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from geoalchemy2.shape import to_shape |
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from sqlalchemy import Column, Float, Integer, String, func, REAL |
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from sqlalchemy.ext.declarative import declarative_base |
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import geopandas as gpd |
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import numpy as np |
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import pandas as pd |
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from egon.data import db |
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from egon.data.datasets import Dataset |
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from egon.data.datasets.electricity_demand import ( |
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EgonDemandRegioZensusElectricity, |
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) |
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from egon.data.datasets.electricity_demand.temporal import calc_load_curves_cts |
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from egon.data.datasets.electricity_demand_timeseries.hh_buildings import ( |
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OsmBuildingsSynthetic, |
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) |
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from egon.data.datasets.electricity_demand_timeseries.tools import ( |
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random_ints_until_sum, |
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random_point_in_square, |
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specific_int_until_sum, |
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write_table_to_postgis, |
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write_table_to_postgres, |
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) |
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from egon.data.datasets.zensus_mv_grid_districts import MapZensusGridDistricts |
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from egon.data.datasets.zensus_vg250 import DestatisZensusPopulationPerHa |
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from egon.data.datasets.electricity_demand import EgonDemandRegioZensusElectricity |
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import egon.data.config |
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engine = db.engine() |
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Base = declarative_base() |
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data_config = egon.data.config.datasets() |
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RANDOM_SEED = egon.data.config.settings()["egon-data"]["--random-seed"] |
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import saio |
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# import db tables |
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saio.register_schema("openstreetmap", engine=engine) |
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saio.register_schema("society", engine=engine) |
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saio.register_schema("demand", engine=engine) |
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saio.register_schema("boundaries", engine=engine) |
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class EgonCtsElectricityDemandBuildingShare(Base): |
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__tablename__ = "egon_cts_electricity_demand_building_share" |
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__table_args__ = {"schema": "demand"} |
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id = Column(Integer, primary_key=True) |
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scenario = Column(String, primary_key=True) |
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bus_id = Column(Integer, index=True) |
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profile_share = Column(Float) |
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class CtsPeakLoads(Base): |
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__tablename__ = "egon_cts_peak_loads" |
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__table_args__ = {"schema": "demand"} |
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id = Column(String, primary_key=True) |
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cts_peak_load_in_w_2035 = Column(REAL) |
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cts_peak_load_in_w_100RE = Column(REAL) |
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def amenities_without_buildings(): |
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""" |
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Returns |
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------- |
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pd.DataFrame |
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Table of amenities without buildings |
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""" |
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from saio.openstreetmap import osm_amenities_not_in_buildings_filtered |
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with db.session_scope() as session: |
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cells_query = ( |
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session.query( |
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DestatisZensusPopulationPerHa.id.label( |
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"zensus_population_id" |
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), |
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# TODO can be used for square around amenity |
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# (1 geom_amenity: 1 geom_building) |
