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feat: energy community assessments completed

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This commit is contained in:
Saeed Ranjbar 2024-12-11 22:25:53 +01:00
parent ee9dd58f82
commit 00e2a9d9bf
12 changed files with 12956 additions and 15 deletions
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12
energy_system_modelling_package/random_assignation.py
View File

@ -29,7 +29,7 @@ residential_systems_percentage = {'system 1 gas': 15,
'system 8 electricity': 35}
residential_new_systems_percentage = {
'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 100,
'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
@ -43,7 +43,7 @@ residential_new_systems_percentage = {
'Central Hydronic Ground and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Water and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Central Hydronic Water and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
'Grid Tied PV System': 0,
'Grid Tied PV System': 100,
'system 1 gas': 0,
'system 1 gas grid tied pv': 0,
'system 1 electricity': 0,
@ -70,12 +70,12 @@ non_residential_systems_percentage = {'system 1 gas': 0,
'system 1 electricity': 0,
'system 2 gas': 0,
'system 2 electricity': 0,
'system 3 and 4 gas': 39,
'system 3 and 4 electricity': 36,
'system 3 and 4 gas': 50,
'system 3 and 4 electricity': 0,
'system 5 gas': 0,
'system 5 electricity': 0,
'system 6 gas': 13,
'system 6 electricity': 12,
'system 6 gas': 50,
'system 6 electricity': 0,
'system 8 gas': 0,
'system 8 electricity': 0}

14
hub/data/energy_systems/montreal_future_systems.xml
View File

@ -449,19 +449,19 @@
<generation_system_id>12</generation_system_id>
<name>Photovoltaic Module</name>
<system_type>photovoltaic</system_type>
<model_name>445MS</model_name>
<model_name>CS7N650MS</model_name>
<manufacturer>Canadian Solar</manufacturer>
<nominal_electricity_output>332</nominal_electricity_output>
<electricity_efficiency>0.201</electricity_efficiency>
<nominal_electricity_output>487</nominal_electricity_output>
<electricity_efficiency>0.209</electricity_efficiency>
<nominal_ambient_temperature>20</nominal_ambient_temperature>
<nominal_cell_temperature>40</nominal_cell_temperature>
<nominal_cell_temperature>41</nominal_cell_temperature>
<nominal_radiation>800</nominal_radiation>
<standard_test_condition_cell_temperature>25</standard_test_condition_cell_temperature>
<standard_test_condition_radiation>1000</standard_test_condition_radiation>
<standard_test_condition_maximum_power>445</standard_test_condition_maximum_power>
<standard_test_condition_maximum_power>650</standard_test_condition_maximum_power>
<cell_temperature_coefficient>0.35</cell_temperature_coefficient>
<width>2.01</width>
<height>1.048</height>
<width>2.4</width>
<height>1.3</height>
<distribution_systems/>
<energy_storage_systems/>
</pv_generation_component>

6
hub/imports/usage/comnet_usage_parameters.py
View File

@ -39,7 +39,11 @@ class ComnetUsageParameters:
comnet_archetype_usages = []
building_functions = building.function.split('_')
for function in building_functions:
usages.append(function.split('-'))
try:
float(function.split('-')[0])
usages.append(function.split('-', maxsplit=1))
except ValueError:
usages.append([function])
for usage in usages:
comnet_usage_name = Dictionaries().hub_usage_to_comnet_usage[usage[-1]]
try:

6
hub/imports/usage/nrcan_usage_parameters.py
View File

@ -39,7 +39,11 @@ class NrcanUsageParameters:
comnet_archetype_usages = []
building_functions = building.function.split('_')
for function in building_functions:
usages.append(function.split('-'))
try:
float(function.split('-')[0])
usages.append(function.split('-', maxsplit=1))
except ValueError:
usages.append([function])
for usage in usages:
usage_name = Dictionaries().hub_usage_to_nrcan_usage[usage[-1]]
try:

