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6 changed files with 4104 additions and 244 deletions

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30
main.py
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@ -3,7 +3,6 @@ from pathlib import Path
from hub.imports.geometry_factory import GeometryFactory
from hub.imports.construction_factory import ConstructionFactory
from hub.imports.usage_factory import UsageFactory
from hub.imports.weather_factory import WeatherFactory
from hub.helpers.dictionaries import Dictionaries
from hub.imports.energy_systems_factory import EnergySystemsFactory
import hub.helpers.constants as cte
@ -13,14 +12,10 @@ from sra_engine import SraEngine
try:
file_path = (Path(__file__).parent / 'input_files' / '195_v1.geojson')
climate_reference_city = 'Montreal'
weather_format = 'epw'
file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
construction_format = 'nrcan'
usage_format = 'nrcan'
energy_systems_format = 'montreal_custom'
attic_heated_case = 0
basement_heated_case = 1
out_path = (Path(__file__).parent / 'output_files')
tmp_folder = (Path(__file__).parent / 'tmp')
@ -28,29 +23,30 @@ try:
print('[simulation start]')
city = GeometryFactory('geojson',
path=file_path,
height_field='heightmax',
year_of_construction_field='ANNEE_CONS',
function_field='CODE_UTILI',
height_field='height',
year_of_construction_field='year_of_construction',
function_field='function',
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
city.climate_reference_city = climate_reference_city
city.climate_file = (tmp_folder / f'{climate_reference_city}.cli').resolve()
print(f'city created from {file_path}')
WeatherFactory(weather_format, city).enrich()
print('enrich weather... done')
ConstructionFactory(construction_format, city).enrich()
print('enrich constructions... done')
UsageFactory(usage_format, city).enrich()
print('enrich usage... done')
i = 1
x = len(city.buildings)
for building in city.buildings:
building.energy_systems_archetype_name = 'system 1 gas pv'
if i < x:
building.energy_systems_archetype_name = 'system 1 gas'
else:
building.energy_systems_archetype_name = 'system 6 gas'
i = i + 1
EnergySystemsFactory(energy_systems_format, city).enrich()
print('enrich systems... done')
print('exporting:')
sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
SraEngine(city, sra_file, tmp_folder)
SraEngine(city, tmp_folder)
print(' sra processed...')
MonthlyEnergyBalanceEngine(city, tmp_folder)
print(' insel processed...')

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@ -9,68 +9,50 @@ class Results:
self._path = path
def print(self):
print_results = None
file = 'city name: ' + self._city.name + '\n'
array = [None] * 12
for building in self._city.buildings:
if cte.MONTH in building.heating_demand.keys():
heating_results = building.heating_demand[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} heating Wh'})
heating_results = building.heating_demand[cte.MONTH]
else:
heating_results = pd.DataFrame(array, columns=[f'{building.name} heating demand Wh'])
heating_results = [None] * 12
if cte.MONTH in building.cooling_demand.keys():
cooling_results = building.cooling_demand[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} cooling Wh'})
cooling_results = building.cooling_demand[cte.MONTH]
else:
cooling_results = pd.DataFrame(array, columns=[f'{building.name} cooling demand Wh'])
cooling_results = [None] * 12
if cte.MONTH in building.lighting_electrical_demand.keys():
lighting_results = building.lighting_electrical_demand[cte.MONTH]\
.rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'})
lighting_results = building.lighting_electrical_demand[cte.MONTH]
else:
lighting_results = pd.DataFrame(array, columns=[f'{building.name} lighting electrical demand Wh'])
lighting_results = [None] * 12
if cte.MONTH in building.appliances_electrical_demand.keys():
appliances_results = building.appliances_electrical_demand[cte.MONTH]\
.rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'})
appliances_results = building.appliances_electrical_demand[cte.MONTH]
else:
appliances_results = pd.DataFrame(array, columns=[f'{building.name} appliances electrical demand Wh'])
appliances_results = [None] * 12
if cte.MONTH in building.domestic_hot_water_heat_demand.keys():
dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]\
.rename(columns={cte.INSEL_MEB: f'{building.name} domestic hot water demand Wh'})
dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]
else:
dhw_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water demand Wh'])
dhw_results = [None] * 12
if cte.MONTH in building.heating_consumption.keys():
heating_consumption_results = pd.DataFrame(building.heating_consumption[cte.MONTH],
columns=[f'{building.name} heating consumption Wh'])
heating_consumption_results = building.heating_consumption[cte.MONTH]
else:
heating_consumption_results = pd.DataFrame(array, columns=[f'{building.name} heating consumption Wh'])
heating_consumption_results = [None] * 12
if cte.MONTH in building.cooling_consumption.keys():
cooling_consumption_results = pd.DataFrame(building.cooling_consumption[cte.MONTH],
columns=[f'{building.name} cooling consumption Wh'])
cooling_consumption_results = building.cooling_consumption[cte.MONTH]
else:
cooling_consumption_results = pd.DataFrame(array, columns=[f'{building.name} cooling consumption Wh'])
cooling_consumption_results = [None] * 12
if cte.MONTH in building.domestic_hot_water_consumption.keys():
dhw_consumption_results = pd.DataFrame(building.domestic_hot_water_consumption[cte.MONTH],
columns=[f'{building.name} domestic hot water consumption Wh'])
dhw_consumption_results = building.domestic_hot_water_consumption[cte.MONTH]
else:
dhw_consumption_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water consumption Wh'])
dhw_consumption_results = [None] * 12
if cte.MONTH in building.heating_peak_load.keys():
heating_peak_load_results = pd.DataFrame(building.heating_peak_load[cte.MONTH],
columns=[f'{building.name} heating peak load W'])
heating_peak_load_results = building.heating_peak_load[cte.MONTH]
else:
heating_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} heating peak load W'])
heating_peak_load_results = [None] * 12
if cte.MONTH in building.cooling_peak_load.keys():
cooling_peak_load_results = pd.DataFrame(building.cooling_peak_load[cte.MONTH],
columns=[f'{building.name} cooling peak load W'])
cooling_peak_load_results = building.cooling_peak_load[cte.MONTH]
else:
cooling_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} cooling peak load W'])
if cte.MONTH in building.onsite_electrical_production.keys():
monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH]
onsite_electrical_production = pd.DataFrame(monthly_onsite_electrical_production,
columns=[f'{building.name} onsite electrical production Wh'])
else:
onsite_electrical_production = pd.DataFrame(array, columns=[f'{building.name} onsite electrical production Wh'])
cooling_peak_load_results = [None] * 12
heating = 0
cooling = 0
for system in building.energy_systems:
@ -82,7 +64,7 @@ class Results:
if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
peak_lighting = 0
peak_appliances = 0
for thermal_zone in building.internal_zones[0].thermal_zones:
thermal_zone = building.thermal_zones_from_internal_zones[0]
lighting = thermal_zone.lighting
for schedule in lighting.schedules:
for value in schedule.values:
@ -103,170 +85,36 @@ class Results:
conditioning_peak.append(heating * value)
monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
electricity_peak_load_results = pd.DataFrame(monthly_electricity_peak
, columns=[f'{building.name} electricity peak load W'])
electricity_peak_load_results = monthly_electricity_peak
else:
electricity_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} electricity peak load W'])
if cte.MONTH in building.distribution_systems_electrical_consumption.keys():
extra_electrical_consumption = pd.DataFrame(building.distribution_systems_electrical_consumption[cte.MONTH],
columns=[
f'{building.name} electrical consumption for distribution Wh'])
else:
extra_electrical_consumption = pd.DataFrame(array,
columns=[
f'{building.name} electrical consumption for distribution Wh'])
if print_results is None:
print_results = heating_results
else:
print_results = pd.concat([print_results, heating_results], axis='columns')
print_results = pd.concat([print_results,
cooling_results,
lighting_results,
appliances_results,
dhw_results,
heating_consumption_results,
cooling_consumption_results,
dhw_consumption_results,
heating_peak_load_results,
cooling_peak_load_results,
electricity_peak_load_results,
onsite_electrical_production,
extra_electrical_consumption], axis='columns')
file += '\n'
file += f'name: {building.name}\n'