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# not unique amenity_ids yet |
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osm_amenities_not_in_buildings_filtered.geom_amenity, |
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osm_amenities_not_in_buildings_filtered.egon_amenity_id, |
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# EgonDemandRegioZensusElectricity.demand, |
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# # TODO can be used to generate n random buildings |
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# # (n amenities : 1 randombuilding) |
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# func.count( |
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# osm_amenities_not_in_buildings_filtered.egon_amenity_id |
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# ).label("n_amenities_inside"), |
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# DestatisZensusPopulationPerHa.geom, |
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) |
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.filter( |
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func.st_within( |
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osm_amenities_not_in_buildings_filtered.geom_amenity, |
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DestatisZensusPopulationPerHa.geom, |
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) |
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) |
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.filter( |
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DestatisZensusPopulationPerHa.id |
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== EgonDemandRegioZensusElectricity.zensus_population_id |
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) |
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.filter( |
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EgonDemandRegioZensusElectricity.sector == "service", |
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EgonDemandRegioZensusElectricity.scenario == "eGon2035" |
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# ).group_by( |
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# EgonDemandRegioZensusElectricity.zensus_population_id, |
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# DestatisZensusPopulationPerHa.geom, |
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) |
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) |
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# # TODO can be used to generate n random buildings |
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# df_cells_with_amenities_not_in_buildings = gpd.read_postgis( |
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# cells_query.statement, cells_query.session.bind, geom_col="geom" |
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# ) |
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# |
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# # TODO can be used for square around amenity |
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df_synthetic_buildings_for_amenities = gpd.read_postgis( |
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cells_query.statement, |
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cells_query.session.bind, |
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geom_col="geom_amenity", |
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) |
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return df_synthetic_buildings_for_amenities |
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def place_buildings_with_amenities(df, amenities=None, max_amenities=None): |
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""" |
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Building centers are placed randomly within census cells. |
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The Number of buildings is derived from n_amenity_inside, the selected |
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method and number of amenities per building. |
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""" |
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if isinstance(max_amenities, int): |
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# amount of amenities is randomly generated within bounds (max_amenities, |
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# amenities per cell) |
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df["n_amenities_inside"] = df["n_amenities_inside"].apply( |
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random_ints_until_sum, args=[max_amenities] |
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) |
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if isinstance(amenities, int): |
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# Specific amount of amenities per building |
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df["n_amenities_inside"] = df["n_amenities_inside"].apply( |
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specific_int_until_sum, args=[amenities] |
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) |
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# Unnest each building |
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df = df.explode(column="n_amenities_inside") |
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# building count per cell |
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df["building_count"] = df.groupby(["zensus_population_id"]).cumcount() + 1 |
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# generate random synthetic buildings |
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edge_length = 5 |
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# create random points within census cells |
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points = random_point_in_square(geom=df["geom"], tol=edge_length / 2) |