715
input_files/1.geojson Normal file
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input_files/2.geojson Normal file
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input_files/selected_buildings_1.geojson Normal file
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132
main.py
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@ -0,0 +1,132 @@
from pathlib import Path
import pandas as pd
from building_modelling.ep_run_enrich import energy_plus_workflow
from hub.helpers.dictionaries import Dictionaries
from hub.imports.construction_factory import ConstructionFactory
from hub.imports.energy_systems_factory import EnergySystemsFactory
from hub.imports.geometry_factory import GeometryFactory
from hub.imports.usage_factory import UsageFactory
from hub.imports.weather_factory import WeatherFactory
from energy_system_modelling_package import random_assignation
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
import hub.helpers.constants as cte
import json
base_path = Path(__file__).parent.resolve()
input_files_path = base_path / 'input_files'
input_files_path.mkdir(parents=True, exist_ok=True)
output_files_path = base_path / 'out_files'
output_files_path.mkdir(exist_ok=True, parents=True)
energy_plus_output_path = output_files_path / 'energy_plus_outputs'
energy_plus_output_path.mkdir(parents=True, exist_ok=True)
geojson_path = input_files_path / 'selected_buildings.geojson'
city = GeometryFactory(file_type='geojson',
path=geojson_path,
aliases_field=['address'],
height_field='height',
year_of_construction_field='year_of_construction',
function_field='function',
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
for building in city.buildings:
if Dictionaries().hub_function_to_nrcan_construction_function[building.function] == 'n/a':
building.function = cte.WAREHOUSE
ConstructionFactory('nrcan', city).enrich()
UsageFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()
residential_buildings = []
non_residential_buildings = []
for building in city.buildings:
if building.function == cte.RESIDENTIAL:
residential_buildings.append(building)
else:
non_residential_buildings.append(building)
# random_assignation.call_random(residential_buildings, random_assignation.residential_systems_percentage)
# random_assignation.call_random(non_residential_buildings, random_assignation.non_residential_systems_percentage)
# EnergySystemsFactory('montreal_future', city).enrich()
base_results = pd.read_csv(output_files_path / 'results_summary.csv')
energy_system_archetypes = base_results['energy_system_archetype_name'].to_list()
for (i, building) in enumerate(city.buildings):
building.energy_systems_archetype_name = energy_system_archetypes[i]
EnergySystemsFactory('montreal_future', city).enrich()
energy_plus_workflow(city, energy_plus_output_path)
# hourly_simulation_results = {}
# hourly_elec_consumption = pd.DataFrame()
# hourly_lighting_appliance_consumption = pd.DataFrame()
# for building in city.buildings:
# hourly_electricity_consumption = HourlyElectricityDemand(building).calculate()
# heating_electricity_consumption = building.heating_consumption[
# cte.HOUR] if 'electricity' in building.energy_systems_archetype_name else [0] * len(
# building.heating_demand[cte.HOUR])
# dhw_electricity_consumption = building.domestic_hot_water_consumption[
# cte.HOUR] if 'electricity' in building.energy_systems_archetype_name else [0] * len(
# building.domestic_hot_water_heat_demand[cte.HOUR])
# if building.cooling_consumption[cte.YEAR][0] == 0:
# cooling_electricity_consumption = [building.cooling_demand[cte.HOUR][i] / (2 * 3.23) for i in
# range(len(building.cooling_consumption[cte.HOUR]))]
# hourly_electricity_consumption = [hourly_electricity_consumption[i] + cooling_electricity_consumption[i] for i in range(len(hourly_electricity_consumption))]
# else:
# cooling_electricity_consumption = building.cooling_consumption[cte.HOUR]
# lighting = building.lighting_electrical_demand[cte.HOUR]
# appliance = building.appliances_electrical_demand[cte.HOUR]
#
# results = pd.DataFrame()
