings:
|
||||
# building.energy_systems_archetype_name = 'PV+4Pipe+DHW'
|
||||
# EnergySystemsFactory('montreal_future', city).enrich()
|
||||
# buildings_dic = {}
|
||||
# for building in city.buildings:
|
||||
# EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich()
|
||||
# buildings_dic[building.name] = to_dict(building, hourly_pv)
|
||||
# scenario['efficient with PV+4Pipe+DHW']=buildings_dic
|
||||
# print("Scenario 3: efficient with PV+4Pipe+DHW run successfully")
|
||||
#
|
||||
# def extract_HP_size(building):
|
||||
# dic={
|
||||
# # Heat Pump Rated Heating and Cooling Output
|
||||
# 'hp_heat_size': building.energy_systems[1].generation_systems[1].nominal_heat_output/1000,
|
||||
# 'hp_cooling_output': building.energy_systems[1].generation_systems[1].nominal_cooling_output/1000,
|
||||
# # Boiler Rated Heat Output
|
||||
# 'boiler_heat_output': building.energy_systems[1].generation_systems[0].nominal_heat_output/1000,
|
||||
# # TES characteristics
|
||||
# 'tes_volume':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].volume,
|
||||
# 'tes_height':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].height,
|
||||
# # DHW HP
|
||||
# 'dhw_hp_heat_output': building.energy_systems[-1].generation_systems[0].nominal_heat_output/1000,
|
||||
# # DHW TES Characteristics
|
||||
# 'dhw_tes_volume': building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].volume,
|
||||
# 'dhw_tes_height': building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].height,
|
||||
# }
|
||||
#
|
||||
#
|
||||
# return dic
|
||||
# HPs={}
|
||||
# for building in city.buildings:
|
||||
# HPs[building.name]=extract_HP_size(building)
|
||||
|
||||
clusters=pd.read_csv(output_path/'clusters.csv')
|
||||
|
||||
|
||||
# Step 2: Extract the demand data for each building
|
||||
def extract_building_demand(city):
|
||||
building_demand = {}
|
||||
for building in city.buildings:
|
||||
demands = {
|
||||
'heating_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.heating_demand[cte.HOUR]],
|
||||
'cooling_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.cooling_demand[cte.HOUR]],
|
||||
'domestic_hot_water_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.domestic_hot_water_heat_demand[cte.HOUR]],
|
||||
'appliances_electrical_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.appliances_electrical_demand[cte.HOUR]],
|
||||
'lighting_electrical_demand': [x / (1000 * cte.WATTS_HOUR_TO_JULES) for x in building.lighting_electrical_demand[cte.HOUR]]
|
||||
}
|
||||
building_demand[building.name] = demands
|
||||
return building_demand
|
||||
|
||||
# Step 3: Sum the demand types for each cluster
|
||||
def sum_demands_by_cluster(building_demand, clusters, demand_types):
|
||||
cluster_demands = {cluster: {demand_type: [0] * 8760 for demand_type in demand_types} for cluster in clusters['cluster'].unique()}
|
||||
|
||||
for _, row in clusters.iterrows():
|
||||
building_id = str(row['id'])
|
||||
cluster = row['cluster']
|
||||
if building_id in building_demand:
|
||||
for demand_type in demand_types:
|
||||
cluster_demands[cluster][demand_type] = [sum(x) for x in zip(cluster_demands[cluster][demand_type], building_demand[building_id][demand_type])]
|
||||
|
||||
return cluster_demands
|
||||
|
||||
|
||||
def plot_demands_by_cluster(cluster_demands, demand_types, output_folder):
|
||||
import os
|
||||
os.makedirs(output_folder, exist_ok=True)
|
||||
|
||||
for cluster, demands in cluster_demands.items():
|
||||
plt.figure(figsize=(15, 10))
|
||||
for demand_type in demand_types:
|
||||
plt.plot(demands[demand_type], label=demand_type)
|
||||
|
||||
plt.title(f'Summed Demands for Cluster {cluster}')
|
||||
plt.xlabel('Hour of the Year')
|
||||
plt.ylabel('Demand (kWh)')
|
||||
plt.legend(loc='upper right')
|
||||
plt.grid(True)
|
||||
plt.tight_layout()
|
||||
plt.savefig(os.path.join(output_folder, f'cluster_{cluster}_summed_demands.png'))
|
||||
plt.close()