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df.reset_index(drop=True, inplace=True) |
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# Store center of polygon |
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df["geom_point"] = points |
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# Drop geometry of census cell |
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df = df.drop(columns=["geom"]) |
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return df |
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def create_synthetic_buildings(df, points=None, crs="EPSG:3035"): |
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""" |
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Synthetic buildings are generated around points. |
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""" |
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if isinstance(points, str) and points in df.columns: |
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points = df[points] |
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elif isinstance(points, gpd.GeoSeries): |
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pass |
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else: |
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raise ValueError("Points are of the wrong type") |
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# Create building using a square around point |
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edge_length = 5 |
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df["geom_building"] = points.buffer(distance=edge_length / 2, cap_style=3) |
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if "geom_point" not in df.columns: |
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df["geom_point"] = df["geom_building"].centroid |
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# TODO Check CRS |
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df = gpd.GeoDataFrame( |
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df, |
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crs=crs, |
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geometry="geom_building", |
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) |
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# TODO remove after implementation of egon_building_id |
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df.rename(columns={"id": "egon_building_id"}, inplace=True) |
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# get max number of building ids from synthetic residential table |
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with db.session_scope() as session: |
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max_synth_residential_id = session.execute( |
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func.max(OsmBuildingsSynthetic.id) |
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).scalar() |
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max_synth_residential_id = int(max_synth_residential_id) |
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# create sequential ids |
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df["egon_building_id"] = range( |
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max_synth_residential_id + 1, |
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max_synth_residential_id + df.shape[0] + 1, |
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) |
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df["area"] = df["geom_building"].area |
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# set building type of synthetic building |
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df["building"] = "cts" |
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# TODO remove in #772 |
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df = df.rename( |
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columns={ |
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# "zensus_population_id": "cell_id", |
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"egon_building_id": "id", |
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} |
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) |
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return df |
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def buildings_with_amenities(): |
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"""""" |
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from saio.boundaries import egon_map_zensus_buildings_filtered_all |
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from saio.openstreetmap import osm_buildings_filtered_with_amenities |
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with db.session_scope() as session: |
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cells_query = ( |
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session.query( |
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osm_buildings_filtered_with_amenities.id.label( |
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"egon_building_id" |
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), |
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osm_buildings_filtered_with_amenities.building, |
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osm_buildings_filtered_with_amenities.n_amenities_inside, |
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osm_buildings_filtered_with_amenities.area, |
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osm_buildings_filtered_with_amenities.geom_building, |
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osm_buildings_filtered_with_amenities.geom_point, |
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egon_map_zensus_buildings_filtered_all.zensus_population_id, |
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) |
