hub/pv_generation.py

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.results_factory import ResultFactory
from hub.exports.exports_factory import ExportsFactory
from hub.imports.weather_factory import WeatherFactory
from pv_assessment.solar_calculator import SolarCalculator
from pv_assessment.pv_system_assessment_without_consumption import PvSystemProduction
from scripts import random_assignation
import subprocess
from pathlib import Path

input_file = "data/selected_buildings.geojson"

output_path = (Path(__file__).parent.parent / 'hub / out_files').resolve()
output_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)

city = GeometryFactory(
               "geojson",
               input_file,
               height_field="height",
               year_of_construction_field="contr_year",
               function_field="function_c",
               adjacency_field="adjacency",
               lot_area_field='lot_area',
               build_area_field='build_area',
               function_to_hub=Dictionaries().montreal_function_to_hub_function).city
ConstructionFactory('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()

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
solar_parameters.tilted_irradiance_calculator()
random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
EnergySystemsFactory('montreal_future', city).enrich()
for building in city.buildings:
  PvSystemProduction(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=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()

print("simulation done")

# PLOTTING STUFF

import geopandas as gpd
import matplotlib.pyplot as plt
from shapely.geometry import shape, Polygon
import os

# Paths
layers_file = "data/cmm_limites_avec_mtl.geojson"  # Polygon layers
main_dir = os.path.abspath(".")

# Read layer polygons
layers_gdf = gpd.read_file(layers_file)
if layers_gdf.crs is None:
    layers_gdf.set_crs(epsg=32188, inplace=True)

buildings_data = []
for b in city.buildings:
    yearly_pv = b.pv_generation['year'][0]/1000
    if isinstance(yearly_pv, list):
        yearly_pv = sum(yearly_pv)

    # Extract the ground surface polygon from the building
    # Assuming the ground surface is at index 0. If not,
    # you may need to find it by checking the surface type.
    if not b.surfaces:
        continue  # no surfaces, skip

    ground_surface = b.surfaces[0]
    coords_3d = ground_surface.solid_polygon.coordinates
    # Convert 3D coordinates (X,Y,Z) into 2D (X,Y)
    coords_2d = [(c[0], c[1]) for c in coords_3d]

    # Create a Shapely polygon from these coordinates
    footprint_poly = Polygon(coords_2d)

    buildings_data.append({
        "geometry": footprint_poly,
        "yearly_pv": yearly_pv
    })

# Create the GeoDataFrame from the buildings_data
buildings_gdf = gpd.GeoDataFrame(buildings_data, crs="EPSG:26911")  # Change CRS if needed

# If layers_gdf is EPSG:32188 and buildings_gdf is EPSG:4326, reproject buildings:
buildings_gdf = buildings_gdf.to_crs(layers_gdf.crs)

# Loop through selected layers (e.g., layer_6, layer_7, etc.)
# Let's say we have a list of layers we want to process:
layers_of_interest = [f"layer_{i}" for i in range(6, 21)]  # example from layer_6 to layer_20

for layer_name in layers_of_interest:
    layer_poly = layers_gdf[layers_gdf["region"] == layer_name]
    if layer_poly.empty:
        continue

    # There might be multiple polygons per layer_name; dissolve them into one for simplicity
    layer_union = layer_poly.unary_union
    if layer_union.is_empty:
        continue

    # Select buildings within this layer polygon
    # Spatial join or mask
    buildings_in_layer = buildings_gdf[buildings_gdf.geometry.within(layer_union)]
    if buildings_in_layer.empty:
        continue

    # Plot the layer polygon and buildings
    fig, ax = plt.subplots(figsize=(20, 20), dpi=600)

    # Plot polygon
    layer_poly.plot(ax=ax, facecolor="none", edgecolor="black", linewidth=1)

    # Buildings colored by pv_generation
    # Let's set a colormap normalized by min/max PV
    vmin = buildings_in_layer["yearly_pv"].min()
    vmax = buildings_in_layer["yearly_pv"].max()

    buildings_in_layer.plot(column="yearly_pv", ax=ax,
                            cmap="viridis",
                            edgecolor="white",
                            linewidth=0.5,
                            legend=True,
                            legend_kwds={'label': "Yearly PV Generation (kWh)", 'orientation': "vertical"},
                            vmin=vmin, vmax=vmax)

    ax.set_title(f"Yearly PV Generation - {layer_name}", fontsize=14)
    ax.set_aspect('equal', 'box')
    plt.tight_layout()
    plt.savefig(os.path.join(main_dir, f"{layer_name}_pv_generation.pdf"), dpi=300)
    plt.close(fig)

# After generating individual layer plots, create a combined plot of all buildings (all layers):
fig, ax = plt.subplots(figsize=(20, 20), dpi=1200)

# If you want all buildings, possibly also plot the entire layers polygon union as context
layers_gdf.plot(ax=ax, facecolor="none", edgecolor="grey", linewidth=0.5)

vmin = buildings_gdf["yearly_pv"].min()
vmax = buildings_gdf["yearly_pv"].max()

buildings_gdf.plot(column="yearly_pv", ax=ax,
                   cmap="plasma",
                   edgecolor="none",
                   legend=True,
                   legend_kwds={'label': "Yearly PV Generation (kWh)", 'orientation': "vertical"},
                   vmin=vmin, vmax=vmax)

ax.set_title("Yearly PV Generation - All Layers", fontsize=14)
ax.set_aspect('equal', 'box')
plt.tight_layout()
plt.savefig(os.path.join(main_dir, "all_layers_pv_generation.png"), dpi=1200)
plt.close(fig)

# Finally, sum PV production by layer polygon and create a plot of polygons colored by total PV
layer_sums = []
for idx, row in layers_gdf.iterrows():
    region_name = row["region"]
    poly_geom = row["geometry"]
    # Buildings inside this polygon
    b_in_poly = buildings_gdf[buildings_gdf.geometry.within(poly_geom)]
    total_pv = b_in_poly["yearly_pv"].sum()
    layer_sums.append({
        "region": region_name,
        "total_pv": total_pv,
        "geometry": poly_geom
    })

layers_pv_gdf = gpd.GeoDataFrame(layer_sums, crs=layers_gdf.crs)

fig, ax = plt.subplots(figsize=(10, 10), dpi=300)
vmin = layers_pv_gdf["total_pv"].min()
vmax = layers_pv_gdf["total_pv"].max()

layers_pv_gdf.plot(column="total_pv", ax=ax,
                   cmap="YlOrRd",
                   edgecolor="black",
                   legend=True,
                   legend_kwds={'label': "Total PV Generation per Layer (kWh)", 'orientation': "vertical"},
                   vmin=vmin, vmax=vmax)

ax.set_title("Total PV Generation by Layer", fontsize=14)
ax.set_aspect('equal', 'box')
plt.tight_layout()
plt.savefig(os.path.join(main_dir, "layers_total_pv.png"), dpi=300)
plt.close(fig)

print("All plots generated successfully.")