energy_system_modelling_workflow/pv_athena.py

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')