|
||||
|
||||
|
||||
# Example usage
|
||||
demand_types = [
|
||||
'heating_demand',
|
||||
'cooling_demand',
|
||||
'domestic_hot_water_demand',
|
||||
'appliances_electrical_demand',
|
||||
'lighting_electrical_demand'
|
||||
]
|
||||
|
||||
# Extract the building demand data
|
||||
building_demand = extract_building_demand(city)
|
||||
cluster_demands = sum_demands_by_cluster(building_demand, clusters, demand_types)
|
||||
# Create a DataFrame to export the results
|
||||
cluster_demands_df = {f"{cluster}_{demand_type}": data for cluster, demands in cluster_demands.items() for
|
||||
demand_type, data in demands.items()}
|
||||
cluster_demands_df = pd.DataFrame(cluster_demands_df)
|
||||
|
||||
# Save the results to an Excel file
|
||||
|
||||
cluster_demands_df.to_excel(output_path/'cluster_demands.xlsx', index=False)
|
||||
|
||||
print(f"Clustered demand data successfully exported to {output_path}")
|
||||
|
||||
|
||||
|
||||
#%%-----------------------------------------------
|
||||
# Scenario 4
|
||||
#%% # -----------------------------------------------
|
||||
|
||||
del city
|
||||
del buildings_dic
|
||||
|
||||
|
||||
geojson_file_path_clusters= output_path / 'new.geojson'
|
||||
|
||||
with open(geojson_file_path_clusters , 'r') as f:
|
||||
building_type_data_new = json.load(f)
|
||||
|
||||
# Create city object from GeoJSON file
|
||||
city = GeometryFactory('geojson',
|
||||
path=geojson_file_path_clusters,
|
||||
height_field='maximum_roof_height',
|
||||
year_of_construction_field='year_built',
|
||||
function_field='building_type',
|
||||
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
|
||||
#%%-----------------------------------------------
|
||||
# Enrich city data
|
||||
#%% # -----------------------------------------------
|
||||
ConstructionFactory('nrcan', city).enrich()
|
||||
UsageFactory('nrcan', city).enrich()
|
||||
WeatherFactory('epw', city).enrich()
|
||||
|
||||
buildings_clusters={
|
||||
1651: 4,
|
||||
1662: 0,
|
||||
1667: 1,
|
||||
1674: 2,
|
||||
1688: 3
|
||||
}
|
||||
|
||||
for building_id in buildings_clusters:
|
||||
cluster=buildings_clusters[building_id]
|
||||
for idx, feature in enumerate(building_type_data_new['features']):
|
||||
if feature['properties']['id'] == str(building_id):
|
||||
building_type_data_new['features'][idx]['properties']['heating_demand_kWh']=cluster_demands[cluster]['heating_demand']
|
||||
building_type_data_new['features'][idx]['properties']['cooling_demand_kWh'] = cluster_demands[cluster]['cooling_demand']
|
||||
building_type_data_new['features'][idx]['properties']['domestic_hot_water_heat_demand_kWh'] = cluster_demands[cluster]['domestic_hot_water_demand']
|
||||
building_type_data_new['features'][idx]['properties']['appliances_electrical_demand_kWh'] = cluster_demands[cluster]['appliances_electrical_demand']
|
||||
building_type_data_new['features'][idx]['properties']['lighting_electrical_demand_kWh'] = cluster_demands[cluster]['lighting_electrical_demand']
|
||||
|
||||
enrich_buildings_with_geojson_data (building_type_data_new, city)
|
||||
|
||||
|
||||
for building in city.buildings:
|
||||
building.energy_systems_archetype_name = 'PV+4Pipe+DHW'
|
||||
EnergySystemsFactory('montreal_future', city).enrich()
|
||||
buildings_dic = {}
|
||||
for building in city.buildings:
|
||||
EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich()
|
||||
buildings_dic[building.name] = to_dict(building, hourly_pv)
|
||||
scenario['efficient with PV+4Pipe+DHW']=buildings_dic
|
||||
print("Scenario 4: efficient with PV+4Pipe+DHW run successfully for Clusters")
|
||||
|
||||
def extract_HP_size(building):
|
||||
dic={
|
||||
# Heat Pump Rated Heating and Cooling Output
|
||||
'hp_heat_size': building.energy_systems[1].generation_systems[1].nominal_heat_output/1000,
|
||||
'hp_cooling_output': building.energy_systems[1].generation_systems[1].nominal_cooling_output/1000,
|
||||
# Boiler Rated Heat Output
|
