129 lines
5.3 KiB
Python
129 lines
5.3 KiB
Python
from hub.imports.energy_systems_factory import EnergySystemsFactory
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from hub.imports.geometry_factory import GeometryFactory
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from hub.helpers.dictionaries import Dictionaries
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from hub.imports.construction_factory import ConstructionFactory
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from hub.imports.results_factory import ResultFactory
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from hub.exports.exports_factory import ExportsFactory
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from hub.imports.weather_factory import WeatherFactory
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from pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
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from pv_assessment.pv_system_assessment import PvSystemAssessment
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from pv_assessment.solar_calculator import SolarCalculator
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import random_assignation
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import subprocess
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from pathlib import Path
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input_file = "data/selected_buildings.geojson"
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demand_file = "data/energy_demand_data.csv"
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# Define specific paths for outputs from SRA (Simplified Radiosity Algorith) and PV calculation processes
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output_path = (Path(__file__).parent.parent / 'hub/out_files').resolve()
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output_path.mkdir(parents=True, exist_ok=True)
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sra_output_path = output_path / 'sra_outputs'
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sra_output_path.mkdir(parents=True, exist_ok=True)
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pv_assessment_path = output_path / 'pv_outputs'
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pv_assessment_path.mkdir(parents=True, exist_ok=True)
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city = GeometryFactory(
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"geojson",
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input_file,
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height_field="height",
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year_of_construction_field="contr_year",
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function_field="function_c",
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adjacency_field="adjacency",
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lot_area_field='lot_area',
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build_area_field='build_area',
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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ConstructionFactory('nrcan', city).enrich()
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WeatherFactory('epw', city).enrich()
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ResultFactory('archetypes', city, demand_file).enrich()
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# Export the city data in SRA-compatible format to facilitate solar radiation assessment
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ExportsFactory('sra', city, sra_output_path).export()
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# Run SRA simulation using an external command, passing the generated SRA XML file path as input
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sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
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subprocess.run(['sra', str(sra_path)])
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# Enrich city data with SRA simulation results for subsequent analysis
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ResultFactory('sra', city, sra_output_path).enrich()
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# Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
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tilt_angle = 37
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solar_parameters = SolarCalculator(city=city,
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surface_azimuth_angle=180,
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tilt_angle=tilt_angle,
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standard_meridian=-75)
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solar_angles = solar_parameters.solar_angles # Obtain solar angles for further analysis
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solar_parameters.tilted_irradiance_calculator() # Calculate the solar radiation on a tilted surface
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# Assignation of Energy System Archetypes to Buildings
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random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
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EnergySystemsFactory('montreal_future', city).enrich()
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for building in city.buildings:
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PvSystemAssessment(building=building,
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pv_system=None,
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battery=None,
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electricity_demand=None,
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tilt_angle=tilt_angle,
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solar_angles=solar_angles,
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pv_installation_type='rooftop',
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simulation_model_type='explicit',
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module_model_name=None,
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inverter_efficiency=0.95,
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system_catalogue_handler=None,
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roof_percentage_coverage=0.75,
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facade_coverage_percentage=0,
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csv_output=False,
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output_path=pv_assessment_path).enrich()
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r = []
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for building in city.buildings:
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r.append((building.build_area - building.lot_area) / building.thermal_zones_from_internal_zones[0].total_floor_area)
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print("done")
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import geopandas as gpd
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import matplotlib.pyplot as plt
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from matplotlib.colors import Normalize
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# Load GeoJSON file
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gdf = gpd.read_file(input_file)
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# Extract self-sufficiency values
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self_sufficiency_values = [building.self_sufficiency['year'] / 1000 for building in city.buildings]
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# Add self-sufficiency values to GeoDataFrame
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gdf['self_sufficiency'] = self_sufficiency_values
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# Determine the color normalization range
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vmin = min(0, gdf['self_sufficiency'].min()) # Include 0 if min is positive
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vmax = max(0, gdf['self_sufficiency'].max()) # Include 0 if max is negative
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# Set up the figure and axis
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fig, ax = plt.subplots(1, 1, figsize=(14, 10))
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# Define a colormap and normalize the values
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cmap = plt.cm.viridis
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norm = Normalize(vmin=vmin, vmax=vmax)
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# Plot the GeoDataFrame
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gdf.plot(column='self_sufficiency',
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cmap=cmap,
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linewidth=0.8,
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edgecolor='grey', # Add edges for better distinction
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legend=False, # Turn off the built-in legend
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ax=ax)
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# Add a custom colorbar
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sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
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sm._A = [] # Needed for ScalarMappable with no data
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cbar = fig.colorbar(sm, ax=ax, fraction=0.03, pad=0.04)
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cbar.set_label('Self-Sufficiency (kWh/year)', fontsize=12)
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# Add gridlines and axis labels
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ax.grid(color='lightgrey', linestyle='--', linewidth=0.5, alpha=0.7)
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ax.set_title('Building Self-Sufficiency Levels', fontsize=16, fontweight='bold', pad=20)
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ax.set_xlabel('Longitude', fontsize=12)
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ax.set_ylabel('Latitude', fontsize=12)
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# Improve layout
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plt.tight_layout()
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plt.show()
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