53 lines
2.8 KiB
Python
53 lines
2.8 KiB
Python
import pandas as pd
|
|
from pathlib import Path
|
|
from scripts.ep_run_enrich import energy_plus_workflow
|
|
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 scripts import random_assignation
|
|
from hub.imports.energy_systems_factory import EnergySystemsFactory
|
|
from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
|
|
from scripts.costs.cost import Cost
|
|
from scripts.costs.constants import SYSTEM_RETROFIT_AND_PV
|
|
# Specify the GeoJSON file path
|
|
file_path = (Path(__file__).parent / 'input_files' / 'processed_output.geojson')
|
|
output_path = (Path(__file__).parent / 'out_files').resolve()
|
|
city = GeometryFactory('geojson',
|
|
path=file_path,
|
|
height_field='height',
|
|
year_of_construction_field='year_of_construction',
|
|
function_field='function',
|
|
function_to_hub=Dictionaries().montreal_function_to_hub_function).city
|
|
ConstructionFactory('nrcan', city).enrich()
|
|
|
|
UsageFactory('nrcan', city).enrich()
|
|
WeatherFactory('epw', city).enrich()
|
|
energy_plus_workflow(city)
|
|
random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
|
|
EnergySystemsFactory('montreal_future', city).enrich()
|
|
for building in city.buildings:
|
|
EnergySystemsSimulationFactory('archetype13', building=building, output_path=output_path).enrich()
|
|
sum_floor_area = 0
|
|
buildings_list = []
|
|
for building in city.buildings:
|
|
buildings_list.append(building.name)
|
|
df = pd.DataFrame(columns=['building_name', 'total_floor_area', 'investment_cost', 'lc CAPEX'])
|
|
df['building_name'] = buildings_list
|
|
for building in city.buildings:
|
|
for thermal_zone in building.thermal_zones_from_internal_zones:
|
|
sum_floor_area += thermal_zone.total_floor_area
|
|
costs = Cost(building=building, retrofit_scenario=SYSTEM_RETROFIT_AND_PV).life_cycle
|
|
costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
|
|
output_path / f'{building.name}_cc.csv')
|
|
investment_cost = costs.loc['global_capital_costs',
|
|
f'Scenario {SYSTEM_RETROFIT_AND_PV}'].loc[0, 'D3020_heat_and_cooling_generating_systems']
|
|
lcc_capex = costs.loc['total_capital_costs_systems', f'Scenario {SYSTEM_RETROFIT_AND_PV}']
|
|
df.loc[df['building_name'] == building.name, 'total_floor_area'] = (
|
|
building.thermal_zones_from_internal_zones[0].total_floor_area)
|
|
df.loc[df['building_name'] == building.name, 'investment_cost'] = investment_cost
|
|
df.loc[df['building_name'] == building.name, 'lc CAPEX'] = lcc_capex
|
|
|
|
df.to_csv(output_path / 'economic analysis.csv')
|