||||
'boiler_heat_output': building.energy_systems[1].generation_systems[0].nominal_heat_output/1000,
|
||||
# TES characteristics
|
||||
'tes_volume':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].volume,
|
||||
'tes_height':building.energy_systems[1].generation_systems[0].energy_storage_systems[0].height,
|
||||
# DHW HP
|
||||
'dhw_hp_heat_output': building.energy_systems[-1].generation_systems[0].nominal_heat_output/1000,
|
||||
# DHW TES Characteristics
|
||||
'dhw_tes_volume': building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].volume,
|
||||
'dhw_tes_height': building.energy_systems[-1].generation_systems[0].energy_storage_systems[0].height,
|
||||
}
|
||||
|
||||
|
||||
return dic
|
||||
HPs={}
|
||||
for building in city.buildings:
|
||||
HPs[building.name]=extract_HP_size(building)
|
||||
|
||||
#%%-------------------------------------------------------
|
||||
#""""EXPORTERS"""
|
||||
#%%-------------------------------------------------------
|
||||
|
||||
|
||||
# Convert the dictionary to a DataFrame
|
||||
df = pd.DataFrame.from_dict(HPs, orient='index')
|
||||
|
||||
# Save the DataFrame to an Excel file
|
||||
output_path_HPs =output_path/ 'HPs_data_sc4.xlsx'
|
||||
df.to_excel(output_path_HPs, index_label='building_id')
|
||||
|
||||
print(f"Data successfully exported to {output_path}")
|
||||
|
||||
|
||||
|
||||
#%%-------------------------------------------------------
|
||||
#""""EXPORTERS"""
|
||||
#%%-------------------------------------------------------
|
||||
|
||||
|
||||
# Convert the dictionary to a DataFrame
|
||||
df = pd.DataFrame.from_dict(HPs, orient='index')
|
||||
|
||||
# Save the DataFrame to an Excel file
|
||||
output_path_HPs =output_path/ 'HPs_data.xlsx'
|
||||
df.to_excel(output_path_HPs, index_label='building_id')
|
||||
|
||||
print(f"Data successfully exported to {output_path}")
|
||||
|
||||
import pandas as pd
|
||||
|
||||
districts_demands={}
|
||||
def extract_and_sum_demand_data(scenario, demand_types):
|
||||
|
||||
|
||||
# Conversion factor constant
|
||||
conversion_factor = 1 / (cte.WATTS_HOUR_TO_JULES * 1000)
|
||||
|
||||
# Loop through each scenario
|
||||
for scenario_key, buildings in scenario.items():
|
||||
# Loop through each building in the scenario
|
||||
# Initialize an empty dictionary to store the district demand sums
|
||||
district_demand = {demand_type: [0] * 8760 for demand_type in demand_types}
|
||||
district_demand['hourly_pv_kWh']= [0] * 8760
|
||||
for building_id, building_data in buildings.items():
|
||||
# Loop through each demand type and sum up the data
|
||||
for demand_type in demand_types:
|
||||
if demand_type in building_data:
|
||||
district_demand[demand_type] = [sum(x) for x in zip(district_demand[demand_type], building_data[demand_type])]
|
||||
|
||||
# If PV data is available and relevant
|
||||
if scenario_key == "efficient with PV":
|
||||
district_demand['hourly_pv_kWh'] = [sum(x) for x in zip(district_demand['hourly_pv_kWh'], building_data['hourly_pv_kWh'])]
|
||||
if scenario_key == 'efficient with PV+4Pipe+DHW':
|
||||
district_demand['hourly_pv_kWh'] = districts_demands["efficient with PV"]['hourly_pv_kWh']
|
||||
districts_demands[scenario_key]=district_demand
|
||||
|
||||
return districts_demands
|
||||
|
||||
# Example usage
|
||||
# Assuming 'scenario' is a dictionary with the required structure and 'cte' is defined somewhere with WATTS_HOUR_TO_JULES constant
|
||||
demand_types = [
|
||||
'heating_consumption_kWh',
|
||||
'cooling_consumption_kWh',
|
||||
'domestic_hot_water_consumption_kWh',
|
||||
'appliances_consumption_kWh',
|
||||
'lighting_consumption_kWh',
|
||||
# 'hourly_pv_kWh' # Include this only if you want to consider PV data
|
||||
]
|
||||
|
||||
# # Call the function with your scenario data
|
||||
district_demand = extract_and_sum_demand_data(scenario, demand_types)
|
||||
#
|
||||
# """"EXPORTERS"""
|
||||