# results['heating_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in
# heating_electricity_consumption]
# results['cooling_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in
# cooling_electricity_consumption]
# results['dhw_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in
# dhw_electricity_consumption]
# results['lighting_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in lighting]
#
# results['appliance_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in appliance]
# results['lighting_appliance_total_kWh'] = [(lighting[i] + appliance[i]) / (cte.WATTS_HOUR_TO_JULES * 1000)
# for i in range(len(lighting))]
# results['total_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in hourly_electricity_consumption]
# results.to_csv(output_files_path / 'paper_results' / 'individual_building_simulation_results' / f'{building.name}.csv')
# hourly_elec_consumption[f'{building.name}'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in hourly_electricity_consumption]
# hourly_lighting_appliance_consumption[f'{building.name}'] = [(lighting[i] + appliance[i]) / (cte.WATTS_HOUR_TO_JULES * 1000)
# for i in range(len(lighting))]
#
# hourly_elec_consumption.to_csv(output_files_path / 'paper_results' / 'total_hourly_electricity_consumption.csv')
# hourly_lighting_appliance_consumption.to_csv(output_files_path / 'paper_results' / 'total_lighting_appliance.csv')
building_names = [building.name for building in city.buildings]
building_addresses = [building.aliases[0] for building in city.buildings]
building_heights = [building.max_height for building in city.buildings]
buildings_total_floor_areas = [building.thermal_zones_from_internal_zones[0].total_floor_area
for building in city.buildings]
buildings_roof_areas = [building.roofs[0].perimeter_area for building in city.buildings]
building_energy_system_archetype_names = [building.energy_systems_archetype_name for building in city.buildings]
building_yearly_heating_electricity_consumptions = []
building_yearly_dhw_electricity_consumptions = []
for building in city.buildings:
if 'electricity' in building.energy_systems_archetype_name:
building_yearly_heating_electricity_consumptions.append(building.heating_consumption[cte.YEAR][0] /
(cte.WATTS_HOUR_TO_JULES * 1000))
building_yearly_dhw_electricity_consumptions.append(building.domestic_hot_water_consumption[cte.YEAR][0] /
(cte.WATTS_HOUR_TO_JULES * 1000))
else:
building_yearly_heating_electricity_consumptions.append(0)
building_yearly_dhw_electricity_consumptions.append(0)
building_yearly_cooling_consumptions = []
for building in city.buildings:
if building.cooling_consumption[cte.YEAR][0] == 0:
building_yearly_cooling_consumptions.append(building.cooling_demand[cte.YEAR][0] / (2 * 3.23 * cte.WATTS_HOUR_TO_JULES * 1000))
else:
building_yearly_cooling_consumptions.append(building.cooling_consumption[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000))
building_yearly_lighting_demands = [building.lighting_electrical_demand[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000)
for building in city.buildings]
building_yearly_appliance_demands = [building.appliances_electrical_demand[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000)
for building in city.buildings]
summary = pd.DataFrame()
summary['building_name'] = building_names
summary['building_address'] = building_addresses
summary['building_height_m'] = building_heights
summary['total_floor_area_m2'] = buildings_total_floor_areas
summary['roof_area_m2'] = buildings_roof_areas
summary['energy_system_archetype_name'] = building_energy_system_archetype_names
summary['yearly_heating_electricity_consumption_kWh'] = building_yearly_heating_electricity_consumptions
summary['yearly_cooling_electricity_consumption_kWh'] = building_yearly_cooling_consumptions
summary['yearly_dhw_electricity_consumption_kWh'] = building_yearly_dhw_electricity_consumptions
summary['yearly_lighting_consumption_kWh'] = building_yearly_lighting_demands
summary['yearly_appliance_consumption_kWh'] = building_yearly_appliance_demands
summary.to_csv(output_files_path / 'yearly_results.csv', index=False)