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.filter( |
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osm_buildings_filtered_with_amenities.id |
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== egon_map_zensus_buildings_filtered_all.id |
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) |
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.filter( |
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EgonDemandRegioZensusElectricity.zensus_population_id |
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== egon_map_zensus_buildings_filtered_all.zensus_population_id |
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) |
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.filter( |
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EgonDemandRegioZensusElectricity.sector == "service", |
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EgonDemandRegioZensusElectricity.scenario == "eGon2035", |
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) |
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) |
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df_amenities_in_buildings = pd.read_sql( |
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cells_query.statement, cells_query.session.bind, index_col=None |
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) |
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# TODO necessary? |
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df_amenities_in_buildings["geom_building"] = df_amenities_in_buildings[ |
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"geom_building" |
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].apply(to_shape) |
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df_amenities_in_buildings["geom_point"] = df_amenities_in_buildings[ |
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"geom_point" |
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].apply(to_shape) |
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# # Count amenities per building |
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# df_amenities_in_buildings["n_amenities_inside"] = 1 |
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# df_amenities_in_buildings[ |
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# "n_amenities_inside" |
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# ] = df_amenities_in_buildings.groupby("egon_building_id")[ |
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# "n_amenities_inside" |
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# ].transform( |
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# "sum" |
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# ) |
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# # Only keep one building for multiple amenities |
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# df_amenities_in_buildings = df_amenities_in_buildings.drop_duplicates( |
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# "egon_building_id" |
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# ) |
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# df_amenities_in_buildings["building"] = "cts" |
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# TODO maybe remove later |
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df_amenities_in_buildings.sort_values("egon_building_id").reset_index( |
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drop=True, inplace=True |
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) |
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df_amenities_in_buildings.rename( |
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columns={ |
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# "zensus_population_id": "cell_id", |
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"egon_building_id": "id" |
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}, |
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inplace=True, |
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) |
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return df_amenities_in_buildings |
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292
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# TODO maybe replace with tools.write_table_to_db |
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def write_synthetic_buildings_to_db(df_synthetic_buildings): |
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"""""" |
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if "geom_point" not in df_synthetic_buildings.columns: |
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df_synthetic_buildings["geom_point"] = df_synthetic_buildings[ |
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"geom_building" |
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299
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].centroid |
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300
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301
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df_synthetic_buildings = df_synthetic_buildings.rename( |
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columns={ |
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"zensus_population_id": "cell_id", |
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"egon_building_id": "id", |
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} |
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) |
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# Only take existing columns |
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columns = [ |
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309
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column.key for column in OsmBuildingsSynthetic.__table__.columns |
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310