# import pandas as pd
|
||||
#
|
||||
#
|
||||
# Export the DataFrame to an Excel file
|
||||
excel_file_path = r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\districts_balance.xlsx'
|
||||
# df.to_excel(excel_file_path, index=True, index_label='Building')
|
||||
|
||||
# Create an Excel writer object
|
||||
with pd.ExcelWriter(excel_file_path, engine='xlsxwriter') as writer:
|
||||
for scenarios,demands in district_demand.items():
|
||||
# Convert demands to a DataFrame
|
||||
df_demands = pd.DataFrame(demands)
|
||||
# Convert building_id to string and check its length
|
||||
sheet_name = str(scenarios)
|
||||
if len(sheet_name) > 31:
|
||||
sheet_name = sheet_name[:31] # Truncate to 31 characters if necessary
|
||||
# Write the DataFrame to a specific sheet named after the building_id
|
||||
df_demands.to_excel(writer, sheet_name=sheet_name, index=False)
|
||||
|
||||
|
||||
print("district balance data is exported successfully")
|
||||
|
||||
|
||||
import pandas as pd
|
||||
|
||||
# Assuming your scenario dictionary is already defined as follows:
|
||||
# scenario = {
|
||||
# 'baseline': { ... },
|
||||
# 'efficient with PV': { ... }
|
||||
# }
|
||||
|
||||
|
||||
def dict_to_df_col_wise(building_data):
|
||||
"""
|
||||
Converts a dictionary of building data to a DataFrame.
|
||||
|
||||
Args:
|
||||
building_data (dict): Dictionary containing building data where keys are building ids and values are dictionaries
|
||||
with hourly data for various demand types.
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: DataFrame with columns for each building and demand type.
|
||||
"""
|
||||
# Create a dictionary to hold DataFrames for each demand type
|
||||
df_dict= {}
|
||||
|
||||
# Loop over each building
|
||||
for building_id, data in building_data.items():
|
||||
# Create a DataFrame for this building's data
|
||||
building_df = pd.DataFrame(data)
|
||||
|
||||
# Rename columns to include building_id
|
||||
building_df.columns = [f"{building_id}_{col}" for col in building_df.columns]
|
||||
|
||||
# Add this DataFrame to the dictionary
|
||||
df_dict[building_id] = building_df
|
||||
|
||||
# Concatenate all building DataFrames column-wise
|
||||
result_df = pd.concat(df_dict.values(), axis=1)
|
||||
|
||||
return result_df
|
||||
|
||||
# Create DataFrames for each scenario
|
||||
baseline_df = dict_to_df_col_wise(scenario['baseline'])
|
||||
efficient_with_pv_df = dict_to_df_col_wise(scenario['efficient with PV'])
|
||||
efficient_with_pv_hps = dict_to_df_col_wise(scenario['efficient with PV+4Pipe+DHW'])
|
||||
|
||||
|
||||
# Write the DataFrames to an Excel file with two separate sheets
|
||||
with pd.ExcelWriter(r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\scenario_data.xlsx') as writer:
|
||||
baseline_df.to_excel(writer, sheet_name='baseline', index=True)
|
||||
efficient_with_pv_df.to_excel(writer, sheet_name='efficient with PV', index=True)
|
||||
efficient_with_pv_hps.to_excel(writer, sheet_name='efficient with HPs_2', index=True)
|
||||
|
||||
print("hourly data has been successfully exported per building to scenario_data.xlsx")
|
||||
|
||||
|
||||
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
|
||||
def convert_hourly_to_monthly(hourly_data):
|
||||
"""
|
||||
Converts hourly data to monthly data by summing up the values for each month.
|
||||
|
||||
Args:
|
||||
hourly_data (list): List of hourly data (length 8760).
|
||||
|
||||
Returns:
|
||||
list: List of monthly data (length 12).
|
||||
"""
|
||||
hourly_series = pd.Series(hourly_data, index=pd.date_range(start='1/1/2023', periods=8760, freq='H'))
|
||||
monthly_data = hourly_series.resample('M').sum()
|
||||
return monthly_data.tolist()
|
||||
|
||||
import os
|
||||
def plot_stacked_demands_vs_pv(district_demand, demand_types, output_path, pv_type='hourly_pv_kWh'):
|
||||
"""
|
||||
Plots the stacked monthly demand for each scenario and compares it to the PV data.
