data.datasets.heat_demand_timeseries.create_individual_heating_peak_loads()

Complexity

Conditions

3

Size

Total Lines	108
Code Lines	33

Duplication

Lines	0
Ratio	0 %

Importance

Changes

0

from datetime import date, datetime
def create_individual_heating_peak_loads(scenario="eGon2035"):
    engine = db.engine()

    EgonIndividualHeatingPeakLoads.__table__.drop(bind=engine, checkfirst=True)

    EgonIndividualHeatingPeakLoads.__table__.create(
        bind=engine, checkfirst=True
    )

    start_time = datetime.now()

    idp_df = db.select_dataframe(
        """
        SELECT index, idp FROM demand.egon_heat_idp_pool
        """,
        index_col="index",
    )

    annual_demand = db.select_dataframe(
        f"""
        SELECT a.zensus_population_id, demand/c.count as per_building, bus_id
        FROM demand.egon_peta_heat a


        JOIN (SELECT COUNT(building_id), zensus_population_id
        FROM demand.egon_heat_timeseries_selected_profiles
        WHERE zensus_population_id IN(
        SELECT zensus_population_id FROM
        demand.egon_heat_timeseries_selected_profiles
        WHERE zensus_population_id IN (
        SELECT zensus_population_id FROM
        boundaries.egon_map_zensus_grid_districts
       ))
        GROUP BY zensus_population_id)c
        ON a.zensus_population_id = c.zensus_population_id

        JOIN boundaries.egon_map_zensus_grid_districts d
        ON a.zensus_population_id = d.zensus_population_id

        WHERE a.scenario = '{scenario}'
        AND a.sector = 'residential'
        AND a.zensus_population_id NOT IN (
            SELECT zensus_population_id
            FROM demand.egon_map_zensus_district_heating_areas
            WHERE scenario = '{scenario}'
        )

        """,
        index_col="zensus_population_id",
    )

    daily_demand_shares = db.select_dataframe(
        """
        SELECT climate_zone, day_of_year as day, daily_demand_share FROM
        demand.egon_daily_heat_demand_per_climate_zone
        """
    )

    start_time = datetime.now()
    for grid in annual_demand.bus_id.unique():
        selected_profiles = db.select_dataframe(
            f"""
            SELECT a.zensus_population_id, building_id, c.climate_zone,
            selected_idp, ordinality as day
            FROM demand.egon_heat_timeseries_selected_profiles a
            INNER JOIN boundaries.egon_map_zensus_climate_zones c
            ON a.zensus_population_id = c.zensus_population_id
            ,

            UNNEST (selected_idp_profiles) WITH ORDINALITY as selected_idp

            WHERE a.zensus_population_id NOT IN (
                SELECT zensus_population_id
                FROM demand.egon_map_zensus_district_heating_areas
                WHERE scenario = '{scenario}'
            )
            AND a.zensus_population_id IN (
                SELECT zensus_population_id
                FROM boundaries.egon_map_zensus_grid_districts
                WHERE bus_id = '{grid}'
            )

            """
        )

        df = pd.merge(
            selected_profiles, daily_demand_shares, on=["day", "climate_zone"]
        )

        slice_df = pd.merge(
            df, idp_df, left_on="selected_idp", right_on="index"
        )

        for hour in range(24):
            slice_df[hour] = (
                slice_df.idp.str[hour]
                .mul(slice_df.daily_demand_share)
                .mul(
                    annual_demand.loc[
                        slice_df.zensus_population_id.values, "per_building"
                    ].values
                )
            )

        calulate_peak_load(slice_df, scenario)

    print(f"Time to create peak loads per building for {scenario}")
    print(datetime.now() - start_time)