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] |
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311
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df_synthetic_buildings = df_synthetic_buildings.loc[:, columns] |
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312
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313
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dtypes = {i: OsmBuildingsSynthetic.__table__.columns[i].type for i in OsmBuildingsSynthetic.__table__.columns.keys()} |
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314
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315
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# Write new buildings incl coord into db |
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316
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df_synthetic_buildings.to_postgis( |
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317
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name=OsmBuildingsSynthetic.__tablename__, |
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318
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con=engine, |
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319
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if_exists="append", |
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320
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# schema="openstreetmap", |
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321
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schema=OsmBuildingsSynthetic.__table_args__["schema"], |
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322
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# dtype={ |
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323
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# "id": OsmBuildingsSynthetic.id.type, |
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324
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# "cell_id": OsmBuildingsSynthetic.cell_id.type, |
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325
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# "geom_building": OsmBuildingsSynthetic.geom_building.type, |
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326
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# "geom_point": OsmBuildingsSynthetic.geom_point.type, |
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327
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# "n_amenities_inside": OsmBuildingsSynthetic.n_amenities_inside.type, |
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328
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# "building": OsmBuildingsSynthetic.building.type, |
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329
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# "area": OsmBuildingsSynthetic.area.type, |
|
330
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# }, |
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331
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dtype=dtypes, |
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332
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) |
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333
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334
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335
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def buildings_without_amenities(): |
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336
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""" """ |
|
337
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from saio.boundaries import egon_map_zensus_buildings_filtered_all |
|
338
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|
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from saio.openstreetmap import ( |
|
339
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osm_amenities_not_in_buildings_filtered, |
|
340
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osm_amenities_shops_filtered, |
|
341
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osm_buildings_filtered, |
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342
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osm_buildings_synthetic, |
|
343
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) |
|
344
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# buildings_filtered in cts-demand-cells without amenities |
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345
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with db.session_scope() as session: |
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346
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|
347
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# Synthetic Buildings |
|
348
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q_synth_buildings = session.query( |
|
349
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osm_buildings_synthetic.cell_id.cast(Integer).label( |
|
350
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|
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"zensus_population_id" |
|
351
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), |
|
352
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osm_buildings_synthetic.id.cast(Integer).label("id"), |
|
353
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osm_buildings_synthetic.area.label("area"), |
|
354
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osm_buildings_synthetic.geom_building.label("geom_building"), |
|
355
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osm_buildings_synthetic.geom_point.label("geom_point"), |
|
356
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|
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) |
|
357
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|
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|
358