|
||||
|
||||
Args:
|
||||
district_demand (dict): Dictionary with scenario keys and demand data.
|
||||
demand_types (list): List of demand types to plot.
|
||||
output_path (str): Path to save the plots.
|
||||
pv_type (str): The PV data type to compare against.
|
||||
"""
|
||||
os.makedirs(output_path, exist_ok=True)
|
||||
|
||||
for scenario_key, demand_data in district_demand.items():
|
||||
# Convert hourly data to monthly data for each demand type
|
||||
monthly_data = {demand_type: convert_hourly_to_monthly(demand_data[demand_type]) for demand_type in
|
||||
demand_types}
|
||||
monthly_pv = convert_hourly_to_monthly(demand_data.get(pv_type, [0] * 8760))
|
||||
|
||||
# Create a DataFrame for easier plotting
|
||||
combined_data = pd.DataFrame(monthly_data)
|
||||
combined_data['Month'] = range(1, 13)
|
||||
combined_data['PV'] = monthly_pv
|
||||
|
||||
# Plotting
|
||||
fig, ax1 = plt.subplots(figsize=(14, 8))
|
||||
|
||||
# Plot stacked demands
|
||||
combined_data.set_index('Month', inplace=True)
|
||||
combined_data[demand_types].plot(kind='bar', stacked=True, ax=ax1, colormap='tab20')
|
||||
|
||||
ax1.set_xlabel('Month')
|
||||
ax1.set_ylabel('Energy Demand (kWh)')
|
||||
ax1.set_title(f'Monthly Energy Demand and PV Generation for {scenario_key}')
|
||||
|
||||
# Plot PV data on the secondary y-axis
|
||||
ax2 = ax1.twinx()
|
||||
ax2.plot(combined_data.index, combined_data['PV'], color='black', linestyle='-', marker='o',
|
||||
label='PV Generation')
|
||||
ax2.set_ylabel('PV Generation (kWh)')
|
||||
|
||||
# Add legends
|
||||
ax1.legend(loc='upper left')
|
||||
ax2.legend(loc='upper right')
|
||||
|
||||
ax1.set_xticks(combined_data.index)
|
||||
ax1.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
|
||||
|
||||
# Save the plot
|
||||
plt.savefig(os.path.join(output_path, f'{scenario_key}_monthly_demand_vs_pv.png'))
|
||||
plt.close()
|
||||
|
||||
|
||||
# Example usage
|
||||
# district_demand = extract_and_sum_demand_data(scenario, demand_types)
|
||||
|
||||
# Specify the demand types and PV type
|
||||
demand_types = [
|
||||
'heating_consumption_kWh',
|
||||
'cooling_consumption_kWh',
|
||||
'domestic_hot_water_consumption_kWh',
|
||||
'appliances_consumption_kWh',
|
||||
'lighting_consumption_kWh'
|
||||
]
|
||||
|
||||
# Plot the data
|
||||
plot_stacked_demands_vs_pv(district_demand, demand_types, output_path)
|
||||
# Plot the data
|
||||
print('test')
|
||||
import csv
|
||||
# EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich()
|
||||
10
processor.py
10
processor.py
|
|
@ -3,18 +3,16 @@ from pathlib import Path
|
|||
import pandas as pd
|
||||
import geopandas as gpd
|
||||
|
||||
#%%-----------------------------------------------
|
||||
# """This code takes different results from energy plus with different building uses and obtains a unique geojson that combines all of them"""
|
||||
#%% # -----------------------------------------------
|
||||
|
||||
#define output path
|
||||
output_path = (Path(__file__).parent / 'out_files').resolve()
|
||||
output_path.mkdir(parents=True, exist_ok=True)
|
||||
# Define output folders
|
||||
output_folders = ['building_type', 'building_type_2', 'building_type_3']
|
||||
#output paths containing energy+ results are read
|
||||
path1=output_path / '1990_and_2019'/'building_type' / 'updated_buildings.geojson'
