energy_system_modelling_workflow/energy_system_retrofit.py

from pathlib import Path
import subprocess
from building_modelling.ep_run_enrich import energy_plus_workflow
from energy_system_modelling_package.energy_system_modelling_factories.montreal_energy_system_archetype_modelling_factory import \
  MontrealEnergySystemArchetypesSimulationFactory
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.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 energy_system_modelling_package.energy_system_retrofit.energy_system_retrofit_report import EnergySystemRetrofitReport
from building_modelling.geojson_creator import process_geojson
from energy_system_modelling_package import random_assignation
from hub.imports.energy_systems_factory import EnergySystemsFactory
from energy_system_modelling_package.energy_system_modelling_factories.energy_system_sizing_factory import EnergySystemsSizingFactory
from energy_system_modelling_package.energy_system_retrofit.energy_system_retrofit_results import consumption_data, cost_data
from costing_package.cost import Cost
from costing_package.constants import SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS
from hub.exports.exports_factory import ExportsFactory

# Specify the GeoJSON file path
main_path = Path(__file__).parent.resolve()
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=0.00006, path=main_path)
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)
pv_assessment_path = output_path / 'pv_outputs'
pv_assessment_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()
energy_plus_workflow(city, energy_plus_output_path)
random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
EnergySystemsFactory('montreal_custom', city).enrich()
EnergySystemsSizingFactory('peak_load_sizing', city).enrich()
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()
EnergySystemsSizingFactory('peak_load_sizing', 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
for building in city.buildings:
  MontrealEnergySystemArchetypesSimulationFactory(f'archetype_cluster_{building.energy_systems_archetype_cluster_id}',
                                                  building,
                                                  simulation_results_path).enrich()
  if 'PV' in building.energy_systems_archetype_name:
    PvSystemAssessment(building=building,
                       pv_system=None,
                       battery=None,
                       electricity_demand=None,
                       tilt_angle=tilt_angle,
                       solar_angles=solar_angles,
                       pv_installation_type='rooftop',
                       simulation_model_type='explicit',
                       module_model_name=None,
                       inverter_efficiency=0.95,
                       system_catalogue_handler=None,
                       roof_percentage_coverage=0.75,
                       facade_coverage_percentage=0,
                       csv_output=False,
                       output_path=pv_assessment_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)
EnergySystemRetrofitReport(city, output_path, 'PV Implementation and System Retrofit',
                           current_status_energy_consumption, retrofitted_energy_consumption,
                           current_status_life_cycle_cost, retrofitted_life_cycle_cost).create_report()