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|
|
# Buildings filtered |
|
359
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|
|
q_buildings_filtered = session.query( |
|
360
|
|
|
egon_map_zensus_buildings_filtered_all.zensus_population_id, |
|
361
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|
|
osm_buildings_filtered.id, |
|
362
|
|
|
osm_buildings_filtered.area, |
|
363
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|
|
osm_buildings_filtered.geom_building, |
|
364
|
|
|
osm_buildings_filtered.geom_point, |
|
365
|
|
|
).filter( |
|
366
|
|
|
osm_buildings_filtered.id |
|
367
|
|
|
== egon_map_zensus_buildings_filtered_all.id |
|
368
|
|
|
) |
|
369
|
|
|
|
|
370
|
|
|
# Amenities + zensus_population_id |
|
371
|
|
|
q_amenities = ( |
|
372
|
|
|
session.query( |
|
373
|
|
|
DestatisZensusPopulationPerHa.id.label( |
|
374
|
|
|
"zensus_population_id" |
|
375
|
|
|
), |
|
376
|
|
|
) |
|
377
|
|
|
.filter( |
|
378
|
|
|
func.st_within( |
|
379
|
|
|
osm_amenities_shops_filtered.geom_amenity, |
|
380
|
|
|
DestatisZensusPopulationPerHa.geom, |
|
381
|
|
|
) |
|
382
|
|
|
) |
|
383
|
|
|
.distinct(DestatisZensusPopulationPerHa.id) |
|
384
|
|
|
) |
|
385
|
|
|
|
|
386
|
|
|
# Cells with CTS demand but without amenities |
|
387
|
|
|
q_cts_without_amenities = ( |
|
388
|
|
|
session.query( |
|
389
|
|
|
EgonDemandRegioZensusElectricity.zensus_population_id, |
|
390
|
|
|
) |
|
391
|
|
|
.filter( |
|
392
|
|
|
EgonDemandRegioZensusElectricity.sector == "service", |
|
393
|
|
|
EgonDemandRegioZensusElectricity.scenario == "eGon2035", |
|
394
|
|
|
) |
|
395
|
|
|
.filter( |
|
396
|
|
|
EgonDemandRegioZensusElectricity.zensus_population_id.notin_( |
|
397
|
|
|
q_amenities |
|
398
|
|
|
) |
|
399
|
|
|
) |
|
400
|
|
|
.distinct() |
|
401
|
|
|
) |
|
402
|
|
|
|
|
403
|
|
|
# Buildings filtered + synthetic buildings residential in |
|
404
|
|
|
# cells with CTS demand but without amenities |
|
405
|
|
|
cells_query = q_synth_buildings.union(q_buildings_filtered).filter( |
|
406
|
|
|
egon_map_zensus_buildings_filtered_all.zensus_population_id.in_( |
|
407
|
|
|
q_cts_without_amenities |
|
408
|
|
|
) |
|
409
|
|
|
) |
|
410
|
|
|
|
|
411
|
|
|
# df_buildings_without_amenities = pd.read_sql( |
|
412
|
|
|
# cells_query.statement, cells_query.session.bind, index_col=None) |
|
413
|
|
|
df_buildings_without_amenities = gpd.read_postgis( |
|
414
|
|
|
cells_query.statement, |
|
415
|
|
|
cells_query.session.bind, |
|
416
|
|
|
geom_col="geom_building", |
|
417
|
|
|
) |
|
418
|
|
|
|
|
419
|
|
|
df_buildings_without_amenities = df_buildings_without_amenities.rename( |
|
420
|
|
|
columns={ |
|
421
|
|
|
# "zensus_population_id": "cell_id", |
|
422
|
|
|
"egon_building_id": "id", |
|
423
|
|
|
} |
|
424
|
|
|
) |
|
425
|
|
|
|
|
426
|
|
|
return df_buildings_without_amenities |
|
427
|
|
|
|
|
428
|
|
|
|
|
429
|
|
|
def select_cts_buildings(df_buildings_without_amenities): |
|
430
|
|
|
""" """ |
|
431
|
|
|
# TODO Adapt method |
|
432
|
|
|
# Select one building each cell |
|
433
|
|
|
# take the first |
|
434
|
|
|
df_buildings_with_cts_demand = ( |
|
435
|
|
|
df_buildings_without_amenities.drop_duplicates( |
|
436
|
|
|
# subset="cell_id", keep="first" |
|
437
|
|
|
subset="zensus_population_id", keep="first" |
|
438
|
|
|
).reset_index(drop=True) |
|
439
|
|
|
) |
|
440
|
|
|
df_buildings_with_cts_demand["n_amenities_inside"] = 1 |
|
441
|
|
|
df_buildings_with_cts_demand["building"] = "cts" |
|
442
|
|
|
|
|
443
|
|
|
return df_buildings_with_cts_demand |
|
444
|
|
|
|
|
445
|
|
|
|
|
446
|
|
|
def cells_with_cts_demand_only(df_buildings_without_amenities): |
|
447
|
|
|
"""""" |
|
448
|
|
|
from saio.openstreetmap import osm_amenities_shops_filtered |
|
449
|
|
|
# cells mit amenities |
|
450
|
|
|
with db.session_scope() as session: |
|
451
|
|
|
sub_query = ( |
|
452
|
|
|
session.query( |
|
453
|
|
|
DestatisZensusPopulationPerHa.id.label( |
|
454
|
|
|
"zensus_population_id" |
|
455
|
|
|
), |
|
456
|
|
|
) |
|
457
|
|
|
.filter( |
|
458
|
|
|
func.st_within( |
|
459
|
|
|
osm_amenities_shops_filtered.geom_amenity, |
|
460
|
|
|
DestatisZensusPopulationPerHa.geom, |
|
461
|
|
|
) |
|
462
|
|
|
) |
|
463
|
|
|
.distinct(DestatisZensusPopulationPerHa.id) |
|
464
|
|
|
) |
|
465
|
|
|
|
|
466
|
|
|
cells_query = ( |
|
467
|
|
|
session.query( |
|
468
|
|
|
EgonDemandRegioZensusElectricity.zensus_population_id, |
|
469
|
|
|
EgonDemandRegioZensusElectricity.scenario, |
|
470
|
|
|
EgonDemandRegioZensusElectricity.sector, |
|
471
|
|
|
EgonDemandRegioZensusElectricity.demand, |
|
472
|
|
|
DestatisZensusPopulationPerHa.geom, |
|
473
|
|
|
) |
|
474
|
|
|
.filter( |
|
475
|
|
|
EgonDemandRegioZensusElectricity.sector == "service", |
|
476
|
|
|
EgonDemandRegioZensusElectricity.scenario == "eGon2035", |
|
477
|
|
|
) |
|
478
|
|
|
.filter( |
|
479
|
|
|
EgonDemandRegioZensusElectricity.zensus_population_id.notin_( |
|
480
|
|
|
sub_query |
|
481
|
|
|
) |
|
482
|
|
|
) |
|
483
|
|
|
.filter( |
|
484
|
|
|
EgonDemandRegioZensusElectricity.zensus_population_id |
|
485
|
|
|
== DestatisZensusPopulationPerHa.id |
|
486
|
|
|
) |
|
487
|
|
|
) |
|
488
|
|
|
|
|
489
|
|
|
df_cts_cell_without_amenities = gpd.read_postgis( |
|
490
|
|
|
cells_query.statement, |
|
491
|
|
|
cells_query.session.bind, |
|
492
|
|
|
geom_col="geom", |
|
493
|
|
|
index_col=None, |
|
494
|
|
|
) |
|
495
|
|
|
|
|
496
|
|
|
# TODO maybe remove |
|
497
|
|
|
df_buildings_without_amenities = df_buildings_without_amenities.rename( |
|
498
|
|
|
columns={"cell_id": "zensus_population_id"} |
|
499
|
|
|
) |
|
500
|
|
|
|
|
501
|
|
|
# Census cells with only cts demand |
|
502
|
|
|
df_cells_only_cts_demand = df_cts_cell_without_amenities.loc[ |
|
503
|
|
|
~df_cts_cell_without_amenities["zensus_population_id"].isin( |
|
504
|
|
|
df_buildings_without_amenities["zensus_population_id"].unique() |
|
505