file += f'year of construction: {building.year_of_construction}\n'
file += f'function: {building.function}\n'
file += f'floor area: {building.floor_area}\n'
if building.average_storey_height is not None and building.eave_height is not None:
file += f'storeys: {int(building.eave_height / building.average_storey_height)}\n'
else:
file += f'storeys: n/a\n'
file += f'volume: {building.volume}\n'
full_path_results = Path(self._path / 'demand.csv').resolve()
print_results.to_csv(full_path_results, na_rep='null')
full_path_metadata = Path(self._path / 'metadata.csv').resolve()
with open(full_path_metadata, 'w') as metadata_file:
metadata_file.write(file)
def outputsforgraph(self):
file = 'city name: ' + self._city.name + '\n'
array = [None] * 12
for building in self._city.buildings:
if cte.MONTH in building.heating_demand.keys():
heating_results = building.heating_demand[cte.MONTH].rename(
columns={cte.INSEL_MEB: f'{building.name} heating Wh'})
else:
heating_results = pd.DataFrame(array, columns=[f'{building.name} heating demand Wh'])
if cte.MONTH in building.cooling_demand.keys():
cooling_results = building.cooling_demand[cte.MONTH].rename(
columns={cte.INSEL_MEB: f'{building.name} cooling Wh'})
else:
cooling_results = pd.DataFrame(array, columns=[f'{building.name} cooling demand Wh'])
if cte.MONTH in building.lighting_electrical_demand.keys():
lighting_results = building.lighting_electrical_demand[cte.MONTH] \
.rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'})
else:
lighting_results = pd.DataFrame(array, columns=[f'{building.name} lighting electrical demand Wh'])
if cte.MONTH in building.appliances_electrical_demand.keys():
appliances_results = building.appliances_electrical_demand[cte.MONTH] \
.rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'})
else:
appliances_results = pd.DataFrame(array, columns=[f'{building.name} appliances electrical demand Wh'])
if cte.MONTH in building.domestic_hot_water_heat_demand.keys():
dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH] \
.rename(columns={cte.INSEL_MEB: f'{building.name} domestic hot water demand Wh'})
else:
dhw_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water demand Wh'])
if cte.MONTH in building.heating_consumption.keys():
heating_consumption_results = pd.DataFrame(building.heating_consumption[cte.MONTH],
columns=[f'{building.name} heating consumption Wh'])
else:
heating_consumption_results = pd.DataFrame(array, columns=[f'{building.name} heating consumption Wh'])
if cte.MONTH in building.cooling_consumption.keys():
cooling_consumption_results = pd.DataFrame(building.cooling_consumption[cte.MONTH],
columns=[f'{building.name} cooling consumption Wh'])
else:
cooling_consumption_results = pd.DataFrame(array, columns=[f'{building.name} cooling consumption Wh'])
if cte.MONTH in building.domestic_hot_water_consumption.keys():
dhw_consumption_results = pd.DataFrame(building.domestic_hot_water_consumption[cte.MONTH],
columns=[f'{building.name} domestic hot water consumption Wh'])
else:
dhw_consumption_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water consumption Wh'])
if cte.MONTH in building.heating_peak_load.keys():
heating_peak_load_results = pd.DataFrame(building.heating_peak_load[cte.MONTH],
columns=[f'{building.name} heating peak load W'])
else:
heating_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} heating peak load W'])
if cte.MONTH in building.cooling_peak_load.keys():
cooling_peak_load_results = pd.DataFrame(building.cooling_peak_load[cte.MONTH],
columns=[f'{building.name} cooling peak load W'])
else:
cooling_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} cooling peak load W'])
electricity_peak_load_results = [None] * 12
if cte.MONTH in building.onsite_electrical_production.keys():
monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH]
onsite_electrical_production = pd.DataFrame(monthly_onsite_electrical_production,
columns=[f'{building.name} onsite electrical production Wh'])
onsite_electrical_production = monthly_onsite_electrical_production
else:
onsite_electrical_production = pd.DataFrame(array,
columns=[f'{building.name} onsite electrical production Wh'])
heating = 0
cooling = 0
for system in building.energy_systems:
for demand_type in system.demand_types:
if demand_type == cte.HEATING:
heating = 1
if demand_type == cte.COOLING:
cooling = 1
if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