|
||||
path2=output_path / '1990_and_2019'/ 'building_type_2' / 'updated_buildings.geojson'
|
||||
path3=output_path / '1990_and_2019'/'building_type_3' / 'updated_buildings.geojson'
|
||||
path1=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type\updated_buildings.geojson'
|
||||
path2=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type_2\updated_buildings.geojson'
|
||||
path3=r'C:\Users\a_gabald\PycharmProjects\summer_course_2024\out_files\building_type_3\updated_buildings.geojson'
|
||||
with open(path1, 'r') as f:
|
||||
building_type_data = json.load(f)
|
||||
with open(path2, 'r') as f:
|
||||
|
|
|
|||
|
|
@ -4,8 +4,8 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
|
|||
Copyright © 2022 Concordia CERC group
|
||||
Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
|
||||
"""
|
||||
from scripts.system_simulation_models.archetype13_electric import Archetype13
|
||||
# from scripts.system_simulation_models.archetype13 import Archetype13
|
||||
|
||||
from scripts.system_simulation_models.archetype13 import Archetype13
|
||||
from scripts.system_simulation_models.archetype13_stratified_tes import Archetype13Stratified
|
||||
from scripts.system_simulation_models.archetype1 import Archetype1
|
||||
from scripts.system_simulation_models.archetypes14_15 import Archetype14_15
|
||||
|
|
|
|||
|
|
@ -16,8 +16,7 @@ class Archetype1:
|
|||
self._domestic_hot_water_peak_load = building.domestic_hot_water_peak_load[cte.YEAR][0]
|
||||
self._hourly_heating_demand = [0] + [demand / 3600 for demand in building.heating_demand[cte.HOUR]]
|
||||
self._hourly_cooling_demand = [demand / 3600 for demand in building.cooling_demand[cte.HOUR]]
|
||||
self._hourly_dhw_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in
|
||||
building.domestic_hot_water_heat_demand[cte.HOUR]]
|
||||
self._hourly_dhw_demand = building.domestic_hot_water_heat_demand[cte.HOUR]
|
||||
self._output_path = output_path
|
||||
self._t_out = [0] + building.external_temperature[cte.HOUR]
|
||||
self.results = {}
|
||||
|
|
@ -28,9 +27,9 @@ class Archetype1:
|
|||
heat_pump = self._hvac_system.generation_systems[0]
|
||||
boiler = self._hvac_system.generation_systems[1]
|
||||
thermal_storage = heat_pump.energy_storage_systems[0]
|
||||
heat_pump.nominal_heat_output = round(0.5 * self._heating_peak_load)
|
||||
heat_pump.nominal_cooling_output = round(self._cooling_peak_load)
|
||||
boiler.nominal_heat_output = round(0.5 * self._heating_peak_load)
|
||||
heat_pump.nominal_heat_output = round(0.5 * self._heating_peak_load / 3600)
|
||||
heat_pump.nominal_cooling_output = round(self._cooling_peak_load / 3600)
|
||||
boiler.nominal_heat_output = round(0.5 * self._heating_peak_load / 3600)
|
||||
thermal_storage.volume = round(
|
||||
(self._heating_peak_load * storage_factor * cte.WATTS_HOUR_TO_JULES) /
|
||||
(cte.WATER_HEAT_CAPACITY * cte.WATER_DENSITY * 25))
|
||||
|
|
@ -61,7 +60,7 @@ class Archetype1:
|
|||
(t_sup_hp, t_tank, t_ret, m_ch, m_dis, q_hp, q_boiler, hp_cop,
|
||||
hp_electricity, boiler_gas, boiler_consumption, t_sup_boiler, heating_consumption) = [variables[name] for name in
|
||||
variable_names]
|
||||
t_tank[0] = 30
|
||||
t_tank[0] = 55
|
||||
dt = 3600
|
||||
hp_heating_cap = hp.nominal_heat_output
|
||||
hp_efficiency = float(hp.heat_efficiency)
|
||||
|
|
|
|||
|
|