|
|
|
) |
|
506
|
|
|
] |
|
507
|
|
|
|
|
508
|
|
|
df_cells_only_cts_demand.reset_index(drop=True, inplace=True) |
|
509
|
|
|
|
|
510
|
|
|
return df_cells_only_cts_demand |
|
511
|
|
|
|
|
512
|
|
|
|
|
513
|
|
|
def calc_census_cell_share(scenario="eGon2035"): |
|
514
|
|
|
"""""" |
|
515
|
|
|
|
|
516
|
|
|
with db.session_scope() as session: |
|
517
|
|
|
cells_query = ( |
|
518
|
|
|
session.query( |
|
519
|
|
|
EgonDemandRegioZensusElectricity, MapZensusGridDistricts.bus_id |
|
520
|
|
|
) |
|
521
|
|
|
.filter(EgonDemandRegioZensusElectricity.sector == "service") |
|
522
|
|
|
.filter(EgonDemandRegioZensusElectricity.scenario == scenario) |
|
523
|
|
|
.filter( |
|
524
|
|
|
EgonDemandRegioZensusElectricity.zensus_population_id |
|
525
|
|
|
== MapZensusGridDistricts.zensus_population_id |
|
526
|
|
|
) |
|
527
|
|
|
) |
|
528
|
|
|
|
|
529
|
|
|
df_demand_regio_electricity_demand = pd.read_sql( |
|
530
|
|
|
cells_query.statement, |
|
531
|
|
|
cells_query.session.bind, |
|
532
|
|
|
index_col="zensus_population_id", |
|
533
|
|
|
) |
|
534
|
|
|
|
|
535
|
|
|
# get demand share of cell per bus |
|
536
|
|
|
# share ist für scenarios identisch |
|
537
|
|
|
df_census_share = df_demand_regio_electricity_demand[ |
|
538
|
|
|
"demand" |
|
539
|
|
|
] / df_demand_regio_electricity_demand.groupby("bus_id")[ |
|
540
|
|
|
"demand" |
|
541
|
|
|
].transform( |
|
542
|
|
|
"sum" |
|
543
|
|
|
) |
|
544
|
|
|
df_census_share = df_census_share.rename("cell_share") |
|
545
|
|
|
|
|
546
|
|
|
df_census_share = pd.concat( |
|
547
|
|
|
[df_census_share, df_demand_regio_electricity_demand[["bus_id", "scenario"]]], axis=1 |
|
548
|
|
|
) |
|
549
|
|
|
|
|
550
|
|
|
df_census_share.reset_index(inplace=True) |
|
551
|
|
|
return df_census_share |
|
552
|
|
|
|
|
553
|
|
|
|
|
554
|
|
|
def calc_building_demand_profile_share(df_cts_buildings, scenario="eGon2035"): |
|
555
|
|
|
""" |
|
556
|
|
|
Share of cts electricity demand profile per bus for every selected building |
|
557
|
|
|
""" |
|
558
|
|
|
|
|
559
|
|
|
def calc_building_amenity_share(df_cts_buildings): |
|
560
|
|
|
"""""" |
|
561
|
|
|
df_building_amenity_share = 1 / df_cts_buildings.groupby( |
|
562
|
|
|
"zensus_population_id")["n_amenities_inside"].transform("sum") |
|
563
|
|
|
df_building_amenity_share = pd.concat( |
|
564
|
|
|
[ |
|
565
|
|
|
df_building_amenity_share.rename("building_amenity_share"), |
|
566
|
|
|
df_cts_buildings[["zensus_population_id", "id"]], |
|
567
|
|
|
], |
|
568
|
|
|
axis=1, |
|
569
|
|
|
) |
|
570
|
|
|
return df_building_amenity_share |
|
571
|
|
|
|
|
572
|
|
|
df_building_amenity_share = calc_building_amenity_share(df_cts_buildings) |
|
573
|
|
|
|
|
574
|
|
|
df_census_cell_share = calc_census_cell_share(scenario) |
|
575
|
|
|
|
|
576
|
|
|
df_demand_share = pd.merge(left=df_building_amenity_share, right=df_census_cell_share, |
|
577
|
|
|
left_on="zensus_population_id", right_on="zensus_population_id") |
|
578
|
|
|
df_demand_share["profile_share"] = df_demand_share["building_amenity_share"].multiply( |
|
579
|
|
|
df_demand_share["cell_share"]) |
|
580
|
|
|
|
|
581
|
|
|
return df_demand_share[["id", "bus_id", "scenario", "profile_share"]] |
|
582
|
|
|
|
|
583
|
|
|
|
|
584
|
|
|
def calc_building_profiles(df_demand_share=None, egon_building_id=None, scenario="eGon2035"): |
|
585
|
|
|
""" |
|
586
|
|
|
|
|
587
|
|
|
""" |
|
588
|
|
|
|
|
589
|
|
|
if not isinstance(df_demand_share, pd.DataFrame): |
|
590
|
|
|
with db.session_scope() as session: |
|
591
|
|
|
cells_query = session.query(EgonCtsElectricityDemandBuildingShare) |
|
592
|
|
|
|
|
593
|
|
|
df_demand_share = pd.read_sql( |
|
594
|
|
|
cells_query.statement, cells_query.session.bind, index_col=None) |
|
595
|
|
|
|
|
596
|
|
|
df_cts_profiles = calc_load_curves_cts(scenario) |
|
597
|
|
|
|
|
598
|
|
|
# Only calculate selected building profile if egon_building_id is given |
|
599
|
|
|
if isinstance(egon_building_id, int) and egon_building_id in df_demand_share["id"]: |
|
600
|
|
|
df_demand_share = df_demand_share.loc[ |
|
601
|
|
|
df_demand_share["id"] == egon_building_id] |
|
602
|
|
|
|
|
603
|
|
|
df_building_profiles = pd.DataFrame() |
|
604
|
|
|
for bus_id, df in df_demand_share.groupby('bus_id'): |
|
605
|
|
|
shares = df.set_index("id", drop=True)["profile_share"] |
|
606
|
|
|
profile = df_cts_profiles.loc[:, bus_id] |
|
607
|
|
|
building_profiles = profile.apply(lambda x: x * shares) |
|
|
|
|
|
|
608
|
|
|
df_building_profiles = pd.concat([df_building_profiles, building_profiles], axis=1) |
|
609
|
|
|
|
|
610
|
|
|
return df_building_profiles |
|
611
|
|
|
|
|
612
|
|
|
|
|
613
|
|
|
def cts_to_buildings(): |
|
614
|
|
|
"""""" |
|
615
|
|
|
# Buildings with amenities |
|
616
|
|
|
df_buildings_with_amenities = buildings_with_amenities() |
|
617
|
|
|
|
|
618
|
|
|
# Remove synthetic CTS buildings if existing |
|
619
|
|
|
delete_synthetic_cts_buildings() |
|
620
|
|
|
# Create synthetic buildings for amenites without buildings |
|
621
|
|
|
df_amenities_without_buildings = amenities_without_buildings() |
|
622
|
|
|
df_amenities_without_buildings["n_amenities_inside"] = 1 |
|
623
|
|
|
df_synthetic_buildings_with_amenities = create_synthetic_buildings( |
|
624
|
|
|
df_amenities_without_buildings, points="geom_amenity" |
|
625
|
|
|
) |
|
626
|
|
|
|
|
627
|
|
|
# TODO write to DB and remove renaming |
|
628
|
|
|
# write_synthetic_buildings_to_db(df_synthetic_buildings_with_amenities) |
|
629
|
|
|
write_table_to_postgis(df_synthetic_buildings_with_amenities.rename( |
|
630
|
|
|
columns={ |
|
631
|
|
|
"zensus_population_id": "cell_id", |
|
632
|
|
|
"egon_building_id": "id", |
|
633
|
|
|
}), |
|
634
|
|
|
OsmBuildingsSynthetic, |
|
635
|
|
|
drop=False) |
|
636
|
|
|
|
|
637
|
|
|
# Cells without amenities but CTS demand and buildings |
|
638
|
|
|