peak_lighting = 0
peak_appliances = 0
for thermal_zone in building.internal_zones[0].thermal_zones:
lighting = thermal_zone.lighting
for schedule in lighting.schedules:
for value in schedule.values:
if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
peak_lighting = value * lighting.density * thermal_zone.total_floor_area
appliances = thermal_zone.appliances
for schedule in appliances.schedules:
for value in schedule.values:
if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
peak_appliances = value * appliances.density * thermal_zone.total_floor_area
monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
conditioning_peak = []
for i, value in enumerate(building.heating_peak_load[cte.MONTH]):
if cooling * building.cooling_peak_load[cte.MONTH][i] > heating * value:
conditioning_peak.append(cooling * building.cooling_peak_load[cte.MONTH][i])
else:
conditioning_peak.append(heating * value)
monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
electricity_peak_load_results = pd.DataFrame(monthly_electricity_peak
, columns=[f'{building.name} electricity peak load W'])
else:
electricity_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} electricity peak load W'])
onsite_electrical_production = [None] * 12
if cte.MONTH in building.distribution_systems_electrical_consumption.keys():
extra_electrical_consumption = pd.DataFrame(building.distribution_systems_electrical_consumption[cte.MONTH],
columns=[
f'{building.name} electrical consumption for distribution Wh'])
extra_electrical_consumption = building.distribution_systems_electrical_consumption[cte.MONTH]
else:
extra_electrical_consumption = pd.DataFrame(array,
columns=[
f'{building.name} electrical consumption for distribution Wh'])
extra_electrical_consumption = [None] * 12
if print_results is None:
print_results = heating_results
else:
print_results = pd.concat([print_results, heating_results], axis='columns')
print_results = pd.concat([print_results,
columns_names = [f'{building.name} heating demand J',
f'{building.name} cooling demand J',
f'{building.name} lighting demand J',
f'{building.name} appliances demand J',
f'{building.name} domestic hot water demand J',
f'{building.name} heating consumption J',
f'{building.name} cooling consumption J',
f'{building.name} domestic hot water consumption J',
f'{building.name} heating peak load W',
f'{building.name} cooling peak load W',
f'{building.name} electricity peak load W',
f'{building.name} onsite electrical production J',
f'{building.name} extra electrical consumption J'
]
print_results = pd.DataFrame([heating_results,
cooling_results,
lighting_results,
appliances_results,
@ -278,7 +126,8 @@ class Results:
cooling_peak_load_results,
electricity_peak_load_results,
onsite_electrical_production,
extra_electrical_consumption], axis='columns')
extra_electrical_consumption]).T
print_results.columns = columns_names
file += '\n'
file += f'name: {building.name}\n'
file += f'year of construction: {building.year_of_construction}\n'
@ -290,7 +139,7 @@ class Results:
file += f'storeys: n/a\n'
file += f'volume: {building.volume}\n'
full_path_results = Path(self._path / 'demand.csv').resolve()
full_path_results = Path(self._path / f'demand_{building.name}.csv').resolve()
print_results.to_csv(full_path_results, na_rep='null')
full_path_metadata = Path(self._path / 'metadata.csv').resolve()
with open(full_path_metadata, 'w') as metadata_file:

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@ -7,14 +7,13 @@ from hub.imports.results_factory import ResultFactory
class SraEngine:
def __init__(self, city, file_path, output_file_path):
def __init__(self, city, output_file_path):
"""
SRA class
:param file_path: _sra.xml file path
:param city: City
:param output_file_path: path to output the sra calculation
"""
self._city = city
self._file_path = file_path
self._output_file_path = output_file_path
if platform.system() == 'Linux':
self._executable = 'sra'
@ -29,6 +28,8 @@ class SraEngine:
Calls the software
"""
try:
subprocess.run([self._executable, str(self._file_path)], stdout=subprocess.DEVNULL)
subprocess.run([self._executable,
(self._output_file_path / f'{self._city.name}_sra.xml')],
stdout=subprocess.DEVNULL)
except (SubprocessError, TimeoutExpired, CalledProcessError) as error:
raise Exception(error)

0
test_problems Normal file
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