@ -22,13 +22,12 @@ class Archetype13:
|
|||
self._hourly_dhw_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in
|
||||
building.domestic_hot_water_heat_demand[cte.HOUR]]
|
||||
self._output_path = output_path
|
||||
self._t_out = [15]*8760
|
||||
# building.external_temperature[cte.HOUR]
|
||||
self._t_out = building.external_temperature[cte.HOUR]
|
||||
self.results = {}
|
||||
self.dt = 900
|
||||
|
||||
def hvac_sizing(self,storage_factor=3):
|
||||
storage_factor = storage_factor
|
||||
def hvac_sizing(self):
|
||||
storage_factor = 3
|
||||
heat_pump = self._hvac_system.generation_systems[1]
|
||||
boiler = self._hvac_system.generation_systems[0]
|
||||
thermal_storage = boiler.energy_storage_systems[0]
|
||||
|
|
@ -40,8 +39,8 @@ class Archetype13:
|
|||
(cte.WATER_HEAT_CAPACITY * cte.WATER_DENSITY * 25))
|
||||
return heat_pump, boiler, thermal_storage
|
||||
|
||||
def dhw_sizing(self,storage_factor=3):
|
||||
storage_factor = storage_factor
|
||||
def dhw_sizing(self):
|
||||
storage_factor = 3
|
||||
dhw_hp = self._dhw_system.generation_systems[0]
|
||||
dhw_hp.nominal_heat_output = 0.7 * self._domestic_hot_water_peak_load
|
||||
dhw_hp.source_temperature = self._t_out
|
||||
|
|
@ -52,7 +51,7 @@ class Archetype13:
|
|||
|
||||
def heating_system_simulation(self):
|
||||
hp, boiler, tes = self.hvac_sizing()
|
||||
# cop_curve_coefficients = [float(coefficient) for coefficient in hp.heat_efficiency_curve.coefficients]
|
||||
cop_curve_coefficients = [float(coefficient) for coefficient in hp.heat_efficiency_curve.coefficients]
|
||||
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
|
||||
demand = [0] + [x for x in self._hourly_heating_demand for _ in range(number_of_ts)]
|
||||
t_out = [0] + [x for x in self._t_out for _ in range(number_of_ts)]
|
||||
|
|
@ -103,13 +102,12 @@ class Archetype13:
|
|||
t_tank_fahrenheit = 1.8 * t_tank[i + 1] + 32
|
||||
t_out_fahrenheit = 1.8 * t_out[i + 1] + 32
|
||||
if q_hp[i + 1] > 0:
|
||||
hp_cop[i + 1] =4.07
|
||||
# (1 / (cop_curve_coefficients[0] +
|
||||
# cop_curve_coefficients[1] * t_tank_fahrenheit +
|
||||
# cop_curve_coefficients[2] * t_tank_fahrenheit ** 2 +
|
||||
# cop_curve_coefficients[3] * t_out_fahrenheit +
|
||||
# cop_curve_coefficients[4] * t_out_fahrenheit ** 2 +
|
||||
# cop_curve_coefficients[5] * t_tank_fahrenheit * t_out_fahrenheit)) * hp_efficiency
|
||||
hp_cop[i + 1] = (1 / (cop_curve_coefficients[0] +
|
||||
cop_curve_coefficients[1] * t_tank_fahrenheit +
|
||||
cop_curve_coefficients[2] * t_tank_fahrenheit ** 2 +
|
||||
cop_curve_coefficients[3] * t_out_fahrenheit +
|
||||
cop_curve_coefficients[4] * t_out_fahrenheit ** 2 +
|
||||
cop_curve_coefficients[5] * t_tank_fahrenheit * t_out_fahrenheit)) * hp_efficiency
|
||||
hp_electricity[i + 1] = q_hp[i + 1] / hp_cop[i + 1]
|
||||
else:
|
||||
hp_cop[i + 1] = 0
|
||||
|
|
@ -121,7 +119,7 @@ class Archetype13:
|
|||
elif demand[i + 1] > 0.5 * self._heating_peak_load / self.dt:
|
||||
q_boiler[i + 1] = 0.5 * boiler_heating_cap
|
||||
boiler_energy_consumption[i + 1] = q_boiler[i + 1] / boiler_efficiency
|
||||
# boiler_gas_consumption[i + 1] = (q_boiler[i + 1] * self.dt) / (boiler_efficiency * cte.NATURAL_GAS_LHV)
|
||||
boiler_gas_consumption[i + 1] = (q_boiler[i + 1] * self.dt) / (boiler_efficiency * cte.NATURAL_GAS_LHV)
|
||||