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from pathlib import Path
import subprocess
import pandas as pd
from energy_system_modelling_package import random_assignation
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_system_assessment import \
PvSystemAssessment
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_calculator import \
SolarCalculator
from hub.imports.energy_systems_factory import EnergySystemsFactory
from hub.imports.geometry_factory import GeometryFactory
from hub.helpers.dictionaries import Dictionaries
from hub.imports.construction_factory import ConstructionFactory
from hub.imports.usage_factory import UsageFactory
from hub.imports.weather_factory import WeatherFactory
from hub.imports.results_factory import ResultFactory
from hub.exports.exports_factory import ExportsFactory
import hub.helpers.constants as cte
# Define paths for input and output directories, ensuring directories are created if they do not exist
base_path = Path(__file__).parent.resolve()
input_files_path = base_path / 'input_files'
input_files_path.mkdir(parents=True, exist_ok=True)
output_files_path = base_path / 'out_files'
output_files_path.mkdir(exist_ok=True, parents=True)
energy_plus_output_path = output_files_path / 'energy_plus_outputs'
energy_plus_output_path.mkdir(parents=True, exist_ok=True)
geojson_path = input_files_path / 'selected_buildings.geojson'
sra_output_path = output_files_path / 'sra_outputs'
sra_output_path.mkdir(parents=True, exist_ok=True)
pv_assessment_path = output_files_path / 'pv_outputs'
pv_assessment_path.mkdir(parents=True, exist_ok=True)
# Generate a GeoJSON file for city buildings based on latitude, longitude, and building dimensions
# Initialize a city object from the geojson file, mapping building functions using a predefined dictionary
city = GeometryFactory(file_type='geojson',
path=geojson_path,
height_field='height',
year_of_construction_field='year_of_construction',
function_field='function',
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
# Enrich city data with construction, usage, and weather information specific to the location
for building in city.buildings:
if Dictionaries().hub_function_to_nrcan_construction_function[building.function] == 'n/a':
building.function = cte.WAREHOUSE
ConstructionFactory('nrcan', city).enrich()
UsageFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()
# Execute the EnergyPlus workflow to simulate building energy performance and generate output
# energy_plus_workflow(city, energy_plus_output_path)
# Export the city data in SRA-compatible format to facilitate solar radiation assessment
ExportsFactory('sra', city, sra_output_path).export()
# Run SRA simulation using an external command, passing the generated SRA XML file path as input
sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
subprocess.run(['sra', str(sra_path)])
# Enrich city data with SRA simulation results for subsequent analysis
ResultFactory('sra', city, sra_output_path).enrich()
# Assign PV system archetype name to the buildings in city
random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
# Enrich city model with Montreal future systems parameters
EnergySystemsFactory('montreal_future', city).enrich()
# # Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
tilt_angle = 37
solar_parameters = SolarCalculator(city=city,
surface_azimuth_angle=180,
tilt_angle=tilt_angle,
standard_meridian=-75)
solar_angles = solar_parameters.solar_angles # Obtain solar angles for further analysis
solar_parameters.tilted_irradiance_calculator() # Calculate the solar radiation on a tilted surface
# # PV modelling building by building
# List of available PV modules ['RE400CAA Pure 2', 'RE410CAA Pure 2', 'RE420CAA Pure 2', 'RE430CAA Pure 2',
# 'REC600AA Pro M', 'REC610AA Pro M', 'REC620AA Pro M', 'REC630AA Pro M', 'REC640AA Pro M']
building_names = []
hourly_pv_outputs = pd.DataFrame()
for building in city.buildings:
building_names.append(building.name)
pv_modeller = PvSystemAssessment(building=building,
pv_system=None,
battery=None,
tilt_angle=tilt_angle,
solar_angles=solar_angles,
pv_installation_type='rooftop',
simulation_model_type='explicit',
module_model_name='CS7N650MS',
inverter_efficiency=0.95,
system_catalogue_handler='montreal_future',
roof_percentage_coverage=0.75,
facade_coverage_percentage=0,
csv_output=False,
output_path=pv_assessment_path)
pv_modeller.enrich()
results = pv_modeller.results
pv_output = [x / 1000 for x in results['total_hourly_pv_system_output_W']]
hourly_pv_outputs[f'{building.name}'] = pv_output
hourly_pv_outputs.to_csv(output_files_path / 'paper_results' / 'hourly_pv_outputs.csv')

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