energy_system_modelling_workflow/energy_system_retrofit.py

from pathlib import Path
import subprocess
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 hub.imports.results_factory import ResultFactory
from scripts.energy_system_retrofit_report import EnergySystemRetrofitReport
from scripts.geojson_creator import process_geojson
from scripts import random_assignation
from hub.imports.energy_systems_factory import EnergySystemsFactory
from scripts.energy_system_sizing import SystemSizing
from scripts.solar_angles import CitySolarAngles
from scripts.pv_sizing_and_simulation import PVSizingSimulation
from scripts.energy_system_retrofit_results import consumption_data, cost_data
from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
from scripts.costs.cost import Cost
from scripts.costs.constants import SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS
import hub.helpers.constants as cte
from hub.exports.exports_factory import ExportsFactory
from scripts.pv_feasibility import pv_feasibility

# Specify the GeoJSON file path
input_files_path = (Path(__file__).parent / 'input_files')
input_files_path.mkdir(parents=True, exist_ok=True)
geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
geojson_file_path = input_files_path / 'output_buildings.geojson'
output_path = (Path(__file__).parent / 'out_files').resolve()
output_path.mkdir(parents=True, exist_ok=True)
energy_plus_output_path = output_path / 'energy_plus_outputs'
energy_plus_output_path.mkdir(parents=True, exist_ok=True)
simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_results').resolve()
simulation_results_path.mkdir(parents=True, exist_ok=True)
sra_output_path = output_path / 'sra_outputs'
sra_output_path.mkdir(parents=True, exist_ok=True)
cost_analysis_output_path = output_path / 'cost_analysis'
cost_analysis_output_path.mkdir(parents=True, exist_ok=True)
city = GeometryFactory(file_type='geojson',
                       path=geojson_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()
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()
pv_feasibility(-73.5681295982132, 45.49218262677643, 0.0001, selected_buildings=city.buildings)
energy_plus_workflow(city, energy_plus_output_path)
solar_angles = CitySolarAngles(city.name,
                               city.latitude,
                               city.longitude,
                               tilt_angle=45,
                               surface_azimuth_angle=180).calculate
random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
EnergySystemsFactory('montreal_custom', city).enrich()
SystemSizing(city.buildings).montreal_custom()
current_status_energy_consumption = consumption_data(city)
current_status_life_cycle_cost = {}
for building in city.buildings:
  cost_retrofit_scenario = CURRENT_STATUS
  lcc_dataframe = Cost(building=building,
                       retrofit_scenario=cost_retrofit_scenario,
                       fuel_tariffs=['Electricity-D', 'Gas-Energir']).life_cycle
  lcc_dataframe.to_csv(cost_analysis_output_path / f'{building.name}_current_status_lcc.csv')
  current_status_life_cycle_cost[f'{building.name}'] = cost_data(building, lcc_dataframe, cost_retrofit_scenario)
random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
EnergySystemsFactory('montreal_future', city).enrich()
for building in city.buildings:
  if 'PV' in building.energy_systems_archetype_name:
    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()
  if building.energy_systems_archetype_name == 'PV+4Pipe+DHW':
    EnergySystemsSimulationFactory('archetype13', building=building, output_path=simulation_results_path).enrich()
retrofitted_energy_consumption = consumption_data(city)
retrofitted_life_cycle_cost = {}
for building in city.buildings:
  cost_retrofit_scenario = SYSTEM_RETROFIT_AND_PV
  lcc_dataframe = Cost(building=building,
                       retrofit_scenario=cost_retrofit_scenario,
                       fuel_tariffs=['Electricity-D', 'Gas-Energir']).life_cycle
  lcc_dataframe.to_csv(cost_analysis_output_path / f'{building.name}_retrofitted_lcc.csv')
  retrofitted_life_cycle_cost[f'{building.name}'] = cost_data(building, lcc_dataframe, cost_retrofit_scenario)
for i in range(12):
  dhw_consumption = 0
  for building in city.buildings:
    dhw_consumption += building.domestic_hot_water_consumption[cte.MONTH][i] / 3.6e6