t_sup_boiler[i + 1] = t_sup_hp[i + 1] + (q_boiler[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY))
|
||||
# storage discharging
|
||||
if demand[i + 1] == 0:
|
||||
|
|
@ -180,7 +178,7 @@ class Archetype13:
|
|||
|
||||
def cooling_system_simulation(self):
|
||||
hp = self.hvac_sizing()[0]
|
||||
# eer_curve_coefficients = [float(coefficient) for coefficient in hp.cooling_efficiency_curve.coefficients]
|
||||
eer_curve_coefficients = [float(coefficient) for coefficient in hp.cooling_efficiency_curve.coefficients]
|
||||
cooling_efficiency = float(hp.cooling_efficiency)
|
||||
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
|
||||
demand = [0] + [x for x in self._hourly_cooling_demand for _ in range(number_of_ts)]
|
||||
|
|
@ -218,13 +216,12 @@ class Archetype13:
|
|||
t_sup_hp_fahrenheit = 1.8 * t_sup_hp[i] + 32
|
||||
t_out_fahrenheit = 1.8 * t_out[i] + 32
|
||||
if q_hp[i] > 0:
|
||||
hp_cop[i] = 4
|
||||
# (1 / (eer_curve_coefficients[0] +
|
||||
# eer_curve_coefficients[1] * t_sup_hp_fahrenheit +
|
||||
# eer_curve_coefficients[2] * t_sup_hp_fahrenheit ** 2 +
|
||||
# eer_curve_coefficients[3] * t_out_fahrenheit +
|
||||
# eer_curve_coefficients[4] * t_out_fahrenheit ** 2 +
|
||||
# eer_curve_coefficients[5] * t_sup_hp_fahrenheit * t_out_fahrenheit)) * cooling_efficiency / 3.41
|
||||
hp_cop[i] = (1 / (eer_curve_coefficients[0] +
|
||||
eer_curve_coefficients[1] * t_sup_hp_fahrenheit +
|
||||
eer_curve_coefficients[2] * t_sup_hp_fahrenheit ** 2 +
|
||||
eer_curve_coefficients[3] * t_out_fahrenheit +
|
||||
eer_curve_coefficients[4] * t_out_fahrenheit ** 2 +
|
||||
eer_curve_coefficients[5] * t_sup_hp_fahrenheit * t_out_fahrenheit)) * cooling_efficiency / 3.41
|
||||
hp_electricity[i] = q_hp[i] / cooling_efficiency
|
||||
else:
|
||||
hp_cop[i] = 0
|
||||
|
|
@ -254,7 +251,7 @@ class Archetype13:
|
|||
|
||||
def dhw_system_simulation(self):
|
||||
hp, tes = self.dhw_sizing()
|
||||
# cop_curve_coefficients = [float(coefficient) for coefficient in hp.heat_efficiency_curve.coefficients]
|
||||
cop_curve_coefficients = [float(coefficient) for coefficient in hp.heat_efficiency_curve.coefficients]
|
||||
number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
|
||||
demand = [0] + [x for x in self._hourly_dhw_demand for _ in range(number_of_ts)]
|
||||
t_out = [0] + [x for x in self._t_out for _ in range(number_of_ts)]
|
||||
|
|
@ -301,13 +298,12 @@ class Archetype13:
|
|||
t_sup_hp_fahrenheit = 1.8 * t_sup_hp[i] + 32
|
||||
t_out_fahrenheit = 1.8 * t_out[i] + 32
|
||||
if q_hp[i] > 0:
|
||||
hp_cop[i] = 3
|
||||
# (cop_curve_coefficients[0] +
|
||||
# cop_curve_coefficients[1] * t_out[i] +
|
||||
# cop_curve_coefficients[2] * t_out[i] ** 2 +
|
||||
# cop_curve_coefficients[3] * t_tank[i] +
|
||||
# cop_curve_coefficients[4] * t_tank[i] ** 2 +
|
||||
# cop_curve_coefficients[5] * t_tank[i] * t_out[i]) * float(hp.heat_efficiency)
|
||||
hp_cop[i] = (cop_curve_coefficients[0] +
|
||||
cop_curve_coefficients[1] * t_out[i] +
|
||||
cop_curve_coefficients[2] * t_out[i] ** 2 +
|
||||
cop_curve_coefficients[3] * t_tank[i] +
|
||||
cop_curve_coefficients[4] * t_tank[i] ** 2 +
|
||||
cop_curve_coefficients[5] * t_tank[i] * t_out[i]) * float(hp.heat_efficiency)
|
||||
hp_electricity[i] = q_hp[i] / hp_cop[i]
|
||||
else:
|
||||
hp_cop[i] = 0
|
||||
Loading…
Reference in New Issue
Block a user