216 lines
8.1 KiB
Python
216 lines
8.1 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.solar_calculator import SolarCalculator
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from pv_assessment.pv_system_assessment_without_consumption import PvSystemProduction
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from scripts 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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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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ExportsFactory('sra', city, sra_output_path).export()
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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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ResultFactory('sra', city, sra_output_path).enrich()
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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
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solar_parameters.tilted_irradiance_calculator()
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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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PvSystemProduction(building=building,
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pv_system=None,
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battery=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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print("simulation done")
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# PLOTTING STUFF
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import geopandas as gpd
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import matplotlib.pyplot as plt
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from shapely.geometry import shape, Polygon
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import os
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# Paths
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layers_file = "data/cmm_limites_avec_mtl.geojson" # Polygon layers
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main_dir = os.path.abspath(".")
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# Read layer polygons
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layers_gdf = gpd.read_file(layers_file)
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if layers_gdf.crs is None:
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layers_gdf.set_crs(epsg=32188, inplace=True)
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buildings_data = []
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for b in city.buildings:
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yearly_pv = b.pv_generation['year'][0]/1000
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if isinstance(yearly_pv, list):
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yearly_pv = sum(yearly_pv)
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# Extract the ground surface polygon from the building
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# Assuming the ground surface is at index 0. If not,
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# you may need to find it by checking the surface type.
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if not b.surfaces:
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continue # no surfaces, skip
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ground_surface = b.surfaces[0]
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coords_3d = ground_surface.solid_polygon.coordinates
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# Convert 3D coordinates (X,Y,Z) into 2D (X,Y)
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coords_2d = [(c[0], c[1]) for c in coords_3d]
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# Create a Shapely polygon from these coordinates
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footprint_poly = Polygon(coords_2d)
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buildings_data.append({
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"geometry": footprint_poly,
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"yearly_pv": yearly_pv
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})
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# Create the GeoDataFrame from the buildings_data
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buildings_gdf = gpd.GeoDataFrame(buildings_data, crs="EPSG:26911") # Change CRS if needed
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# If layers_gdf is EPSG:32188 and buildings_gdf is EPSG:4326, reproject buildings:
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buildings_gdf = buildings_gdf.to_crs(layers_gdf.crs)
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# Loop through selected layers (e.g., layer_6, layer_7, etc.)
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# Let's say we have a list of layers we want to process:
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layers_of_interest = [f"layer_{i}" for i in range(6, 21)] # example from layer_6 to layer_20
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for layer_name in layers_of_interest:
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layer_poly = layers_gdf[layers_gdf["region"] == layer_name]
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if layer_poly.empty:
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continue
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# There might be multiple polygons per layer_name; dissolve them into one for simplicity
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layer_union = layer_poly.unary_union
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if layer_union.is_empty:
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continue
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# Select buildings within this layer polygon
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# Spatial join or mask
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buildings_in_layer = buildings_gdf[buildings_gdf.geometry.within(layer_union)]
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if buildings_in_layer.empty:
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continue
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# Plot the layer polygon and buildings
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fig, ax = plt.subplots(figsize=(20, 20), dpi=600)
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# Plot polygon
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layer_poly.plot(ax=ax, facecolor="none", edgecolor="black", linewidth=1)
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# Buildings colored by pv_generation
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# Let's set a colormap normalized by min/max PV
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vmin = buildings_in_layer["yearly_pv"].min()
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vmax = buildings_in_layer["yearly_pv"].max()
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buildings_in_layer.plot(column="yearly_pv", ax=ax,
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cmap="viridis",
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edgecolor="white",
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linewidth=0.5,
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legend=True,
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legend_kwds={'label': "Yearly PV Generation (kWh)", 'orientation': "vertical"},
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vmin=vmin, vmax=vmax)
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ax.set_title(f"Yearly PV Generation - {layer_name}", fontsize=14)
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ax.set_aspect('equal', 'box')
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plt.tight_layout()
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plt.savefig(os.path.join(main_dir, f"{layer_name}_pv_generation.pdf"), dpi=300)
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plt.close(fig)
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# After generating individual layer plots, create a combined plot of all buildings (all layers):
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fig, ax = plt.subplots(figsize=(20, 20), dpi=1200)
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# If you want all buildings, possibly also plot the entire layers polygon union as context
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layers_gdf.plot(ax=ax, facecolor="none", edgecolor="grey", linewidth=0.5)
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vmin = buildings_gdf["yearly_pv"].min()
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vmax = buildings_gdf["yearly_pv"].max()
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buildings_gdf.plot(column="yearly_pv", ax=ax,
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cmap="plasma",
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edgecolor="none",
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legend=True,
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legend_kwds={'label': "Yearly PV Generation (kWh)", 'orientation': "vertical"},
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vmin=vmin, vmax=vmax)
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ax.set_title("Yearly PV Generation - All Layers", fontsize=14)
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ax.set_aspect('equal', 'box')
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plt.tight_layout()
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plt.savefig(os.path.join(main_dir, "all_layers_pv_generation.png"), dpi=1200)
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plt.close(fig)
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# Finally, sum PV production by layer polygon and create a plot of polygons colored by total PV
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layer_sums = []
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for idx, row in layers_gdf.iterrows():
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region_name = row["region"]
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poly_geom = row["geometry"]
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# Buildings inside this polygon
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b_in_poly = buildings_gdf[buildings_gdf.geometry.within(poly_geom)]
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total_pv = b_in_poly["yearly_pv"].sum()
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layer_sums.append({
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"region": region_name,
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"total_pv": total_pv,
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"geometry": poly_geom
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})
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layers_pv_gdf = gpd.GeoDataFrame(layer_sums, crs=layers_gdf.crs)
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fig, ax = plt.subplots(figsize=(10, 10), dpi=300)
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vmin = layers_pv_gdf["total_pv"].min()
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vmax = layers_pv_gdf["total_pv"].max()
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layers_pv_gdf.plot(column="total_pv", ax=ax,
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cmap="YlOrRd",
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edgecolor="black",
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legend=True,
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legend_kwds={'label': "Total PV Generation per Layer (kWh)", 'orientation': "vertical"},
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vmin=vmin, vmax=vmax)
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ax.set_title("Total PV Generation by Layer", fontsize=14)
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ax.set_aspect('equal', 'box')
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plt.tight_layout()
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plt.savefig(os.path.join(main_dir, "layers_total_pv.png"), dpi=300)
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plt.close(fig)
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print("All plots generated successfully.")
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