df_buildings_without_amenities = buildings_without_amenities() |
|
639
|
|
|
|
|
640
|
|
|
# TODO Fix Adhoc Bugfix duplicated buildings |
|
641
|
|
|
mask = df_buildings_without_amenities.loc[ |
|
642
|
|
|
df_buildings_without_amenities['id'].isin( |
|
643
|
|
|
df_buildings_with_amenities['id'])].index |
|
644
|
|
|
df_buildings_without_amenities = df_buildings_without_amenities.drop( |
|
645
|
|
|
index=mask).reset_index(drop=True) |
|
646
|
|
|
|
|
647
|
|
|
df_buildings_without_amenities = select_cts_buildings( |
|
648
|
|
|
df_buildings_without_amenities |
|
649
|
|
|
) |
|
650
|
|
|
df_buildings_without_amenities["n_amenities_inside"] = 1 |
|
651
|
|
|
|
|
652
|
|
|
# Create synthetic amenities and buildings in cells with only CTS demand |
|
653
|
|
|
df_cells_with_cts_demand_only = cells_with_cts_demand_only( |
|
654
|
|
|
df_buildings_without_amenities |
|
655
|
|
|
) |
|
656
|
|
|
# Only 1 Amenity per cell |
|
657
|
|
|
df_cells_with_cts_demand_only["n_amenities_inside"] = 1 |
|
658
|
|
|
# Only 1 Amenity per Building |
|
659
|
|
|
df_cells_with_cts_demand_only = place_buildings_with_amenities( |
|
660
|
|
|
df_cells_with_cts_demand_only, amenities=1 |
|
661
|
|
|
) |
|
662
|
|
|
# Leads to only 1 building per cell |
|
663
|
|
|
df_synthetic_buildings_without_amenities = ( |
|
664
|
|
|
create_synthetic_buildings( |
|
665
|
|
|
df_cells_with_cts_demand_only, points="geom_point" |
|
666
|
|
|
) |
|
667
|
|
|
) |
|
668
|
|
|
|
|
669
|
|
|
# TODO write to DB and remove renaming |
|
670
|
|
|
# write_synthetic_buildings_to_db(df_synthetic_buildings_without_amenities) |
|
671
|
|
|
write_table_to_postgis(df_synthetic_buildings_without_amenities.rename( |
|
672
|
|
|
columns={ |
|
673
|
|
|
"zensus_population_id": "cell_id", |
|
674
|
|
|
"egon_building_id": "id", |
|
675
|
|
|
}), |
|
676
|
|
|
OsmBuildingsSynthetic, |
|
677
|
|
|
drop=False) |
|
678
|
|
|
|
|
679
|
|
|
# Concat all buildings |
|
680
|
|
|
columns = ["zensus_population_id", "id", "geom_building", "n_amenities_inside"] |
|
681
|
|
|
# columns = ["zensus_population_id", "id", "geom_building", "n_amenities_inside", "table"] |
|
682
|
|
|
# df_buildings_with_amenities["table"] = "df_buildings_with_amenities" |
|
683
|
|
|
# df_synthetic_buildings_with_amenities["table"] = "df_synthetic_buildings_with_amenities" |
|
684
|
|
|
# df_buildings_without_amenities["table"] = "df_buildings_without_amenities" |
|
685
|
|
|
# df_synthetic_buildings_without_amenities["table"] = "df_synthetic_buildings_without_amenities" |
|
686
|
|
|
|
|
687
|
|
|
df_cts_buildings = pd.concat( |
|
688
|
|
|
[ |
|
689
|
|
|
df_buildings_with_amenities[columns], |
|
690
|
|
|
df_synthetic_buildings_with_amenities[columns], |
|
691
|
|
|
df_buildings_without_amenities[columns], |
|
692
|
|
|
df_synthetic_buildings_without_amenities[columns], |
|
693
|
|
|
], |
|
694
|
|
|
axis=0, |
|
695
|
|
|
ignore_index=True, |
|
696
|
|
|
) |
|
697
|
|
|
# TODO maybe remove after #772 |
|
698
|
|
|
df_cts_buildings["id"] = df_cts_buildings["id"].astype(int) |
|
699
|
|
|
|
|
700
|
|
|
df_demand_share_2035 = calc_building_demand_profile_share(df_cts_buildings, |
|
701
|
|
|
scenario="eGon2035") |
|
702
|
|
|
df_demand_share_100RE = calc_building_demand_profile_share(df_cts_buildings, |
|
703
|
|
|
scenario="eGon100RE") |
|
704
|
|
|
|
|
705
|
|
|
df_demand_share = pd.concat([df_demand_share_2035, df_demand_share_100RE], |
|
706
|
|
|
axis=0, ignore_index=True) |
|
707
|
|
|
|
|
708
|
|
|
# TODO Why are there nonunique ids? |
|
709
|
|
|
# needs to be removed as soon as 'id' is unique |
|
710
|
|
|
df_demand_share = df_demand_share.drop_duplicates(subset="id") |
|
711
|
|
|
|
|
712
|
|
|
write_table_to_postgres(df_demand_share, |
|
713
|
|
|
EgonCtsElectricityDemandBuildingShare, |
|
714
|
|
|
drop=True) |
|
715
|
|
|
|
|
716
|
|
|
return df_cts_buildings, df_demand_share |
|
717
|
|
|
|
|
718
|
|
|
|
|
719
|
|
|
def get_peak_load_cts_buildings(): |
|
720
|
|
|
|
|
721
|
|
|
# TODO Check units, maybe MwH? |
|
722
|
|
|
df_building_profiles = calc_building_profiles(scenario="eGon2035") |
|
723
|
|
|
df_peak_load_2035 = df_building_profiles.max(axis=0).rename( |
|
724
|
|
|
"cts_peak_load_in_w_2035") |
|
725
|
|
|
df_building_profiles = calc_building_profiles(scenario="eGon2035") |
|
726
|
|
|
df_peak_load_100RE = df_building_profiles.max(axis=0).rename( |
|
727
|
|
|
"cts_peak_load_in_w_100RE") |
|
728
|
|
|
df_peak_load = pd.concat([df_peak_load_2035, df_peak_load_100RE], |
|
729
|
|
|
axis=1).reset_index() |
|
730
|
|
|
|
|
731
|
|
|
CtsPeakLoads.__table__.drop(bind=engine, checkfirst=True) |
|
732
|
|
|
CtsPeakLoads.__table__.create(bind=engine, checkfirst=True) |
|
733
|
|
|
|
|
734
|
|
|
# Write peak loads into db |
|
735
|
|
|
with db.session_scope() as session: |
|
736
|
|
|
session.bulk_insert_mappings( |
|
737
|
|
|
CtsPeakLoads, |
|
738
|
|
|
df_peak_load.to_dict(orient="records"), |
|
739
|
|
|
) |
|
740
|
|
|
|
|
741
|
|
|
|
|
742
|
|
|
def delete_synthetic_cts_buildings(): |
|
743
|
|
|
# import db tables |
|
744
|
|
|
from saio.openstreetmap import osm_buildings_synthetic |
|
745
|
|
|
|
|
746
|
|
|
# cells mit amenities |
|
747
|
|
|
with db.session_scope() as session: |
|
748
|
|
|
session.query( |
|
749
|
|
|
osm_buildings_synthetic |
|
750
|
|
|
).filter( |
|
751
|
|
|
osm_buildings_synthetic.building == 'cts' |
|
752
|
|
|
).delete() |
|
753
|
|
|
|
|
754
|
|
|
|
|
755
|
|
|
class CtsElectricityBuildings(Dataset): |
|
756
|
|
|
def __init__(self, dependencies): |
|
757
|
|
|
super().__init__( |
|
758
|
|
|
name="CtsElectricityBuildings", |
|
759
|
|
|
version="0.0.0.", |
|
760
|
|
|
dependencies=dependencies, |
|
761
|
|
|
tasks=(cts_to_buildings, |
|
762
|
|
|
get_peak_load_cts_buildings, |
|
763
|
|
|
# get_all_cts_building_profiles, |
|
764
|
|
|
), |
|
765
|
|
|
) |
|
766
|
|
|
|
openego /
eGon-data
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